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WO2018143369A1 - Méthode d'évaluation du cancer du pancréas chez un patient diabétique, méthode de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal - Google Patents

Méthode d'évaluation du cancer du pancréas chez un patient diabétique, méthode de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal Download PDF

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Publication number
WO2018143369A1
WO2018143369A1 PCT/JP2018/003477 JP2018003477W WO2018143369A1 WO 2018143369 A1 WO2018143369 A1 WO 2018143369A1 JP 2018003477 W JP2018003477 W JP 2018003477W WO 2018143369 A1 WO2018143369 A1 WO 2018143369A1
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Prior art keywords
evaluation
value
concentration
pancreatic cancer
control unit
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English (en)
Japanese (ja)
Inventor
浩通 伊佐山
卓 水野
實 山門
信矢 菊池
理浩 ▲高▼田
信和 小野
智行 田上
山本　浩史
今泉　明
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Ajinomoto Co Inc
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Ajinomoto Co Inc
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Priority to KR1020197022734A priority Critical patent/KR102475008B1/ko
Priority to JP2018566099A priority patent/JP7120027B2/ja
Publication of WO2018143369A1 publication Critical patent/WO2018143369A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806—Determination of free amino acids
    • G01N33/6812—Assays for specific amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids

Definitions

  • the present invention relates to a pancreatic cancer evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device for diabetic patients using amino acid concentrations in blood.
  • Pancreatic cancer is the fifth leading cause of cancer death in Japan and the fourth leading cause of cancer death in the United States. Pancreatic cancer has few symptoms depending on the site of cancer and is often discovered after it has progressed. Pancreatic cancer often has metastasis to adjacent tissues outside the pancreas even if it is detected at 2 cm or less using diagnostic imaging, and if it cannot be excised, the prognosis is very poor even if chemotherapy is performed.
  • the overall 5-year survival rate for pancreatic cancer is about 5%. On the other hand, for a small pancreatic cancer of 1 cm or less that can be operated, a 5-year survival rate of 57% can be expected. ing.
  • pancreatic cancer uses abdominal ultrasound echo, CT and MRI, but none of them has a high discovery rate of pancreatic cancer.
  • image diagnosis using endoscopes such as ERCP and EUS has become widespread, and it is known that the detection rate of pancreatic cancer is high and effective, but the physical burden on patients is high, bleeding due to examinations, etc. Risk can also occur.
  • tissue diagnosis by biopsy is a definitive diagnosis but a highly invasive test, and it is not practical to perform a biopsy test at the screening stage.
  • Serum cancer markers include CA19-9, CEA, SPAN-1, and DUPAN-2. These markers have relatively high sensitivity and specificity for advanced cancer, but have a low positive rate in early cancers and may be positive in cancers other than pancreatic cancer.
  • pancreatic cancer has a relatively low morbidity compared to other carcinomas, and pancreatic cancer has no established method for cancer screening for the general public.
  • pancreatic cancer has no established method for cancer screening for the general public.
  • pancreatic cancer examples include diabetes, obesity, smoking, family history of pancreatic cancer, chronic pancreatitis, pancreatic findings such as IPMN (intraductive capillary mucinous neoplasms) and cysts.
  • IPMN intraductive capillary mucinous neoplasms
  • cysts cysts.
  • pancreatic cancer As a problem of screening for diabetes, although the population affected by diabetes can be narrowed down to a certain level as compared to the general medical examination group, the number of subjects is still large, and further narrowing down is necessary. While increasing the detection rate of pancreatic cancer among newly diagnosed diabetic patients, it is known that long-term diabetes is also a risk factor for pancreatic cancer. It is assumed that the former can be divided into diabetes as a result of pancreatic cancer, and the latter as diabetes as the cause of pancreatic cancer. However, these high-risk groups cannot be narrowed clinically.
  • CA19-9 which is widely used as a marker for pancreatic cancer, is known to be falsely positive due to an increase in blood glucose, and is considered inappropriate for narrowing down high-risk groups from diabetic patients. Therefore, it is expected to devise a method for performing screening easily and widely.
