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WO2010025122A1 - Procédé permettant de mesurer la probabilité d’une maladie - Google Patents

Procédé permettant de mesurer la probabilité d’une maladie Download PDF

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Publication number
WO2010025122A1
WO2010025122A1 PCT/US2009/054852 US2009054852W WO2010025122A1 WO 2010025122 A1 WO2010025122 A1 WO 2010025122A1 US 2009054852 W US2009054852 W US 2009054852W WO 2010025122 A1 WO2010025122 A1 WO 2010025122A1
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tissue
imaging device
image
pixels
oral
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Darren Michael Roblyer
Rebecca Richards-Kortum
Ann Gillenwater
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William Marsh Rice University
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William Marsh Rice University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0082—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
    • A61B5/0088—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes for oral or dental tissue

Definitions

  • the present invention relates generally to imaging of biological tissue.
  • the present invention relates to methods of imaging biological tissue for measuring disease probability.
  • Head and neck cancer including cancers of the oral cavity, currently ranks as the sixth most common malignancy in the world. There were more than 270,000 new cases of oral cancer reported in 2002. Approximately 60% of these individuals present with stage III or IV disease, and about half will die within five years of diagnosis. Screening individuals at risk for oral cancer and its precursors has the potential to improve early detection, providing the opportunity to intervene when treatment is most effective. In addition, surveillance of patients who have survived their initial oral cancer is important to identify local recurrences and second primary oral tumors, which occur at a higher reported rate than for any other tumor.
  • the present disclosure is related to the application of digital image processing techniques to autofluorescence imaging of tissue to objectively identify and delineate the peripheral extent of neoplastic lesions.
  • this will provide a powerful tool in patient care locations where experts are not available or where physicians encounter few cases of malignant and pre-malignant neoplasia.
  • Low-cost digital cameras with sufficient sensitivity to record tissue autofluorescence in near real time are now readily available, making clinical application of such automated image processing now feasible.
  • the methods of the present disclosure can delineate the presence and extent of neoplastic lesions within a field of view and provide results which correlate with the histopathologic assessment of extent of disease.
  • quantitative autofluorescence imaging may provide a non-invasive and objective method to improve screening and margin delineation of oral cancers and precancers.
  • Figure 1 shows autofluorescence and white light images of the buccal mucosa of a typical study patient.
  • A White light image showing regions of interest of histopathologically confirmed normal tissue and invasive carcinoma.
  • B Fluorescence image at 365 nm excitation.
  • C Fluorescence image at 405 nm excitation.
  • D Fluorescence image at 450 nm excitation.
  • Figure 2 shows A. Scatter plot of normalized red-to-green ratios at 405 nm excitation for the 102 ROI sites in the training set. The horizontal line indicates the threshold used to obtain 95.9% sensitivity and 96.2% specificity. Note that 2 additional abnormal data points had a red- to-green fluorescence intensity ratio greater than 3 but are not shown on this plot.
  • B White light image showing regions of interest of histopathologically confirmed normal tissue and invasive carcinoma.
  • B Fluorescence image at 365 nm excitation.
  • C Fluorescence image at 405 nm excitation.
  • D Fluorescence image at
  • Receiver- operating characteristic (ROC) curve of the classifier based on the normalized red-to-green ratio.
  • the operating point used for classification is indicated by a dot and arrow.
  • C Scatter plot of the red-to-green ratio for the 57 sites in the validation set with threshold selected from the training set indicated. Note that 3 additional abnormal data points had a red-to-green fluorescence intensity ratio greater than 3 but are not shown on this plot.
  • D ROC curve obtained for the validation set. The operating point is indicated and corresponds to the threshold chosen from the training set.
  • Figure 4 A. and B. show images from a patient with an invasive carcinoma in the floor of mouth. A. White light image B. White light image with disease probability mapping showing the predictive probability of a neoplastic lesion. C. and D.
  • the present invention relates generally to imaging of biological tissue.
