WO2019009893A1 - Procédés de mesure et de rapport de vascularité dans un échantillon de tissu - Google Patents

Procédés de mesure et de rapport de vascularité dans un échantillon de tissu Download PDF

Info

Publication number
WO2019009893A1
WO2019009893A1 PCT/US2017/040777 US2017040777W WO2019009893A1 WO 2019009893 A1 WO2019009893 A1 WO 2019009893A1 US 2017040777 W US2017040777 W US 2017040777W WO 2019009893 A1 WO2019009893 A1 WO 2019009893A1
Authority
WO
WIPO (PCT)
Prior art keywords
vessel
tissue
cells
proximity
score
Prior art date
Application number
PCT/US2017/040777
Other languages
English (en)
Inventor
Steven J. Potts
Original Assignee
Flagship Biosciences Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Flagship Biosciences Inc. filed Critical Flagship Biosciences Inc.
Priority to PCT/US2017/040777 priority Critical patent/WO2019009893A1/fr
Publication of WO2019009893A1 publication Critical patent/WO2019009893A1/fr

Links

Classifications

    • 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
    • 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
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6841—In situ hybridisation
    • 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
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • 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
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/30—Staining; Impregnating ; Fixation; Dehydration; Multistep processes for preparing samples of tissue, cell or nucleic acid material and the like for analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7014—(Neo)vascularisation - Angiogenesis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7038—Hypoxia