  • Patent Document 1 discloses a discriminant for a pancreatic cancer disease state based on multivariate analysis in which a healthy subject sample and a pancreatic cancer patient sample are compared.
  • Non-Patent Document 2 reports that changes in the amino acid profile in blood have been observed from 2 to 5 years before diagnosis of pancreatic cancer based on the results of cohort studies.
  • Non-Patent Document 2 suggests that metabolic abnormalities of branched chain amino acids (BCAA) have occurred before pancreatic cancer became apparent by diagnostic imaging or the like.
  • BCAA branched chain amino acids
  • Non-Patent Document 3 it is known that a BCAA metabolic abnormality occurs based on a diabetic condition.
  • Patent Document 1 does not perform analysis specified for diabetic patients.
  • non-patent document 3 suggests an association between diabetes pathology and pancreatic cancer, but the specific technique for applying the transient amino acid change shown in the document to the diagnosis is not clearly described in the document. Also, in this document, a search for a combination of two or more biomolecules or an index formula using multivariate analysis is not performed.
  • the present invention has been made in view of the above, and an evaluation method, a calculation method, an evaluation apparatus, and the like, which can provide highly reliable information that can be helpful in knowing the state of pancreatic cancer in an evaluation subject having diabetes,
  • An object is to provide a calculation device, an evaluation program, a calculation program, an evaluation system, and a terminal device.
  • the present inventors have intensively studied in order to solve the above-mentioned problems, and have a high pancreatic cancer discriminating ability compared with the multivariate discriminant group described in Patent Document 1 by limiting the subject to diabetic patients.
  • a correlation equation (index formula) using amino acid variables was found, and the present invention was completed.
  • the evaluation method according to the present invention includes 19 kinds of amino acids (Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg) are used to evaluate the state of pancreatic cancer for the evaluation object. It is characterized by including.
  • an expression including a variable into which at least two concentration values of the 19 kinds of amino acids are substituted is used. By calculating the value, the state of pancreatic cancer is evaluated for the evaluation object.
  • the evaluation apparatus is an evaluation apparatus including a control unit, and the control unit uses at least two concentration values of the 19 kinds of amino acids in blood to be evaluated having diabetes.
  • the evaluation object is characterized by comprising evaluation means for evaluating the state of pancreatic cancer.
  • the evaluation method according to the present invention is an evaluation method executed in an information processing apparatus including a control unit, and is executed in the control unit, the 19 kinds of amino acids in blood to be evaluated having diabetes An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using at least two concentration values.
  • the evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus including a control unit, and the 19 types in the blood to be evaluated having diabetes for execution in the control unit An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using concentration values of at least two of the amino acids.
  • a recording medium is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.
  • an evaluation system includes an evaluation device including a control unit, and a control unit, and concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood of an evaluation object having diabetes.
  • An evaluation system configured such that a terminal device to be provided is communicably connected via a network, wherein the control unit of the terminal device transmits the concentration data to be evaluated to the evaluation device.
  • Data transmitting means, and result receiving means for receiving an evaluation result related to the state of pancreatic cancer in the evaluation target, transmitted from the evaluation apparatus, and the control unit of the evaluation apparatus is transmitted from the terminal apparatus.
  • the density data receiving means for receiving the density data of the evaluation target, and the density data of the evaluation target received by the density data receiving means
  • the evaluation means for evaluating the state of pancreatic cancer for the evaluation object using the concentration values of at least two of the 19 kinds of amino acids, and the evaluation result obtained by the evaluation means as the terminal
  • a result transmitting means for transmitting to the apparatus.
  • the terminal device is a terminal device including a control unit, and the control unit includes a result acquisition unit that acquires an evaluation result regarding a state of pancreatic cancer in an evaluation target having diabetes, and the evaluation The result is a result of evaluating the state of pancreatic cancer for the evaluation target using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation target.