  • the present invention relates to methods of imaging biological tissue for measuring disease probability. While much of the description and examples herein pertains to the imaging of oral tissue in humans, such is not intended to limit the scope of the present invention. Rather, the methods disclosed herein are applicable to a variety of subjects and tissue types.
  • the present disclosure provides, in certain embodiments, a method comprising providing a tissue sample; providing a first imaging device comprising a white light imaging device; providing a second imaging device comprising at least one imaging device selected from the group consisting of: a fluorescent imaging device, a narrowband reflectance imaging device, and a polarized reflectance imaging device; obtaining a first image of the tissue sample with the first imaging device, wherein the first image comprises a plurality of pixels; obtaining a second image of the tissue sample with the second imaging device, wherein the second image comprises a plurality of pixels; calculating a metric for at least one of the plurality of pixels of the second image; and calculating a disease probability using the metric calculated for the at least one of the plurality of pixels of the second image.
  • tissue sample useful in the methods of the present invention may be any tissue sample suitable for imaging with the imaging devices useful in the methods of the present invention.
  • tissue samples may be human tissue.
  • human tissue may be from the oral cavity.
  • tissue may be abnormal tissue, such as precancerous or cancerous tissue.
  • tissue may be abnormal tissue, such as from an early invasive disease.
  • the tissue may be from a benign condition or from inflammation While the methods of the present invention, in certain embodiments, are capable of use without removal of the tissue from the subject, tissue samples removed from subjects, such as biopsies, may still be useful in the methods of the present invention, provided such removal does not render the tissue incapable of being imaged with the imaging devices useful in the methods of the present invention.
  • the first imaging devices useful in the methods of the present invention generally comprise a white light imaging device. Any suitable white light imaging device may be used in the methods of the present invention. In certain embodiments, such first imaging devices use commonly available broadband light sources, such as those in commercially available digital cameras. The choice of a suitable first imaging device may depend upon, among other things, the type and/or location of the tissue to be imaged.
  • the second imaging devices useful in the methods of the present invention include, but are not limited to, fluorescent imaging devices, narrowband reflectance imaging devices, and polarized reflectance imaging devices.
  • An example of a suitable second imaging device is a multispectral digital microscope (MDM).
  • the fluorescent imaging devices useful as second imaging devices in the methods of the present invention may be capable of producing fluorescent light with excitation wavelengths of one or more of 365 nm, 405 nm, and 450 nm.
  • the choice of a suitable second imaging device may depend upon, among other things, the type and/or location of the tissue to be imaged. When obtaining images with the first and second imaging devices, it is preferred to align each device such that the captured images from each device are of substantially the same portion of the tissue sample.
  • imaging processing techniques known to one of ordinary skill in the art may be used after the images are obtained to eliminate portions of the obtained images outside the portion of the tissue sample which is desired to be imaged.
  • image processing known to one of ordinary skill in the art may be used to account for any movement of the tissue sample during the imaging steps.
  • the resulting images comprise a plurality of pixels, and a variety of metrics may be calculated for at least one of the plurality of pixels.
  • the desired metric may be known or easily determined by one of ordinary skill in the art, with the benefit of the present disclosure.
  • Useful metrics may include, but are not limited to, the ratio of red pixel values to green pixel values, the ratio of red pixel values to blue pixel values, the ratio of green pixel values to blue pixel values.
  • Such a metric may be calculated for any number of pixels in the image, including a single pixel, a plurality of pixels which make up the image of a region of particular interest in the tissue sample, and all of the pixels in the image.
  • the metric(s) may be used to determine, among other things, a disease probability at the location of the tissue to which the individual pixel(s) correspond. Such a determination may be made by gathering a set of metrics from images of known abnormal (such as precancerous or cancerous) tissue and known normal tissue and comparing the values of the calculated metrics. A useful metric(s) may be found by demonstrating that such useful metric(s) are correlated with regions of abnormal tissue or regions of normal tissue. Such a metric may then be used in the methods of the present invention to determine the probability that individual portions of a tissue sample, represented by one or more pixels, and thus one or more values of the metric, are abnormal or normal. As with the metric calculations, such a determination may be made for any number of pixels in the image, including a single pixel, a plurality of pixels which make up the image of a region of particular interest in the tissue sample, and all of the pixels in the image.