Definitions

  • This invention relates to methods for measuring and reporting vascularity in a tissue sample; and more particularly, to methods for evaluation of angiogenesis and hypoxia using digital image analysis platforms.
  • the ability to evaluate the vascular structure of tissue is important in many therapeutic areas, both in areas that seek to increase the growth of blood vessels (pro- angiogenic) and areas that seek to decrease or shrink blood vessels (anti-angiogenic). It is important to be able to evaluate the architecture of the blood vasculature, as well as to determine how adequately blood supply, nutrients, and oxygen are being made available to local tissue, and how adequately waste products are being removed.
  • Angiogenesis is a biological process of generating new blood vessels from pre-existing blood vessels into a tissue or organ. Under normal physiology, angiogenesis is tightly regulated by many angiogenic factors, and switching of the phenotype depends on a net balance between up-regulation of angiogenic stimulators and down-regulation of angiogenic suppressors. Therapeutic areas where interventions serve to modulate angiogenesis include: atherogenesis, arthritis, psoriasis, oncology, corneal neovascularization, and diabetic retinopathy.
  • Evaluation of angiogenesis therapy requires measuring the changes on the tissue vasculature. This is made difficult in that it must either be studied: (i) non-invasively, using radiologic or other non-destructive imaging modalities; (ii) evaluated with histopathology using tissue sections; or (iii) with lab assays with two-dimensional and three- dimensional cell cultures. Histopathology provides the highest resolution evaluation of actual tissue architecture, but quantitation requires measurements on a thin section of a three- dimensional blood vessel network.
  • MMD micro-vessel density
  • Weidner developed a micro-vessel density (MVD) approach in 1991, as described in Weidner, N., et al., "Tumor angiogenesis and metastasis— correlation in invasive breast carcinoma " , New England Journal of Medicine, 1991. 324(1): p. 1-8.
  • the tissue was surveyed with a 4x objective, and the areas with the most vascularization, or "hotspots" were identified. In these areas, the field of view with the highest vascularization was then counted for vessels with a 20x or 40x objective. Then the second highest field of view is counted, and up to ten fields of view are tabulated in this fashion.
  • these "hotspot" areas are chosen by the observer and then micro-vessel density is computed. While studies have ranged from 3 to 5 fields of view or more, most studies utilize the average of the three most vascularized fields of view when reporting results.
  • the counting method itself can be made slightly more objective after the hotspot regions of interest are selected by evaluating the fields of view by using a Chalkey grid eyepiece as suggested in Chalkley, H., "Method for the quantitative morphologic analysis of tissues ", Journal of the National Cancer Institute, 1943. 4(1): p.
  • Van der Laak, J., et al. "An improved procedure to quantify tumor vascularity using true color image analysis: comparison with the manual hot-spot procedure in a human melanoma xenograft model ", J. Pathol, 1998. 184: p. 136-143, describes a semi-automated technique which acquired all fields of view from a tissue section, and then identified hotspots based on the higher areas of positive endothelial staining.
  • TVA image analysis based total microvascular area
  • MVD counts see Sharma, S., M. Sharma, and C. Sarkar, "Morphology of angiogenesis in human cancer: a conceptual overview, histoprognostic perspective and significance of neoangiogenesis ", Histopathology, 2005. 46(5): p. 481-489.
  • Total microvascular area predates digital pathology, and TVA, MVD, and Chalkey counts all use the same approach with selecting hotspots and several fields of view.
  • Vessel density may actually exceed metabolic requirements in tumors, and the result is uniform over vascularization; Hlatky, L., P. Hahnfeldt, and J. Folkman, "Clinical application of antiangiogenic therapy: microvessel density, what it does and doesn't tell us “, J Natl Cancer Inst, 2002. 94(12): p. 883-93.
  • the main endpoint used in MVD has been the number of vessels per square millimeter of tissue section. There are both theoretical and experimental problems with this endpoint.
  • microvessel density from a stereological viewpoint, recognizing that a two-dimensional tissue section is only one sample from the three dimensional tumor, a number of theoretical problems present themselves. Anything observed on a section should be considered a profile, rather than the actual object. Recording the number of vessel profiles per area is not a measurement with roots in reality. Thicker or thinner sections, under or over staining, higher or lower cellularity in the sample, will all effect this endpoint.
  • the best that the statistic can be used for is to compare the effect of one treatment with another, or before and after treatments, rather than as an absolute physical observation.
  • the difficulty with vessel densities is the ability to adequately number vessels with image analysis.
  • tumors with limited vascularity consisting of only small microcapillaries, it may be possibly, but as vascularity increases, it becomes difficult for the pathologist (and especially the computer) to determine which vessel profiles should be part of only one vessel.
  • pathologist and especially the computer
  • Many researchers resort to an area measurement to overcome this problem, the area of vessel profiles / area of tissue.
  • a method including : (i) acquiring at least one digital image of a stained tissue section, wherein the stained tissue section is stained in such a manner to allow identification of at least one vessel object and at least one tissue object; (ii) detecting within the at least one digital image at least one vessel object, wherein the vessel object is selected from the group consisting of fully formed vessels and vessel fragments; (iii) detecting at least one cell within the digital image; and (iv) calculating a vessel proximity score based on the detected vessel object and cell.
  • the central question is what percentage of the cells of interest are near a vessel, rather than the number of vessels themselves.
  • the degree that a given cell or tissue is oxygenated or under hypoxic conditions will be driven not by the number of vessels but by the distance the tissue or cell is to the nearest vessel.
  • endothelial cells or vessels are identified with one of many existing approaches using image analysis; for example, immunohistochemistry stains, or immunofluorescence dyes, which are conventionally used to identify endothelial cells. Then, a perimeter is drawn computationally, for example a radial distance from these endothelial cells which form the vessels. The distance can be selected or input by a user. The percentage of target tissue that is within or outside of this distance is determined and reported. Either a single distance or multiple distances can be utilized.
  • cells of interest are identified and the percentages of these cells that are near vessels are determined.