  • the terminal device is configured to be communicably connected to an evaluation device that evaluates the state of pancreatic cancer for the evaluation target via the network in the terminal device, and the control unit includes The apparatus further comprises concentration data transmitting means for transmitting concentration data relating to the concentration value of at least two of the 19 kinds of amino acids in the blood to be evaluated to the evaluation apparatus, and the result acquisition means is transmitted from the evaluation apparatus. Receiving the evaluation result.
  • the evaluation device is connected to a terminal device that provides concentration data regarding at least two concentration values of the 19 kinds of amino acids in the blood to be evaluated having diabetes via a network.
  • the evaluation apparatus includes a control unit, wherein the control unit receives the density data of the evaluation target transmitted from the terminal device, and the density data receiving unit receives the density data receiving unit.
  • the state of pancreatic cancer is evaluated for the evaluation object using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation object. Therefore, pancreatic cancer in the evaluation object having diabetes It is possible to provide highly reliable information that can be used as a reference in knowing the state of the device.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • FIG. 3 is a diagram illustrating an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of this system.
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e.
  • FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d.
  • FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system.
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system.
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290).
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having
  • FIG. 15 is a diagram showing a list of combinations of three kinds of amino acids included in the new formula.
  • FIG. 16 is a diagram showing a list of combinations of four amino acids included in the new formula.
  • FIG. 17 is a diagram showing a list of combinations of five amino acids included in the new formula.
  • FIG. 18 is a diagram showing a list of combinations of six amino acids included in the new formula.
  • FIG. 19 is a diagram showing a list of combinations of two kinds of amino acids included in the new formula using combinations of three kinds of amino acids.
  • FIG. 16 is a diagram showing a list of combinations of four amino acids included in the new formula.
  • FIG. 17 is a diagram showing a list of combinations of five amino acids included in the new formula.
  • FIG. 18 is a diagram showing a list of combinations of six amino acids included in the new formula.
  • FIG. 19 is a diagram showing a list of combinations of two kinds of amino acids included in the new formula using combinations of three kinds of amino acids.
  • FIG. 20 is a list of combinations of two amino acids included in the new formula using a combination of four amino acids, a list of combinations of two amino acids included in the new formula using a combination of five amino acids, and 6 It is a figure which shows the list of the combination of 2 types of amino acids contained in the new type
  • FIG. 21 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of three amino acids and a list of combinations of three amino acids included in the new formula using combinations of four amino acids. It is.
  • FIG. 22 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of five amino acids and a list of combinations of three amino acids included in the new formula using combinations of six amino acids. It is.
  • Embodiment (1st Embodiment) of the evaluation method and calculation method concerning this invention and the evaluation apparatus, calculation device, evaluation method, calculation method, evaluation program, calculation program, evaluation system, and terminal concerning this invention
  • An apparatus embodiment (second embodiment) will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • concentration data relating to concentration values of at least two of the 19 kinds of amino acids in blood (for example, plasma, serum, etc.) collected from an evaluation subject (for example, an individual such as an animal or a human) having diabetes is acquired. (Step S11).
  • step S11 density data measured by a company or the like that performs density value measurement may be acquired.
  • concentration data may be acquired by measuring concentration values from blood collected from an evaluation object by, for example, the following measurement method (A), (B), or (C).
  • the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
  • A The collected blood sample is collected in a tube treated with EDTA-2Na, and plasma is separated from the blood by centrifuging the tube. All plasma samples are stored frozen at ⁇ 80 ° C. until the concentration value is measured.
  • sulfosalicylic acid is added and protein removal treatment is performed by adjusting the concentration to 3%, and then the concentration value is analyzed with an amino acid analyzer based on the principle of high-performance liquid chromatography (HPCL) using a ninhydrin reaction in a post column.
  • HPCL high-performance liquid chromatography
  • Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are stored frozen at ⁇ 80 ° C. until the concentration value is measured.
  • sulfosalicylic acid is added to remove the protein, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • the collected blood sample is subjected to blood cell separation using a membrane, MEMS technology, or the principle of centrifugation to separate plasma or serum from the blood.
  • Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at ⁇ 80 ° C. until the concentration is measured.
