WO2016189065A1 - Méthode et système d'évaluation de la qualité de la coloration pour l'immunocytochimie et l'hybridation in situ - Google Patents

Méthode et système d'évaluation de la qualité de la coloration pour l'immunocytochimie et l'hybridation in situ Download PDF

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Publication number
WO2016189065A1
WO2016189065A1 PCT/EP2016/061859 EP2016061859W WO2016189065A1 WO 2016189065 A1 WO2016189065 A1 WO 2016189065A1 EP 2016061859 W EP2016061859 W EP 2016061859W WO 2016189065 A1 WO2016189065 A1 WO 2016189065A1
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WIPO (PCT)
Prior art keywords
values
intensity
stain
analyte
image
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PCT/EP2016/061859
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English (en)
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WO2016189065A8 (fr
Inventor
Karl Garsha
Michael Otter
Benjamin Stevens
Jefferson TAFT
Frank Ventura
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Ventana Medical Systems, Inc.
F. Hoffmann-La Roche Ag
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Application filed by Ventana Medical Systems, Inc., F. Hoffmann-La Roche Ag filed Critical Ventana Medical Systems, Inc.
Priority to JP2017561410A priority Critical patent/JP6673941B2/ja
Priority to EP16725132.1A priority patent/EP3304415B1/fr
Priority to CA2981155A priority patent/CA2981155C/fr
Priority to AU2016269085A priority patent/AU2016269085B2/en
Publication of WO2016189065A1 publication Critical patent/WO2016189065A1/fr
Publication of WO2016189065A8 publication Critical patent/WO2016189065A8/fr
Priority to US15/819,676 priority patent/US10614284B2/en

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    • 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
    • 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