  • multiple myeloma a dual immunohistochemistry method can be used, where one antibody with a colorimetric label is used to identify vasculature and the second antibody is used to identify myeloma cells. The percentage of myeloma cells within a given distance of vessels is then calculated.
  • FIG. l shows a histology slide image used to analyze vascularity, the slide contains myeloma cells stained for colorimetric identification, and endothelial cells distinctly stained for colorimetric identification; a proximity distance perimeter is created at a distance from the endothelial cells for determining a composition of cells near a vascular object.
  • FIG.2 shows a histology slide image used to analyze vascularity, wherein vessel portions not oriented orthogonal to the plane of sectioning of the imaged tissue section are omitted from quantitative analysis.
  • proximity analysis refers to the determination of an amount of a first cellular entity that is within a given distance of a second distinct cellular entity
  • microvessel proximity analysis refers to proximity analysis where one of the cellular entities comprises vascular cells
  • well-formed vessel refers to a small artery, vein, or capillary that is easily viewed as a vessel on a histology section, these are frequently ones that have been cut orthogonally to the tissue section and will have well-formed lumen and typical "donut-like" annular morphology;
  • vessel fragment is used to refer to any of: (i) single endothelial cells that have not yet become vessels, (ii) microcapillaries of just a few endothelial cells, or (iii) a larger vessel that has been cut in such a way in histology that only a few cells and/or poorly formed morphology is present;
  • random sampling refers to a number of existing techniques for identifying regions of interest on a tissue section on which to make further measurements
  • whole section analysis refers to utilizing the entire tissue section in order to make measurements, as opposed to select portions of the section.
  • the disclosed methods are performed using a computer with an electronic display.
  • the computer can be any computer system that is programmed or otherwise configured to view and annotate digital images of tissue slides.
  • an amount of first antibodies that identify endothelial cells are used to identify the endothelial cells within a tissue section.
  • a second antibody stain or dye is used to identify other cells types of interest, or "target cells", within the tissue section.
  • Image analysis is used to identify these endothelial cells, and optionally label them as either well-formed vessels or vessel fragments within an image of the tissue section.
  • a proximity distance from these objects is computationally formed on the slide-image.
  • the distance can be presented as an image analysis mark-up, such as a series of contour lines surrounding the vessels at a distance therefrom.
  • the proximity distance can be either entered or selected by the user, or calculated a number of other ways.
  • the proximity distance can be calculated as the average distance between vessels, and we can assume that half this distance is the leading edge of hypoxic conditions. The assumption is that vessels form to provide oxygenation to vessel in response to various cytokine and other factors, and thus are arranged so as to ensure adequate oxygenation.
  • a number of statistics can be used to measure vessel proximity. These include:
  • FIG. 1 Another aspect of vascularity analysis includes the evaluation of changes in the vessels.
  • a given angiogenesis treatment could either repair or disrupt the formation of normal vasculature.
  • Vessel morphology is difficult to measure because only some vessels in any given tissue section are cut orthogonal to the section and thus are displayed in a way where vessel morphology can be calculated (e.g. cell wall thickness, diameter, vessel area, among others). It is suggested to assume that in any given tissue roughly the same number of vessels would be randomly sliced perpendicular to the section. Thus using only the well-formed vessels is an adequate sample for overall statistics for the population. This does assume that the vessel network is isotropic, that the blood vessels are not arranged in any consistent geometric pattern that would bias one randomly chosen direction from another.
  • Heterogeneity of vascularity can also be important.
  • Vascularity can be measured by randomly sampling one or more tissue sections, and calculating heterogeneity on these measurements.
  • the heterogeneity measurement may include ecology indices, or any form of simple or complex statistics that describe population variability (e.g. standard deviation, skewness, among others).
  • a method for measuring and reporting vascularity in a tissue sample includes the vascular proximity analysis of myeloma cells as shown in FIG. 1. As shown, myeloma cells are stained in a first color and endothelial cells are stained in a second color. A proximity distance is input into a computerized platform and resulting contour lines are formed about the slide-image. The percentage of myeloma cells within the proximity distance of vessels or vessel fragments is then computed.
  • FIG.2 shows a slide image wherein endothelial cells are stained, and a computer is used to identify those endothelial tissues which form an annular shape. Any regions of endothelial tissue that fail to form an annular shape are excluded from the analysis.
  • a method for measuring and reporting vascularity in a biological tissue sample includes: (i) obtaining one or more tissue sections from the tissue sample; (ii) staining each of the tissue sections with: a first stain or dye for visually differentiating endothelial cells, lymphatic cells, or a combination thereof within the tissue section, and at least a second stain or dye for visually differentiating target cells, the target cells not including endothelial cells or lymphatic cells; (iii) acquiring at least a first digital image, the first digital image capturing at least a portion of a first tissue section of the stained tissue sections; and (iv) with the first digital image of the first tissue section or portion thereof: detecting at least one of: fully formed vessels and vessel fragments using visual characteristics associated with the first stain or dye; mapping about the first digital image one or more first proximity regions, each of the first proximity regions comprising an area between the detected vessels and a first distance outwardly therefrom; detecting the target cells using visual characteristics associated with
  • the method further comprises: with the first digital image of the first tissue section or portion thereof: annotating one or more regions of interest within the first digital image; calculating a vascular heterogeneity score comprising one or more of: a standard deviation, ecology indices, skewness, or a combination thereof ; and recording the vascular heterogeneity score.
  • the method further comprises: of the fully formed vessels and vessel fragments detected within the first digital image: digitally omitting any vessel fragments that fail to represent annular structure from the vessel proximity score as being non-orthogonal to a plane of the respective tissue section.