  • the probability determination for one or more pixels of the image may be represented by a color-coded image.
  • Such an image may be overlaid with the first image, obtained from a white light source, to provide a color-coded image of the tissue sample, showing the probability of certain regions(s) of the tissue sample being normal or abnormal.
  • quantitative fluorescence imaging devices that can show false color disease-probability maps based on red/green fluorescence intensity ratios at 405 nm excitation at the time of the examination may be used.
  • Autofluorescence images were obtained from the oral cavity of 56 patients with clinically abnormal lesions and 11 normal volunteers. Data were divided into a training set and a validation set. Data acquired from the first 39 patients and 7 normal volunteers imaged between June 2006 and January 2008 were allocated to the training set and used to develop an algorithm for detection of neoplasia. Data acquired from the subsequent 17 patients and 4 normal volunteers imaged between March and June 2008 formed a validation set and used to test the performance of this algorithm relative to histopathology. The clinical protocol was reviewed and approved by the Internal Review Boards at the University of Texas MD Anderson Cancer Center and Rice
  • MDM Multispectral Digital Microscope
  • Biopsies and resected tissues were evaluated using standard histopathologic analysis by a board certified pathologist (AEN, MDW). The location of biopsies and resected lesions was recorded using digital photography so that pathology results could later be correlated to multispectral imaging results. In addition, the locations of gross anatomical features were noted in both autofluorescence images and histology specimens to aid in correlation. The resulting histopathology sections were evaluated to provide a diagnosis along the entire length of the epithelium, also noting any submucosal abnormalities in each slide. Histopathology diagnosis included the following categories: normal, mild dysplasia, moderate dysplasia, severe dysplasia/carcinoma in situ, and invasive carcinoma. For the purposes of diagnostic algorithm development, two major categories were defined: normal tissue (including inflammation and hyperplasia) and neoplastic tissue (including dysplasia, carcinoma in situ and cancer).
  • Images were preprocessed to subtract signal from ambient room light and translated so that white light and fluorescence images of the same field of view were spatially registered.
  • 276 measurements corresponding to 159 unique regions of interest (ROIs) sites of clinically normal and suspicious regions of tissue were selected from white light images by a head and neck surgeon (AMG) blinded to the results of the autofluorescence imaging.
  • ROIs regions of interest sites
  • AMG head and neck surgeon
  • repeat measurements were obtained from the same ROI site to help ensure image data was collected without motion artifacts; often both the first and repeat measurements were included in the analysis. These repeat measurements account for the difference between the number of measurements and the number of ROI sites.
  • Heterogeneity in pathologic diagnoses may occur within relatively small areas of diseased oral mucosa [25, 26], so ROIs were stringently selected from suspicious areas using one of following four criteria: 1) areas corresponding to the same size and location as a biopsy with a pathological diagnosis, 2) ROIs from locations which could be correlated to a histopathology slide with a corresponding pathological diagnosis, 3) areas within well-defined exophytic tumors confirmed by pathological diagnosis and 4) ROIs from a location which was clinically normal and deemed by the physician to be sufficiently distant from the lesion.
  • Autofluorescence images from the training set were analyzed to determine whether specific image features could be used to classify an ROI as normal or neoplastic.
  • the autofluorescence images and white light images were spatially registered so that the ROIs chosen in the white light images corresponded to the same region of tissue in the autofluorescence images.
  • the training set included data from the first 39 patients and 7 normal volunteers and included measurements from 173 ROIs.
  • neoplastic ROIs were associated with a decrease in average green fluorescence intensity and often an increase in red fluorescence intensity.
  • the mean ratio of red-to-green pixel intensities inside each of the ROIs was calculated from the fluorescence images at each excitation wavelength.
  • Red and green pixel intensities were obtained from the collected Red-Green-Blue color images, created by the Bayer color mask on the CCD detector.
