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WO2022201691A1 - Procédé de diagnostic par imagerie, appareil de diagnostic par imagerie, et programme de diagnostic par imagerie - Google Patents

Procédé de diagnostic par imagerie, appareil de diagnostic par imagerie, et programme de diagnostic par imagerie Download PDF

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Publication number
WO2022201691A1
WO2022201691A1 PCT/JP2021/047262 JP2021047262W WO2022201691A1 WO 2022201691 A1 WO2022201691 A1 WO 2022201691A1 JP 2021047262 W JP2021047262 W JP 2021047262W WO 2022201691 A1 WO2022201691 A1 WO 2022201691A1
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score
cells
local
cell
obtaining
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Japanese (ja)
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司 松尾
直也 斎藤
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Konica Minolta Inc
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Konica Minolta Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C12Q1/06—Quantitative determination
    • 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
    • G01N33/483—Physical analysis of biological material

Definitions

  • the present invention relates to an image diagnostic method, an image diagnostic apparatus, and an image diagnostic program.
  • HER2 human epidermal growth factor receptor-related 2
  • a specimen slide stained with HER2 protein is prepared.
  • the coloring intensity of each cell is classified into each coloring intensity category.
  • all processes are visually inspected by an inspector, and the summary score is determined by selecting one of four preset levels.
  • Patent Document 1 describes a method for testing HER2.
  • the test method described in Patent Document 1 includes the steps of staining cells in tissue, classifying the stained cells into a plurality of classes, and determining a cancer diagnostic score based on the proportion of the classified cells. have. Unlike the breast cancer examination method based on the HER2 examination guide, the examination method of Patent Document 1 is performed automatically.
  • test method described in Patent Document 1 includes a step of classifying the stained cells into a plurality of classes, so the test results may vary due to changes in the state of the specimen and the imaging conditions. As described above, in the conventional inspection methods, variations in inspection results may occur regardless of the inspection method.
  • An object of the present invention is to provide an image diagnostic method, an image diagnostic apparatus, and an image diagnostic program that are less likely to cause variations in examination results.
  • a diagnostic imaging method includes the steps of obtaining an image of tissue stained with biomarkers or cells, and obtaining or obtaining a local score that scores the characteristics of each cell in the obtained image. a step of dividing the obtained image into a plurality of sections and obtaining a local score obtained by scoring the characteristics of each section; selects the compartments that satisfy a predetermined condition; and scores characteristics of the selected cells or compartments to obtain a tissue summary score.
  • An image diagnostic apparatus includes an input unit for inputting an image of tissue stained with biomarkers or cells, an analysis unit for analyzing the image input by the input unit, and the analysis unit obtains a local score obtained by scoring the characteristics of each cell in the image, or partitions the image into a plurality of sections, obtains a local score obtained by scoring the characteristics of each partition, and select the cell where the local score of the cell satisfies a predetermined condition, or select the compartment where the local score of the compartment satisfies a predetermined condition, and score the properties of the selected cell or compartment to obtain a summary score for the organization.
  • the diagnostic imaging program comprises a computer, a step of obtaining an image of tissue stained with biomarkers or cells, and a local score obtained by scoring the characteristics of each cell in the obtained image.
  • the obtained image is divided into a plurality of sections, and a step of obtaining a local score obtained by scoring the characteristics of each section; The steps of selecting the compartments whose local scores meet predetermined conditions and scoring properties of the selected cells or compartments to obtain a tissue summary score are performed.
  • an image diagnostic method it is possible to provide an image diagnostic method, an image diagnostic apparatus, and an image diagnostic program that are less likely to cause variations in examination results.
  • FIG. 1 is a flow chart for explaining the diagnostic imaging method of this embodiment.
  • 2A to 2C are schematic diagrams for explaining an example of obtaining a local score obtained by scoring the characteristics of each cell.
  • 3A to 3C are schematic diagrams for explaining an example of obtaining a local score obtained by scoring the characteristics of each partition.
  • 4A to 4C are schematic diagrams for explaining how to set the partitions.
  • FIG. 5 is a schematic diagram for explaining the effect of this embodiment.
  • FIG. 6 is a block diagram of the diagnostic imaging apparatus according to this embodiment.