  • the method may further comprise: with the first digital image of the first tissue section or portion thereof: calculating at least one of: vessel area, vessel wall thickness, diameter, lumen area, or a combination thereof; and recording with the vessel proximity score.
  • the method may further comprise: using the vessel proximity score and recorded data associated with the first digital image and that of a second digital image, the first digital image representing a first tissue section associated with treatment of a first angiogenesis-related therapy, the second digital image representing a second tissue section associated with treatment of a second angiogenesis-related therapy: (i) comparing the efficacy of the first angiogenesis-related therapy with another; (ii) determining whether a patient would benefit from one of the first and second therapy; or (iii) determining whether a patient is likely to have toxicity effects related to one of the first and second angiogenesis-related therapy.
  • each of the target cells, endothelial cells, and lymphatic cells are visually isolated using one of: immunohistochemistry (IHC), immunofluorescence (IF), DNA in situ hybridization (DNA-ISH), RNA in situ hybridization (RNA-ISH), or a combination thereof.
  • IHC immunohistochemistry
  • IF immunofluorescence
  • DNA-ISH DNA in situ hybridization
  • RNA-ISH RNA in situ hybridization
  • the vessel proximity score comprises a percentage of the detected target cells that are disposed within the first proximity regions.
  • the vessel proximity score comprises a tissue hypoxia score, wherein the tissue hypoxia score comprises a percentage of tissue disposed outside of the first proximity regions.
  • the vessel proximity score comprises a tissue hypoxia score, wherein the tissue hypoxia score comprises a percentage of tissue disposed within the first proximity regions.
  • the tissue sections can comprise one or more tissue microarray samples.
  • the vessels the vessels can be detected using DNA or
  • RNA in situ hybridization for differentiating endothelial cells.
  • the target cells can comprise first target cells and second target cells, the first target cells being distinct from the second target cells, and the first and second target cells not including endothelial cells; and the at least a second stain or dye for differentiating the target cells comprises: the second stain or dye for differentiating the first target cells, and a third stain or dye for differentiating the second target cells.
  • the method may further include: calculating a first vessel proximity score for the first target cells; calculating a second vessel proximity score for the second target cells; and recording the first and second vessel proximity scores.
  • the target cells comprise one of: bone or fat cells; and the second stain or dye for differentiating the target cells comprises a stain or dye configured to isolate the bone or fat cells.
  • the target cells comprise tumor cells; and the second stain or dye for differentiating the target cells comprises a stain or dye configured to isolate the tumor cells.
  • Certain embodiments may further include: of the fully formed vessels and vessel fragments detected within the first digital image: digitally omitting from the vessel proximity score any detected vessels having a diameter or vessel area greater than a maximum vessel size.
  • a method for measuring and reporting vascularity in a biological tissue sample includes: obtaining one or more tissue sections from the tissue sample; staining each of the tissue sections with: a first stain or dye for differentiating endothelial cells, lymphatic cells, or a combination thereof; acquiring at least a first digital image, the first digital image capturing at least a portion of a first tissue section of the stained tissue sections; with the first digital image of the first tissue section or portion thereof: detecting at least one of: fully formed vessels and vessel fragments using visual characteristics associated with the first stain or dye; mapping about the first digital image one or more first proximity regions, each of the first proximity regions comprising an area between the detected vessels and a first distance outwardly therefrom; detecting tissue outside of the detected vessels; calculating a tissue hypoxia score, wherein the tissue hypoxia score comprises one of: a percentage of the tissue disposed outside of the first proximity regions, or a percentage of the tissue disposed within the first proximity regions; and recording the tissue hypoxia score.
  • the claimed invention applies to methods for measuring and reporting vascularity in biological tissue samples, which methods are useful in the field of digital pathology analysis.
  • Van der Laak, J., et al. An improved procedure to quantify tumor vascularity using true color image analysis: comparison with the manual hot-spot procedure in a human melanoma xenograft model. J. Pathol, 1998. 184: p. 136-143.
  • prognostic marker in human carcinomas a critical evaluation of histopathological methods for estimation of vascular density. European Journal of Cancer, 2003. 39(7): p. 881-890.
  • Sharma, S., M. Sharma, and C. Sarkar Morphology of angiogenesis in human cancer: a conceptual overview, histoprognostic perspective and significance of
  • angiogenesis in chronic myeloid leukemia a morphometric study. Leukemia, 2003. 17(1): p. 89-97.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Immunology (AREA)
  • Chemical & Material Sciences (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Microbiology (AREA)
  • Biochemistry (AREA)
  • Biotechnology (AREA)
  • Medicinal Chemistry (AREA)
  • Organic Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Cell Biology (AREA)
  • Pathology (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Multimedia (AREA)
  • Oncology (AREA)
  • Hospice & Palliative Care (AREA)
  • Genetics & Genomics (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biophysics (AREA)
  • Theoretical Computer Science (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Virology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La présente divulgation concerne un procédé de mesure et de rapport de vascularité dans un échantillon de tissu biologique. De manière générale, le procédé comprend : dans une image numérique d'une coupe tissulaire, (i) l'identification des cellules endothéliales, des cellules lymphatiques, ou d'une combinaison de celles-ci ; (ii) la cartographie d'une ou de plusieurs régions de proximité, où chacune desdites régions de proximité définit une zone entre des vaisseaux détectés et une première distance vers l'extérieur à partir de ceux-ci ; et (iii) le calcul d'un ou de plusieurs paramètres parmi : un score de proximité de vaisseau ou un score d'hypoxie, où le score de proximité de vaisseau concerne une composition d'objets à l'intérieur desdites régions de proximité, et le score d'hypoxie concerne une composition tissulaire à l'intérieur ou à l'extérieur desdites régions de proximité, respectivement.
PCT/US2017/040777 2017-07-05 2017-07-05 Procédés de mesure et de rapport de vascularité dans un échantillon de tissu WO2019009893A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2017/040777 WO2019009893A1 (fr) 2017-07-05 2017-07-05 Procédés de mesure et de rapport de vascularité dans un échantillon de tissu