  • the concentration value is analyzed by quantifying a substance that increases or decreases by substrate recognition or a spectroscopic value using a molecule that reacts with or binds to a target blood substance such as an enzyme or an aptamer.
  • step S12 the state of pancreatic cancer is evaluated for the evaluation target using the concentration values of at least two of the 19 kinds of amino acids contained in the concentration data acquired in step S11 (step S12).
  • evaluating the state means, for example, examining the current state.
  • the concentration data of the evaluation target is acquired in step S11, and in step S12, the concentration data of the evaluation target acquired in step S11 is included in the 19 kinds of amino acids.
  • Use at least two concentration values to evaluate the status of pancreatic cancer for the evaluation target (in short, it can be useful for knowing information for evaluating the status of pancreatic cancer in the evaluation target or the status of pancreatic cancer in the evaluation target) Get reliable information). Accordingly, it is possible to provide information for evaluating the state of pancreatic cancer in an evaluation subject having diabetes or highly reliable information that can be used as a reference in knowing the state of pancreatic cancer in an evaluation subject having diabetes.
  • the concentration value of at least two of the 19 kinds of amino acids reflects the state of pancreatic cancer in the evaluation target
  • the concentration value is converted by, for example, the following methods
  • the converted value may be determined to reflect the state of pancreatic cancer in the evaluation target.
  • the concentration value or the converted value itself may be treated as an evaluation result regarding the state of pancreatic cancer in the evaluation target.
  • the possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 to
  • a predetermined range for example, exponential conversion, logarithmic conversion, Conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, power conversion, etc., and by combining these calculations for density values, the density values are converted. May be.
  • the value of an exponential function with the concentration value as an index and the Napier number as the base may exceed a predetermined state (for example, there is a possibility of suffering from pancreatic cancer)
  • a predetermined state for example, there is a possibility of suffering from pancreatic cancer
  • a value obtained by dividing the calculated exponential function value by the sum of 1 and the value may be further calculated.
  • the density value may be converted so that the value after conversion under a specific condition becomes a specific value.
  • the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0. Further, for each metabolite and each amino acid, the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10. These conversions may be performed by gender or age.
  • the density value in the present specification may be the density value itself or a value after the density value is converted.
  • position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is the concentration value of at least two of the 19 kinds of amino acids or the concentration
  • the predetermined ruler is for evaluating the state of pancreatic cancer.
  • the ruler is a ruler with a scale, and the “concentration value or a range that can be obtained after conversion, or the range.
  • a scale corresponding to the upper limit value and the lower limit value in “part of” is shown at least.
  • the predetermined mark corresponds to the density value or the value after conversion, and is, for example, a circle mark or a star mark.
  • the concentration value of at least two of the 19 kinds of amino acids is more than a predetermined value (average value ⁇ 1SD, 2SD, 3SD, N quantile, N percentile, or a cutoff value with clinical significance).
  • a concentration deviation value (a value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid and then making the deviation value so that the average is 50 and the standard deviation is 10) It may be used.
  • pancreatic cancer is evaluated for the evaluation target.
  • the state may be evaluated.
  • evaluation is performed.
  • a subject may be evaluated for pancreatic cancer status.
  • the calculated value of the expression reflects the state of pancreatic cancer in the evaluation target
  • the value of the expression is converted by, for example, the method described below, and the converted value is You may determine that it reflects the state of the pancreatic cancer in an evaluation object.
  • the value of the expression or the converted value itself may be handled as the evaluation result regarding the state of pancreatic cancer in the evaluation target.
  • the possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • a predetermined range for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • an arbitrary value is added / subtracted / divided / divided from / to the value of the expression, or the value of the expression is converted into a predetermined conversion method (for example, exponential conversion, Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by combining these calculations on the value of the expression,
  • the value of the expression may be converted.
  • the value of an exponential function with the value of the expression as the index and the Napier number as the base may be suffering from pancreatic cancer with a predetermined state (for example, exceeding a reference value) Is further calculated as the natural logarithm ln (p / (1-p)) is equal to the value of the equation) when the probability p is defined to be high)
  • a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, the value of probability p) may be further calculated.