  • Figures 7A and B are examples analyzed using the present invention.
  • a diagnostic imaging method, diagnostic imaging apparatus, and diagnostic imaging program according to an embodiment of the present invention will be described below.
  • FIG. 1 is a flow chart for explaining an image diagnosis method according to one embodiment of the present invention.
  • 2A to 2C are schematic diagrams for explaining an example of obtaining a local score obtained by scoring the characteristics of each cell.
  • 3A to 3C are schematic diagrams for explaining an example of obtaining a local score obtained by scoring the characteristics of each partition.
  • 4A to 4C are schematic diagrams for explaining how to set the partitions.
  • the diagnostic imaging method includes the steps of obtaining an image (S110), obtaining a local score (S130), selecting (S140), and obtaining a summary score (S150). Moreover, the diagnostic imaging method may further include, after the step of obtaining the image and before the step of obtaining the local score, the step of excluding cells or compartments for which the local score is not calculated (S120). Furthermore, the diagnostic imaging method may have, after the step of obtaining the summary score, a step of converting the summary score into evaluation values of multiple stages (S160).
  • tissue stained with biomarkers or cells is obtained.
  • tissue includes cultured cells, and tissue collected for pathological diagnosis by needle biopsy or surgical biopsy.
  • a biomarker is appropriately selected depending on the test subject. Examples of biomarkers include proteins such as HER2, Programmed Death 1-Ligand 1 (PD-L1), and nucleic acids such as ribonucleic acid (RNA) and deoxyribonucleic acid (DNA).
  • a method for staining biomarkers or cells is not particularly limited as long as the biomarkers or cells can be stained.
  • Examples of staining methods include biomarker staining and staining for morphological observation.
  • biomarker staining include enzymatic antibody methods using diaminobenzidine (DAB), fluorescent dyes, and fluorescent antibody methods using quantum dots.
  • Examples of staining for morphological observation include staining with hematoxylin, eosin, and 4',6-diamidino-2-phenylindole (DAPI).
  • a digital image is, for example, an image of a stained and sampled tissue photographed with a microscope or a slide scanner.
  • biomarker staining and staining for morphological observation are stains that can be observed in bright field, after taking one image, separate each staining information by color vector information to obtain each image. may If the color vectors of each staining interfere and it is difficult to separate them, or if you want to observe multiple biomarkers, you can stain adjacent sections individually, take multiple images, and combine them to obtain a single image. good too.
  • a local score is obtained by scoring the properties of each cell in the obtained image, or the obtained image is divided into a plurality of sections and the properties of each section are scored.
  • One type of local score may be used, or two or more types may be used.
  • FIG. 2A When obtaining a local score that scores the characteristics of each cell, first extract the cells in the image. Next, a calculation region such as a cell membrane or a cell nucleus for calculating a local score in a cell is set (see FIG. 2A). Note that FIGS. 2A and 2B also show differences in the expression levels of membrane proteins. The expression levels of membrane proteins decrease in the order of thick line, solid line, and dotted line.
  • the calculated area may be set based on the result of staining, or may be set based on the result of calculation.
  • the method of setting based on the calculation result includes, for example, a method of setting a range within a certain distance from the outer edge of the cell nucleus as the calculation region (see FIG. 2B).
  • results of biomarker staining and morphological observation staining described above can be used as the staining information for obtaining the local score.
  • results of biomarker staining include coloring intensity (total value of coloring pixel values, average coloring pixel value, mode of coloring pixel values, median coloring pixel value, coloring intensity above a predetermined threshold) (including the number of pixels) (see FIG. 2C), variation in color development, bias in color development, and frequency distribution of color development intensity.
  • staining results for morphological observation include cell heterogeneity, cell circularity, cell size, color density, cell likelihood obtained by machine learning, and cell number in a compartment.
  • Information for obtaining a local score may be the coloring intensity per cell divided by the result of staining for morphological observation, or the result of biomarker staining may be the staining for morphological observation.
  • the intensity of color development that takes into consideration the results may also be used.
  • the coloring intensity considering the result of staining for morphological observation means, for example, the coloring intensity when the result of staining for morphological observation that satisfies a predetermined condition is excluded from the results of biomarker staining.