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2017/040777 WO2019009893A1 (fr) 2017-07-05 2017-07-05 Procédés de mesure et de rapport de vascularité dans un échantillon de tissu

Publications (1)

Publication Number Publication Date
WO2019009893A1 true WO2019009893A1 (fr) 2019-01-10

Family

ID=64950295

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2017/040777 WO2019009893A1 (fr) 2017-07-05 2017-07-05 Procédés de mesure et de rapport de vascularité dans un échantillon de tissu

Country Status (1)

Country Link
WO (1) WO2019009893A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114022481A (zh) * 2022-01-06 2022-02-08 武汉大学 食管癌乏血管区域大小的确定方法和系统
WO2024036109A1 (fr) * 2022-08-08 2024-02-15 Google Llc Traçage cellulaire et apprentissage automatique pour génération de modèles de maladies à faible variance

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030165263A1 (en) * 2002-02-19 2003-09-04 Hamer Michael J. Histological assessment
US20110103657A1 (en) * 2008-01-02 2011-05-05 Bio-Tree Systems, Inc. Methods of obtaining geometry from images
US20120076390A1 (en) * 2010-09-28 2012-03-29 Flagship Bio Methods for feature analysis on consecutive tissue sections
US20140105824A1 (en) * 2012-10-16 2014-04-17 H. Michael Shepard Hypoxia and hyaluronan and markers thereof for diagnosis and monitoring of diseases and conditions and related methods
US20150004630A1 (en) * 2013-02-25 2015-01-01 Flagship Biosciences, LLC Cell-based tissue analysis
US20150290250A1 (en) * 2012-11-20 2015-10-15 Albert Einstein College Of Medicine Of Yeshiva University Regeneration of coronary artery by coronary endothelial specific progenitor cells