  • a classifier was developed to distinguish neoplastic and normal ROIs using linear discriminant analysis with the single input feature of average ratio of red-to-green fluorescence. When more than one measurement corresponded to a ROI site, the mean of the feature values was used for classification. The classifier was trained using all of the ROI sites in training set and the prior probability input into the classifier was chosen to represent the percentage of abnormal to normal measurements in the data set. The classifier was developed after images were acquired from patients in the training set but before measurements were acquired from patients in the validation set.
  • Classifier accuracy in the training set was assessed by plotting the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and the sensitivity and specificity at a particular operating point on the ROC curve [27, 28]. The positive and negative predictive values were also calculated at the operating point. Confidence intervals were calculated for operating characteristics using the Wilson 'score' method including a continuity correction. The algorithm was then applied to data from the validation set using the red-to-green ratio threshold found to produce the highest combination of sensitivity and specificity in the training set. The validation set was designed to rigorously test the algorithm and for most patients, ROI and biopsy pairs were collected on the clinical margins of the lesion in addition to directly on the lesion and in clinically normal areas. The validation set included 103 measurements from 57 unique ROIs in a second group of 17 patients and 4 normal volunteers.
  • the method utilizing the magnitude of the red-to-green fluorescence intensity ratio is termed the raw red-to-green method and the method utilizing a normalized red-to-green fluorescence intensity ratio is termed normalized red-to-green method.
  • the classification algorithms described above provided a relationship between the magnitude of the red-to-green fluorescence intensity ratio for a particular region of interest within the image and die probability of that region having a diagnosis of abnormal. This relationship was used to predict the probability of a diagnosis of dysplasia or cancer for each pixel in an image, given the red-to- green fluorescence intensity ratio at that pixel.
  • the posterior probability values at each pixel in the image were computed and pixels which corresponded to a 50% or greater probability of being classified as dysplastic or cancerous were color coded and digitally overlaid onto the white light images.
  • This method provides a means to illustrate areas of tissue with the highest probability of being neoplastic. The assumption was made that the region of interest method described above could be generalized on a pixel by pixel basis. Disease probability maps were compared to histologic images of tissue resected from the field of view to confirm the accuracy of this method. Results
  • Tables 2 and 3 summarize the anatomic site and histopathologic diagnoses of the 159 sites included in this analysis. The most common sites were tongue, bucca mucosa and floor of mouth, followed by palate, lip, and gingiva.
  • the training set contained 52% normal, 28% dysplastic, and 20% invasive carcinoma sites.
  • the normal histopathologic category could include tissue with hyperkeratosis, hyperplasia, and/or inflammation as long as there was no dysplasia or carcinoma.
  • the normal sites in the training set included 7 sites (13.2% of normal sites) with hyperplasia and hyperkeratosis, 4 sites (7.5% of normal sites) with hyperkeratosis, and 3 sites (5.7% of normal sites) with hyperplasia and/or fibroadipose tissue.
  • the validation set included 3 sites (8.6% of normal sites) with hyperplasia and hyperkeratosis, 1 sites (2.9% of normal sites) with hyperplasia, 1 site (2.9% of normal sites) with a submucosal hemorrhage, and 1 site (2.9% of normal sites) with marked inflammation and osteonecrosis.
  • the abnormal histopathology category could include dysplasia and carcinoma. In the training set 59.2% of the abnormal sites were premalignant (mild, moderate, or severe dysplasia), in the validation set 68.2% of the abnormal sites were premalignant.
  • Figure 1 shows white light and autofluorescence images from the buccal mucosa of a patient with pathologically confirmed invasive carcinoma.
  • the white light image (Fig. IA) shows two ROIs, one which corresponds to a pathologically confirmed invasive carcinoma, and the other which was clinically normal and outside of the pathologically confirmed clear resection margin.
  • Figures IB- ID show autofluorescence images at different excitation wavelengths that were taken before surgery from the same field of view.
  • the autofluorescence image obtained at 405 nm excitation qualitatively shows the greatest visual contrast between the normal and neoplastic ROIs. This observation was typical for study patients.