  • the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value.
  • the value of the equation may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0. Further, the deviation value may be converted to an average of 50 and a standard deviation of 10. These conversions may be performed by gender or age. Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.
  • the predetermined ruler is for evaluating the state of pancreatic cancer, for example, a ruler with a scale, and “the range of the value of the formula or the value after conversion, or the That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of range” is shown.
  • the predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.
  • the degree of possibility that the evaluation target is suffering from pancreatic cancer may be qualitatively evaluated. Specifically, “at least two concentration values of the 19 amino acids and one or more preset threshold values” or “at least two concentration values of the 19 amino acids, the 19 types
  • the degree of possibility that the subject to be evaluated suffers from pancreatic cancer using an expression including a variable into which the concentration value of at least two of the amino acids is substituted and one or more preset threshold values ” May be classified into any one of a plurality of categories defined in consideration of at least.
  • categories for example, in the examples) for belonging to subjects that are highly likely to have pancreatic cancer (for example, subjects considered to be suffering from pancreatic cancer).
  • Rank C described classification for belonging to a subject having a low possibility of suffering from pancreatic cancer (for example, a subject regarded as not suffering from pancreatic cancer) (for example, described in the Examples)
  • a category for example, rank B described in the examples
  • the plurality of categories include a category for belonging to a subject having a high possibility of suffering from pancreatic cancer (for example, the pancreatic cancer category described in Examples), and the like.
  • Category for assigning a subject having a low possibility of being belonging for example, healthy category for assigning a subject having a high possibility of being healthy (for example, a subject considered to be healthy) described in the examples) ) May be included.
  • the density value or the expression value may be converted by a predetermined method, and the evaluation target may be classified into any one of a plurality of categories using the converted value.
  • the form used for the evaluation is not particularly limited, but for example, the following form may be used.
  • Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method
  • Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression
  • Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in
  • the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may create by the method.
  • the formula can be suitably used to evaluate the state of pancreatic cancer regardless of the unit of the amino acid concentration value in the concentration data as input data. .
  • a coefficient and a constant term are added to each variable.
  • the coefficient and the constant term are preferably real numbers, and more preferably May be any value belonging to the range of the 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably, the value obtained for performing the various classifications from the data. Any value may be used as long as it falls within the 95% confidence interval of the obtained coefficient and constant term.
  • the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.
  • Albumin total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST), GPT (ALT), GGTP ( ⁇ -GTP), glucose (blood glucose level), CRP (C-reactive protein), red blood cell, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell, platelet count, etc.
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • the description overlapping the first embodiment described above may be omitted.
  • the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example.
  • the concentration of at least two of the 19 kinds of amino acids is described.
  • a value or a value after the conversion (for example, a density deviation value) may be used.
  • the control unit includes the 19 types included in the concentration data acquired in advance regarding the concentration values of at least two of the 19 types of amino acids in the blood of an evaluation target (for example, an individual such as an animal or a human) having diabetes.
  • the value of the expression is calculated using the expression stored in advance in the storage unit including the concentration value to which at least two concentration values of the amino acids of the above and the concentration value of at least two of the 19 kinds of amino acids are substituted.
  • the state of pancreatic cancer is evaluated for the evaluation target (step S21).
  • step S21 may be created based on formula creation processing (step 1 to step 4) described below.
  • formula creation processing step 1 to step 4
  • an overview of the formula creation process will be described. Note that the processing described here is merely an example, and the method of creating an expression is not limited to this.
  • the control unit previously stores index state information (data having missing values, outliers, etc., previously stored in the storage unit, including concentration data and index data relating to an index representing the state of pancreatic cancer).
  • index state information data having missing values, outliers, etc.
  • concentration data data relating to an index representing the state of pancreatic cancer.
  • step 1 multiple different formula creation methods (principal component analysis and discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination from index state information
  • a plurality of candidate expressions may be created using a combination of multivariate analysis such as trees).
  • multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of diabetic groups not suffering from pancreatic cancer and a large number of pancreatic cancer groups suffering from diabetes
  • a plurality of groups of candidate formulas may be created simultaneously in parallel using a plurality of different algorithms.
  • discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas.
  • the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum expression for evaluation.
  • the candidate formula created using the principal component analysis is a linear formula including each variable that maximizes the variance of all density data.
  • Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there.
  • the candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups.
  • the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data.
  • the candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model.
  • the candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability.
  • k-means method k neighborhoods of each density data are searched, the largest group among the groups to which the neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as.
  • Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.
  • the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2).
  • Candidate expressions are verified for each candidate expression created in step 1.
  • the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate expression of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve).
  • the discrimination rate is an evaluation method according to the present embodiment, and an evaluation object whose true state is negative (for example, an evaluation object not suffering from pancreatic cancer) is correctly evaluated as negative, and the true state Is a rate at which an evaluation target (for example, an evaluation target suffering from pancreatic cancer) is correctly evaluated as positive.
  • Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment.
  • the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment.
  • the Akaike Information Criterion is a standard that expresses how closely the observed data matches the statistical model in the case of regression analysis, etc., and is expressed as “ ⁇ 2 ⁇ (maximum log likelihood of statistical model) + 2 ⁇ (statistics).
  • the model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best.
  • the value of 1 is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability.
  • the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas.
  • Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.
  • the control unit selects a combination of density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method.
  • the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately.
  • Step 1 is executed again using the index state information including the density data selected in Step 3.
  • the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2.
  • the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.
  • the control unit repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4).
  • the selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.
  • FIGS. 3 to 14 the configuration of an evaluation system according to the second embodiment (hereinafter may be referred to as the present system) will be described with reference to FIGS. 3 to 14.
  • This system is merely an example, and the present invention is not limited to this.
  • the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example.
  • the concentration of at least two of the 19 kinds of amino acids is described.
  • a value or a value after the conversion (for example, a density deviation value) may be used.
  • FIG. 3 is a diagram showing an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • the present system includes an evaluation apparatus 100 that evaluates the state of pancreatic cancer for an individual to be evaluated, and individual concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood.
  • a client device 200 (corresponding to a terminal device of the present invention) that provides communication via a network 300.
  • the client device 200 that is a provider of data used for evaluation and the client device 200 that is a provider of evaluation results may be different.
  • this system stores a database apparatus that stores index state information used when creating an expression in the evaluation apparatus 100, an expression used during evaluation, and the like in addition to the evaluation apparatus 100 and the client apparatus 200.
  • 400 may be configured to be communicably connected via the network 300.
  • the information that is useful for knowing the state of pancreatic cancer is, for example, information about values measured for specific items related to the state of pancreatic cancer in organisms including humans.
  • information that is useful for knowing the state of pancreatic cancer is generated by the evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measuring apparatuses) and is mainly stored in the database apparatus 400.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line.
  • the communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path.
  • the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus).
  • a small analysis having a configuration (hardware and software) that calculates (measures) concentration values of at least two of the 19 amino acids in blood and outputs the calculated values (printing, monitor display, etc.)
  • the apparatus may further include an evaluation unit 102d to be described later, and output a result obtained by the evaluation unit 102d using the above configuration.
  • the communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 108 is connected to the input device 112 and the output device 114.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114).
  • the input device 112 a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.
  • the storage unit 106 is a storage unit, and for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression related information database 106d, and an evaluation result file 106e.
  • the concentration data file 106a stores concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood.
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a.
  • the information stored in the density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data.
  • the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • values related to other biological information may be combined with the density data.
  • the index state information file 106b stores the index state information used when creating the formula.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • the information stored in the index state information file 106b includes an individual number and index data (T) related to an index (index T1, index T2, index T3,...) Indicating the state of pancreatic cancer.
  • the density data is associated with each other.
  • the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • the index data is a known index that serves as a marker of pancreatic cancer status, and numerical data may be used.
  • the designated index state information file 106c stores the index state information designated by the designation unit 102b described later.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.
  • the formula related information database 106d includes a formula file 106d1 that stores formulas created by a formula creation unit 102c described later.