  • the obtained image is sectioned (see FIGS. 3A and 3B).
  • the size of the compartment, the shape of the compartment, and the like are not particularly limited.
  • the size of the compartment is the size that contains one or more cells.
  • Examples of compartment shapes include polygons, including squares and rectangles, and circles (see FIG. 4A).
  • Each section may be arranged with a predetermined interval, may be adjacent without a gap, or may partially overlap (see FIGS. 4B and 4C).
  • the setting method of division is not specifically limited, either.
  • the size, shape, and arrangement of the partitions set in advance may be uniformly set, or may be set as appropriate.
  • the number of sections is increased when the density of cells in the image is high, and the number of sections is decreased when the density of cells in the image is low.
  • the partition is then set as the computational region.
  • the coloring intensity of the set calculation area is integrated to obtain a local score obtained by scoring the characteristic of the section (see FIG. 3C).
  • the threshold value for selecting cells or compartments may be a predetermined number of cells or a predetermined number of compartments from the top of the local score by arranging the local score of each cell or the local score of each compartment in order, or the number of cells or compartments from the top of the local score. It may be a predetermined number of cells or a predetermined number of compartments from , or a cell or compartment whose local score is within a predetermined distance.
  • the population selection method for selecting cells or compartments is not particularly limited. For example, the local score of each cell or the local score of each compartment may be arranged in order, and all cells or all compartments may be selected as targets, or may be selected after sampling at arbitrary intervals.
  • tissue summary score In the step of obtaining a tissue summary score (S150), characteristics of selected cells or compartments are scored to obtain a tissue summary score.
  • the tissue summary score can be the maximum, minimum, mean, mode, or median of the local scores of the selected cells or the local scores of the selected compartment. can be a value.
  • the local scores are ordered and the top 10% value of the cells or compartments is the summary score.
  • the local score used in the selection process and the local score used in the process of obtaining the summary score may be the same or different. For example, if both the local score used in the selecting step and the local score used in the step of obtaining the summary score are color intensity, the summary score is calculated only for the cells or compartments whose color intensity satisfies a predetermined condition. do. Further, for example, when the local score used in the selecting step is the number of cells in the compartment and the local score used in the step of obtaining the summary score is the color density, the number of cells in the compartment is A summary score is calculated with only the color density of the plots that meet the conditions.
  • the step of excluding (S120) is optionally performed after the step of obtaining an image (S110) and before the step of obtaining a local score (S130).
  • the excluding step excludes cells or compartments for which no local score is to be calculated.
  • the excluding step may be performed manually or automatically.
  • an automatic exclusion step for example, a method of specifying only the tumor region from the morphological information of hematoxylin staining (H staining) and excluding other regions, a method of removing autofluorescence not derived from biomarkers, etc. included.
  • a manual exclusion step a method is included in which the operator identifies only the tumor region and excludes other regions.
  • regions to be excluded in the excluding step include regions in which no tissue is arranged in the obtained image, regions with poor staining, regions with poor imaging quality, glass regions in invasive cancer regions manually set by the operator, Sections that do not contain the predetermined number of cells are included.
  • the “glass region” means an outer region in which the tissue or section to be analyzed does not exist, or an observation region in which the tissue or section to be analyzed exists only sparsely. . In this case, in the selecting step described above, cells or compartments other than the cells or compartments excluded in the excluding step are selected.
  • the converting step (S160) is optionally performed after the step of obtaining the summary score (S150), and converts the summary score into a multi-level evaluation value.
  • the method of obtaining the reference value used for conversion is not particularly limited.
  • the reference value used for conversion may be a reference value prepared in advance, or may be a reference value obtained by experiment or the like.
  • the reference value can be obtained from the corresponding relationship between the effect of the drug administered for the purpose of acting on the patient's biomarkers and the summary score of the tissue collected from the patient.
  • the reference value can be obtained by, for example, summarizing score average + standard deviation x 3, based on measurement variation of summary scores output for specimen slides using biomarkers as negative controls.
  • biomarker positivity judgment value it is converted into multiple stages like the score in the HER2 test. Alternatively, classify positive and negative based on the reference value of the summary score. Alternatively, a correspondence table is created to indicate which range of summary scores the existing score values belong to. The transformed summary scores are used to determine patient treatment strategies. Alternatively, the results of existing scoring methods are output in parallel to assist the operator (pathologist) in making decisions.