  • Table 4 summarizes the performance of both diagnostic algorithms, based on either the raw or the normalized mean red to green fluorescence intensity ratios, for classifying lesions in the training set.
  • the classifier that used the normalized red-to- green fluorescence intensity ratio had slightly higher AUC than the algorithm based on the raw red/green fluorescence intensity ratio (Raw R/G ratio).
  • the highest AUC was obtained at 405 nm excitation.
  • the sensitivity and specificity values at the point on the ROC curve nearest the gold standard (Q-point) are also reported in Table 4.
  • a scatter plot of the normalized red-to-green ratio at 405 nm excitation for each of the 102 sites in the training set, as well as the threshold of 1.19 used in the classification algorithm is shown in Figure 2A.
  • 4 were misclassified including one site of fibroadipose tissue on the lower lip misclassified at abnormal, one hyperkeratotic site on the right buccal misclassified at abnormal, one cancer site on the right lateral tongue misclassified as normal, and one site on the left soft palate with focal ulceration and dysplasia misclassified as normal.
  • Figure 2B shows the ROC curve for this classifier; the AUC is 0.988, and at the Q-point, the sensitivity is 95.9% (95% confidence interval (CI) 84.9% - 99.3%) and the specificity is 96.2% (95% CI 85.9% - 99.3%). The positive predictive value is 95.9% (95% CI 84.9% - 99.3%) and the negative predictive value is 96.2% (95% CI 85.9% - 99.3%). This operating point is indicated on the ROC curve.
  • the positive predictive value is 88.0% (95% CI 67.7% - 96.9%) and the negative predictive value is 100% (95% CI 86.7% - 99.7%).
  • 3 were misclassified as abnormal, including one site on the left buccal with hyperplasia, one site on the right buccal, and another site on the left buccal.
  • Figure 3 shows white light and 405 nm excited autofluorescence images from a study patient with moderate dysplasia and carcinoma in situ located in the floor of mouth.
  • the white light image is also shown with an overlay of the calculated disease probability map; regions corresponding to a predictive probability of a neoplastic lesion greater than 50% are shaded as indicated by the color bar.
  • the disease probability map indicates the probability that a particular pixel in the image corresponds to a neoplastic area of tissue. Histologic sections obtained at several areas in the tissue are also shown. Only one of these areas was included in the previous classification analysis.
  • the disease probability map shows qualitative agreement with the presence of dysplasia and cancer in the histologic sections.
  • Figure 4 shows representative white light images with and without superimposed disease probability maps from four study patients.
  • Images in the first three rows correspond to patients with histologically confirmed neoplasia, while the image in the bottom row is from a normal volunteer with no clinically suspicious lesions. Although the lesion in Figure A is obvious, those in Figures B and C are less so, highlighting the potential to aid clinicians in identifying the presence of neoplasia and identifying optimal sites for further evaluation with biopsy.
  • Images in Figures 4A and B are from a patient with an invasive carcinoma in the floor of mouth.
  • Images in Figures 4C and D are from a patient with a region of severe dysplasia on the tongue.
  • the images in Figures 4E and F are from a patient with a region of moderate dysplasia on the gingiva.
  • the disease probability map delineates the suspicious regions identified clinically by an oral cancer specialist and are consistent with histopathologic sections obtained.
  • Figures 4G and H are from the inner lip of a normal volunteer and the disease probability map does not indicate any lesions. Discussion The results of this study, among other things, illustrate how autofluorescence imaging may enhance the ability of clinicians to detect and delineate areas of oral dysplasia and carcinoma. Although all four illumination conditions tested allowed visualization of changes in autofluorescence with neoplasia, illumination with 405 nm wavelength produced the highest discriminatory capability. This corresponds to previous findings comparing illumination wavelengths for autofluorescence imaging in freshly resected oral cancer surgical specimens.
  • Sensitivity ranged from 59% to 97%, specificity ranged from 75% to 99%, and meta-analysis resulted in a weighted pooled sensitivity of 85% and a specificity of 97%.