  • the expression file 106d1 stores expressions used for evaluation.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes the rank, the expression (in FIG. 9, Fp (Phe,%), Fp (Gly, Leu, Phe), Fk (Gly, Leu, Phe,...)), A threshold value corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106d.
  • Information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the state of pancreatic cancer (for example, described later)
  • the value of the formula calculated by the calculation unit 102d1 the value after converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification obtained by the classification unit 102d4 described later Results
  • the like the like.
  • control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f.
  • the control unit 102 removes data with missing values, removes data with many outliers, and has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.
  • the receiving unit 102a may receive information (specifically, concentration data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300 or the like.
  • the receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 that is the transmission destination of the evaluation result.
  • the designating unit 102b designates index data and density data that are targets for creating an expression.
  • the formula creating unit 102c creates a formula based on the index state information received by the receiving unit 102a and the index state information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. The formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) that stores the formula in advance.
  • another computer device for example, the database device 400
  • the evaluation unit 102d is a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula received by the reception unit 102a), and concentration data of an individual having diabetes received by the reception unit 102a.
  • the state of pancreatic cancer is evaluated for an individual by calculating the value of the equation using the concentration values of at least two of the 19 kinds of amino acids included in the above.
  • the evaluation unit 102d may evaluate the state of pancreatic cancer for an individual using at least two concentration values of the 19 kinds of amino acids or a converted value of the concentration values (for example, concentration deviation value). Good.
  • FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion related to the present invention.
  • the evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.
  • the calculation unit 102d1 uses an expression including at least two concentration values of the 19 kinds of amino acids and a variable into which at least two concentration values of the 19 kinds of amino acids are substituted. Is calculated. Note that the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 using, for example, the conversion method described above.
  • the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 may convert at least two concentration values of the 19 kinds of amino acids included in the concentration data by, for example, the conversion method described above.
  • the generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion in (which may be a density value or a value after conversion of the density value).
  • the evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the classification unit 102d4 uses an expression value calculated by the calculation unit 102d1 or a value after conversion by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value) to cause an individual to suffer from pancreatic cancer. And classifying it into any one of a plurality of categories defined in consideration of at least the degree of the possibility of being performed.
  • the result output unit 102e outputs the processing result (including the evaluation result obtained by the evaluation unit 102d) in each processing unit of the control unit 102 to the output device 114.
  • the transmission unit 102f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is a transmission source of data used for evaluation.
  • FIG. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the client device 200 includes a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are connected via an arbitrary communication path. Are connected to communicate.
  • the client device 200 is an information processing device in which peripheral devices such as a printer, a monitor, and an image scanner are connected as necessary (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System) It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as PDA (Personal Digital Assistant), or the like.
  • the input device 250 is a keyboard, a mouse, a microphone, or the like.
  • a monitor 261 which will be described later, also realizes a pointing device function in cooperation with the mouse.
  • the output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like.
  • the input / output IF 270 is connected to the input device 250 and the output device 260.
  • the communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other.
  • the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line.
  • the client apparatus 200 can access the evaluation apparatus 100 according to a predetermined communication protocol.
  • the control unit 210 includes a reception unit 211 and a transmission unit 212.
  • the receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280.
  • the transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.
  • the control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit.
  • the ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU in cooperation with the OS and performing various processes.
  • the computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU.
  • the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary.
  • all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.
  • control unit 210 includes an evaluation unit 210a (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided.
  • evaluation unit 210a a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4 having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100.
  • the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2) ( A density value), or position information corresponding to an expression value or a converted value (which may be a density value or a value after conversion of the density value) is generated by the generation unit 210a3, or a classification unit 210a4
  • the individual may be classified into any one of a plurality of categories using the value of the expression or the value after conversion (which may be the density value or the value after conversion of the density value).
  • the network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other.
  • the Internet for example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like It is.