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030165263A1 (en) * 2002-02-19 2003-09-04 Hamer Michael J. Histological assessment
US20110103657A1 (en) * 2008-01-02 2011-05-05 Bio-Tree Systems, Inc. Methods of obtaining geometry from images
US20120076390A1 (en) * 2010-09-28 2012-03-29 Flagship Bio Methods for feature analysis on consecutive tissue sections
US20140105824A1 (en) * 2012-10-16 2014-04-17 H. Michael Shepard Hypoxia and hyaluronan and markers thereof for diagnosis and monitoring of diseases and conditions and related methods
US20150290250A1 (en) * 2012-11-20 2015-10-15 Albert Einstein College Of Medicine Of Yeshiva University Regeneration of coronary artery by coronary endothelial specific progenitor cells
US20150004630A1 (en) * 2013-02-25 2015-01-01 Flagship Biosciences, LLC Cell-based tissue analysis

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114022481A (zh) * 2022-01-06 2022-02-08 武汉大学 食管癌乏血管区域大小的确定方法和系统
CN114022481B (zh) * 2022-01-06 2022-04-19 武汉大学 食管癌乏血管区域大小的确定方法和系统
WO2024036109A1 (fr) * 2022-08-08 2024-02-15 Google Llc Traçage cellulaire et apprentissage automatique pour génération de modèles de maladies à faible variance

Similar Documents

Publication Publication Date Title
US10191053B2 (en) Methods for measuring and reporting vascularity in a tissue sample
Hollon et al. Rapid intraoperative diagnosis of pediatric brain tumors using stimulated Raman histology
JP6772066B2 (ja) 画像を処理して解析するための検査装置
Eguchi et al. The new IASLC-ATS-ERS lung adenocarcinoma classification: what the surgeon should know
CA2473325C (fr) Procede et/ou systeme d'analyse de prelevements biologiques faisant appel a un systeme informatique
Raimondo et al. Automated evaluation of Her-2/neu status in breast tissue from fluorescent in situ hybridization images
JP6106259B2 (ja) 医用イメージングと生検データとを統合する臨床ワークステーション及びこれを使用する方法
US11341649B2 (en) Systems and methods for quantifying multiscale competitive landscapes of clonal diversity in glioblastoma
Cho et al. Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients
Chantrain et al. Computerized quantification of tissue vascularization using high-resolution slide scanning of whole tumor sections
US9603567B2 (en) System and method for evaluation of disease burden
US20100279341A1 (en) Methods and system for analyzing cells
US20040023320A1 (en) Method and system for analyzing cells
Sasano et al. Pathological evaluation of angiogenesis in human tumor
WO2019009893A1 (fr) Procédés de mesure et de rapport de vascularité dans un échantillon de tissu
Krueger et al. Visualizing regional myocardial blood flow in the mouse
CN105167795A (zh) 一种pet/ct宏观数字信息与病理微观信息匹配的方法
Onwudiwe et al. Actin cytoskeletal structure and the statistical variations of the mechanical properties of non-tumorigenic breast and triple-negative breast cancer cells
US20170270660A1 (en) Method for assigning tissue normalization factors for digital image analysis
Hill et al. Non-invasive prostate cancer characterization with diffusion-weighted MRI: insight from in silico studies of a transgenic mouse model
Potts et al. Practical approaches to microvessel analysis: hotspots, microvessel density, and vessel proximity
RU2589652C1 (ru) Способ предоперационного определения степени злокачественности и гистологического подтипа опухолей оболочек головного мозга
Ponti et al. Value and prognostic significance of mitotic rate in a retrospective series of pT1 cutaneous malignant melanoma patients
US20190252075A1 (en) Predicting Prostate Cancer Recurrence Using a Prognostic Model that Combines Immunohistochemical Staining and Gene Expression Profiling
Davis et al. Toward precision medicine: promising areas of research in glioma

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17917046

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17917046

Country of ref document: EP

Kind code of ref document: A1