  • scoring by the diagnostic imaging method according to the present embodiment is determined, for example, by the local score in the top 10% of the cumulative frequencies of cells (compartments). As shown in FIG. 5, in the scoring by the diagnostic imaging method according to the present embodiment, in the first analysis, the summary score at the 10% cumulative cell (compartment) frequency is 3.1. On the other hand, in the second analysis, the summary score at the 10% cell (compartment) cumulative frequency is 2.9. As described above, the scoring by the diagnostic imaging method according to the present embodiment is less likely to produce large variations in the analysis results than the analysis results by the scoring by the conventional HER2 examination guide.
  • FIG. 6 is a block diagram of the diagnostic imaging apparatus 100 according to this embodiment. As shown in FIG. 6 , the diagnostic imaging apparatus 100 according to this embodiment has an input unit 110 and an analysis unit 120 . This embodiment has a control unit 130 that controls the input unit 110 and the analysis unit 120 .
  • the input unit 110 is a device that obtains an image of tissue stained with biomarkers or cells.
  • the input unit 110 may obtain an image by taking an image, or may obtain an image by inputting an image acquired outside.
  • the input unit 110 may be an imaging device (camera) for obtaining images of tissues stained with biomarkers or cells, or may be a part of a computer for inputting images obtained externally.
  • the analysis unit 120 is a device for analyzing the obtained image. As described above, the analysis unit 120 obtains a local score that scores the properties of each cell in the obtained image, or divides the obtained image into a plurality of sections and scores the properties of each section.
  • the analysis unit 120 selects the cell whose local score satisfies a predetermined condition, or selects the partition whose local score satisfies a predetermined condition. Further, the analysis unit 120 scores the properties of the selected cells or compartments to obtain a tissue summary score, as described above.
  • the control unit 130 has a CPU (Central Processing Unit), a stored ROM (Read Only Memory), and a RAM (Random Access Memory).
  • the CPU reads an image diagnosis program corresponding to the processing contents from the ROM, develops it in the RAM, and centrally controls the operation of each block of the image diagnosis apparatus 100 in cooperation with the expanded program.
  • various data stored in the storage unit are referenced.
  • the storage unit is, for example, a nonvolatile semiconductor memory (so-called flash memory) or a hard disk drive.
  • the computer is provided with a step of obtaining an image of tissue stained with biomarkers or cells, obtaining a local score that scores the characteristics of each cell in the obtained image, or obtaining a plurality of obtained images and obtaining a local score obtained by scoring the characteristics of each compartment, and selecting a cell whose local score satisfies a predetermined condition, or selecting a compartment whose local score satisfies a predetermined condition and scoring characteristics of selected cells or compartments to obtain a tissue summary score.
  • the control unit 130 transmits and receives various data to and from an external device (for example, a personal computer) connected to a communication network such as LAN (Local Area Network) or WAN (Wide Area Network) via the communication unit. .
  • the control unit 130 receives image data transmitted from an external device, for example, and performs image diagnosis based on this image data (input image data).
  • the communication unit is, for example, a communication control card such as a LAN card.
  • FIG. 5 is a diagram for explaining the effect of the diagnostic imaging method according to this embodiment.
  • the horizontal axis of FIG. 5 indicates the cumulative frequency (%) of cells or compartments, and the vertical axis indicates the local score.
  • the solid line in FIG. 5 indicates the results of the first analysis, and the dotted line indicates the results of the second analysis.
  • the slice used for the first analysis and the slice used for the second analysis are adjacent slices.
  • Scoring by the conventional HER2 test guide is determined by the percentage of cumulative frequencies of cells (compartments) with a local score of 3+. As shown in FIG. 5, in the conventional HER2 test-guided scoring, the summary score is 3+ because there are 10% or more cells (compartments) with a local score of 3+ in the first analysis. On the other hand, in the second analysis, less than 10% of the cells (compartments) have a local score of 3+, resulting in a summary score of 2+. As described above, in the scoring by the conventional HER2 examination guide, even if the tissues in substantially the same state are analyzed, the analysis results may vary greatly.