  • LAN Local Area Network
  • the network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), CATV ( Community Antenna Television (PD) network, mobile circuit switching network or mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) Mobile Communications-PDC (PDC)) System), wireless paging networks, and local wireless networks such as Bluetooth (registered trademark) , Or PHS network, satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (including Integrated Services Digital Broadcasting), etc.) may be like.
  • VAN Value-Added Network
  • a personal computer communication network including both analog / digital
  • a public telephone network including both analog / digital
  • a dedicated line network including both analog / digital
  • CATV Community Antenna Television (PD) network
  • IMT International Mobile Telecommunication 2000 system
  • GSM Registered Trademark
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.
  • the database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like.
  • the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line.
  • a communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414.
  • the output interface unit 408 is configured to be communicable via an arbitrary communication path.
  • the storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 406 stores various programs used for various processes.
  • the communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 408 is connected to the input device 412 and the output device 414.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 414.
  • the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.
  • the control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b.
  • the transmission unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100.
  • the receiving unit 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100.
  • the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result.
  • the client device 200 includes the evaluation unit 210a
  • conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 200.
  • the evaluation unit 210a converts the value of the expression in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3.
  • the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion. Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value. When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.
  • the evaluation device, the calculation device, the evaluation method, the calculation method, the evaluation program, the calculation program, the evaluation system, and the terminal device according to the present invention have the technical idea described in the claims in addition to the second embodiment described above. It may be implemented in a variety of different embodiments within the scope.
  • each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • all or some of the processing functions provided in the evaluation apparatus 100 may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic.
  • the program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.
  • this computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and the whole or a part of the computer program can be downloaded as necessary.
  • the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, and may be configured as a program product.
  • the “recording medium” refers to a memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electric Electric). Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Register, etc.) Any “possible It is intended to include physical medium "of use.
  • the “program” is a data processing method described in an arbitrary language or description method, and may be in the form of source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.
  • Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.
  • the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus connected to an arbitrary peripheral device.
  • the evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.
  • the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290).
  • the blood amino acid concentration data measured in this example was analyzed using the existing formula group to obtain the area under the ROC curve, and the maximum value was 0.873. Then, a new formula using a combination of two amino acids whose area under the ROC curve exceeds the maximum value was examined based on a combination of two amino acids corresponding to the top 100. As a result, a new formula using a combination of Ser and His having an area under the ROC curve of 0.877 was detected.
  • the scope of study was expanded to new formulas using combinations of amino acids, and for each of these new formulas, the number of combinations of two types of amino acids contained in the formula was counted.
  • Example 1 The sample data used in Example 1 was used. Multiple logistic regression was performed on all combinations from 3 amino acids to 6 amino acids extracted in Example 1. And the discrimination performance regarding 2 group discrimination
  • the number of combinations of three amino acids contained in the formula was counted.
  • the present invention can be widely implemented in many industrial fields, in particular, in fields such as pharmaceuticals, foods, and medical care, and in particular, prediction of progression of pancreatic cancer status and disease risk prediction in diabetic patients. It is extremely useful in the field of bioinformatics for proteome and metabolome analysis.

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  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biophysics (AREA)
  • Gastroenterology & Hepatology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Le problème abordé par la présente invention est de pourvoir à une méthode d'évaluation, etc., capable de fournir des informations extrêmement fiables qui sont potentiellement utiles pour découvrir un état de cancer du pancréas chez un sujet diabétique soumis à évaluation. Dans le présent mode de réalisation, un état de cancer du pancréas est évalué chez un sujet diabétique à l'aide de la valeur de concentration d'au moins deux acides aminés parmi Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys et Arg dans le sang du sujet soumis à évaluation.
PCT/JP2018/003477 2017-02-02 2018-02-01 Méthode d'évaluation du cancer du pancréas chez un patient diabétique, méthode de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal Ceased WO2018143369A1 (fr)

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KR1020197022734A KR102475008B1 (ko) 2017-02-02 2018-02-01 당뇨병 환자에서의 췌장암의 평가 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 평가 시스템 및 단말 장치
JP2018566099A JP7120027B2 (ja) 2017-02-02 2018-02-01 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、および評価システム

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