  • Other reports of the performance of visual oral screening include Sankaranarayanan et al (sensitivity 77%, specificity 76%), Ramadas et al (sensitivity 82%, specificity 85%), and Nagao et al (sensitivity 92%, specificity 64%).
  • the classifier in this study can be applied to entire images of the oral cavity to visualize areas with a high probability of being neoplastic; disease probability maps correlate well with histologic sections obtained from tissue in the field of view.
  • results demonstrate, among other things, quantitative fluorescence imaging as an objective approach to non-invasively identify and delineate the mucosal extent of neoplastic lesions in the, for example, the oral cavity.
  • IZ Roblyer D., et al., Multispectral optical imaging device for in vivo detection of oral neoplasia. J Biomed Opt. 13(2): p. 024019, (2008).

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  • Physics & Mathematics (AREA)
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Abstract

La présente invention a pour objet des procédés comprenant les étapes consistant à fournir un échantillon de tissu, à fournir un premier dispositif d’imagerie comprenant un dispositif d’imagerie à lumière blanche; à fournir un second dispositif d’imagerie comprenant au moins un dispositif d’imagerie choisi dans le groupe comprenant : un dispositif d’imagerie fluorescent, un dispositif d’imagerie à réflexion à bande étroite, et un dispositif d’imagerie à réflexion polarisée; à obtenir une première image de l’échantillon de tissu avec le premier dispositif d’imagerie, la première image comprenant une pluralité de pixels; à obtenir une seconde image de l’échantillon de tissu avec le second dispositif d’imagerie, la seconde image comprenant une pluralité de pixels; à calculer une métrique pour au moins un pixel de la pluralité de pixels de la seconde image; et à calculer une probabilité de maladie au moyen de la métrique calculée pour le ou les pixels de la pluralité de pixels de la seconde image.
PCT/US2009/054852 2008-08-25 2009-08-25 Procédé permettant de mesurer la probabilité d’une maladie Ceased WO2010025122A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013248396A (ja) * 2012-05-30 2013-12-12 Ormco Corp 口腔内撮像システム用のスペクトルフィルタ
WO2017178889A1 (fr) * 2016-04-13 2017-10-19 Inspektor Research Systems B.V. Examen dentaire bi-fréquence

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US5215095A (en) * 1990-08-10 1993-06-01 University Technologies International Optical imaging system for neurosurgery
US20030191368A1 (en) * 1998-01-26 2003-10-09 Massachusetts Institute Of Technology Fluorescence imaging endoscope
US20030232445A1 (en) * 2002-01-18 2003-12-18 Newton Laboratories, Inc. Spectroscopic diagnostic methods and system
US6790174B2 (en) * 1997-09-24 2004-09-14 Olympus Corporation Fluorescent imaging device

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US5215095A (en) * 1990-08-10 1993-06-01 University Technologies International Optical imaging system for neurosurgery
US6790174B2 (en) * 1997-09-24 2004-09-14 Olympus Corporation Fluorescent imaging device
US20030191368A1 (en) * 1998-01-26 2003-10-09 Massachusetts Institute Of Technology Fluorescence imaging endoscope
US20030232445A1 (en) * 2002-01-18 2003-12-18 Newton Laboratories, Inc. Spectroscopic diagnostic methods and system

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013248396A (ja) * 2012-05-30 2013-12-12 Ormco Corp 口腔内撮像システム用のスペクトルフィルタ
WO2017178889A1 (fr) * 2016-04-13 2017-10-19 Inspektor Research Systems B.V. Examen dentaire bi-fréquence
IL262401A (en) * 2016-04-13 2018-12-31 Inspektor Res Systems B V Dual-frequency dental examination
KR20190019052A (ko) * 2016-04-13 2019-02-26 인스펙터 리서치 시스템즈 비.브이. 이중 주파수 치아 검사
US10849506B2 (en) 2016-04-13 2020-12-01 Inspektor Research Systems B.V. Bi-frequency dental examination
KR102434556B1 (ko) 2016-04-13 2022-08-23 인스펙터 리서치 시스템즈 비.브이. 이중 주파수 치아 검사

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