  • FIG. 7A and B are examples analyzed using the present invention.
  • the horizontal axis of FIG. 7A is the HER2 score per core as determined by a pathologist using DAB-stained serial section slides.
  • the vertical axis in FIG. 7A is the evaluation value for each core analyzed using the present invention.
  • FIG. 7B is a concordance table obtained by calculating a provisional PID score with the minimum score of evaluation values analyzed using the present invention as a cut point for the DAB score.
  • a microarray slide of human breast cancer tissue was used as the specimen.
  • Ductal carcinoma in situ, DAB undeterminable cores, cores with stained cytoplasm, invasive ductal carcinoma undeterminable cores, non-cancerous cores, and detached cores were analyzed in advance by a pathologist out of a total of 208 cores. Excluded as unsuitable core.
  • Evaluation values were calculated by the following method. First, slides were immunostained with primary and biotinylated secondary antibodies and labeled with streptavidin-coated fluorescent nanoparticles. PID particle size was about 130 nm (excitation: 580 nm, fluorescence: 620 nm). Then, it was converted into a highly accurate Whole Slide Image (WSI) by a virtual slide scanner. Next, the obtained WSI was image-divided for each core. Next, for each segmented core image, only the infiltration region annotated by the pathologist was set as the analysis target, and the other masked region was set as the analysis target region.
  • WSI Whole Slide Image
  • the evaluation value for each core was calculated by the following procedure. (1) The fluorescence image was divided into 12 ⁇ m square sections, and among all the sections, only the sections within the analysis target area were targeted, and the fluorescence intensity integrated value within the section was calculated for each section. (2) Among the values for each section calculated in (1), the average value of the sections with the top 10% values is calculated. This was used as the evaluation value for each core.
  • the present invention for example, it is useful for diagnosing cancer such as breast cancer, and diagnosing various other diseases.
  • diagnostic imaging apparatus 110 input unit 120 analysis unit 130 control unit

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Abstract

Procédé de diagnostic par l'image comprenant les étapes suivantes : obtention d'une image d'un biomarqueur ou d'un tissu où les cellules sont colorées; acquisition de scores locaux obtenus en évaluant les caractéristiques des cellules dans l'image obtenue, ou acquisition de scores locaux obtenus en divisant l'image obtenue en une pluralité de divisions et en évaluant les caractéristiques des divisions; sélection de cellules ayant un score local satisfaisant une condition prédéterminée, ou sélection de divisions ayant un score local satisfaisant une condition prédéterminée; et obtention d'un score résumé pour le tissu en évaluant les caractéristiques des cellules ou divisions sélectionnées.
PCT/JP2021/047262 2021-03-23 2021-12-21 Procédé de diagnostic par imagerie, appareil de diagnostic par imagerie, et programme de diagnostic par imagerie Ceased WO2022201691A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011179924A (ja) * 2010-02-26 2011-09-15 Olympus Corp 顕微鏡システム、標本観察方法およびプログラム
JP2016001179A (ja) * 2005-05-13 2016-01-07 トリパス イメージング インコーポレイテッド 色原体分離に基づく画像解析の方法
JP2020201271A (ja) * 2015-10-23 2020-12-17 ノバルティス・エイジーＮｏｖａｒｔｉｓ ＡＧ 腫瘍組織を含むサンプルから細胞間の空間的近接を表すスコアを決定する方法
JP2021006037A (ja) * 2014-09-03 2021-01-21 ヴェンタナ メディカル システムズ， インク． 免疫スコアを計算するためのシステム及び方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016001179A (ja) * 2005-05-13 2016-01-07 トリパス イメージング インコーポレイテッド 色原体分離に基づく画像解析の方法
JP2011179924A (ja) * 2010-02-26 2011-09-15 Olympus Corp 顕微鏡システム、標本観察方法およびプログラム
JP2021006037A (ja) * 2014-09-03 2021-01-21 ヴェンタナ メディカル システムズ， インク． 免疫スコアを計算するためのシステム及び方法
JP2020201271A (ja) * 2015-10-23 2020-12-17 ノバルティス・エイジーＮｏｖａｒｔｉｓ ＡＧ 腫瘍組織を含むサンプルから細胞間の空間的近接を表すスコアを決定する方法

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