Definitions

  • ISH assays For commercial in-vitro diagnostic ISH assays, the staining performance, e.g. stain uniformity, stain intensity, or background staining, must be validated to deliver sensitive and specific staining to the sequences of interest, with a high degree of repeatability. For this reason, specifications that define the various factors that influence acceptable performance of ISH assays must be developed and documented.
  • the entropy values are computed by (i) deriving image histogi'ams of intensity values from each of the analyte intensity images, and (ii) calculating a probability that a pixel sampled from an analyte intensity image has a particular value in the respective histogram.
  • the image histograms of intensity values are derived by sorting pixels from each analyte intensity image into bins.
  • the multi -spectral image data comprises scanned images of a stained tissue specimen, e.g. specimens mounted on a slide.
  • spectral images of the tissue sample are taken at several axial positions.
  • the scanned images are combined into a spectral cube (also referred to as an image cube or hyperspectral cube to those of ordinary skill in the art).
  • the tissue samples were stained in a multiplex IHC and/or ISH assay for the detection of biomarkers therein.
  • a computer device for objective stain assessment comprising one or more processors and at least one memory, the at least one memory storing non-transitory computer-readable instructions for execution by the one or more processors to cause the one or more processors to: unmix a multi-spectral image of a tissue specimen to obtain analyte intensity images; compute metrics based on the analyte intensity images, wherein a first metric is a numerical descriptor of the uniformity and distribution of pixel intensity values in the analyte intensity images, and wherein a second metric is a numerical descriptor of the dispersion of pixel intensity values in the analyte intensity images, the pixel intensity values corresponding to signals from a detectable stain in each analyte intensity image; assess a stain quality of a slide by comparing the computed metrics to pre-determined cutoff values, wherein the stain quality of the slide is assessed as acceptable if the computed metrics meet or exceed the pre-determined cut
  • FIG. 5B illustrates an example of an intensity histogram and the number of pixels in any particular bin
  • spectral cube refers to data aligned along three dimensions. Two of the dimensions are the 'x' and 'y' coordinates of an image field of view and the third dimension is wavelength.
  • target refers to any molecule for which the presence, location and/or concentration is or can be determined.
  • target molecules include proteins, nucleic acid sequences, and haptens, such as haptens covalently bonded to proteins.
  • Target molecules are typically detected using one or more conjugates of a specific binding molecule and a detectable label.
  • the columns of the M x N matrix R is the known reference spectral signature of the individual fluorophores and the N x l vector A is the unknown of the proportions of individual fluorophores and the M x 1 vector S is the measured multichannel spectral vector at a pixel.
  • the signal in each pixel (S) is measured during acquisition of the multiplex image and the reference spectra for the known stains are usually determined in an independent offline method from fluorescent specimens labeled with only a single stain using identical instrument settings. It becomes a simple linear algebra matrix exercise to determine the contributions of various stains (Ai) by calculating their contribution to each point in the measured spectrum.
  • unmixing is accomplished using the methods described in
  • the unmixed images may be limited to a certain dynamic range constraint.
  • the dynamic range may be limited by applying a thresholding filter, step (450).
  • the threshold is set to cover the entire positive spectrum range of the unmixed images (0-100% of the brightest value present with negative values clamped to a value of zero). This operation restricts analysis of signals to a consistent part of the dynamic range of the data acquired and avoids the inclusion of pixel values that are not relevant to the signal localization.
  • This step is repeated for each bin (and for each of the selected channels) to provide a probability of a sampled pixel belonging in a particular bin.
  • Each probability is then multiplied by the logarithm to the base of 2 of the probability to convert the value to a unit of bits, step (570).
  • the probabilities are then summed to estimate the entropy for the analyte channels, step (580).
  • a metric is derived that serves as a numerical descriptor of the dispersion of the pixel intensity values for a spectrally unmixed image.
  • the metric of the dispersion of intensity values is a mean to variance ratio ("M7V Ratio"), i.e., a ratio of a mean intensity value of a signal in an image histogram to a variance value of the signal in an image histogram.
  • the pixel intensity mode value represents the pixel intensity value that occurs most often in a particular histogram (and hence in an analyte intensity image).
  • the pixel intensity variance value represents a value of how spread apart certain pixel intensities are in a histogram (and hence in an analyte intensity image).
  • the analyte intensity images to be measured are selected, step (510), and histograms for each analyte intensity image are derived, step (520).
  • the mean value and variance of pixel intensity are measured from each derived image intensity histogram, step (530).
  • a ratio of the mean value and variance of pixel intensity is then computed, step (540), to provide the M/V Ratio metric.
  • objecti ve criteria may be empirically determined by comparing and evaluating reference slides that are known to be acceptable as determined by an expert pathologist, steps (710), to those that are known to be unacceptable as determined by an expert pathologist, steps (710), wherein the same analytes are being detected in both the acceptable and unacceptable quality slides.
  • metrics e.g., entropy and M/V ratio
  • steps (720) and (730) and the metrics for all reference slides are analyzed to derive cutoff values or a decision boundary, using one of a support vector machine, a linear discriminant analysis, or a logistic regression analysis, steps (740).
  • step (920) an evaluation is made as to whether the result is inconsistent, step (920).
  • step (920) To determine whether an inconsistent result is caused by a slide staining apparatus or the scanner, the slide is classified on a separate imaging instrument, step (930) and if the slide meets the staining specification, step (940), the scanner should be checked for inconsistencies. On the other hand, if the slide does not meet the staining specification, step (950), a staining apparatus should be checked for inconsistencies.
  • the artifacts that lead to an inconsistent result may be attributed to the imaging process.
  • detectable labels include enzymes such as horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, ⁇ -galactosidase or ⁇ -glucuronidase; fluorphores such as fluoresceins, luminophores, coumarins, BODJPY dyes, resorufins, and rhodamines (many additional examples of fluorescent molecules can be found in The Handbook— A Guide lo Fluorescent Probes and Labeling Technologies, Molecular Probes, Eugene, OR); nanoparticles such as quantum dots (described further below); metal chelates such as DOTA and DPTA chelates of radioactive or paramagnetic metal ions like Gd 3+ ; and liposomes, for example, liposomes containing trapped fluorescent molecules.
  • enzymes such as horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, ⁇ -galact
  • Logic refers to any information having the form of instruction signals and/or data that may be applied to affect the operation of a processor.
  • Software is one example of such logic.
  • Logic may also comprise digital and/or analog hardware circuits, for example, hardware circuits comprising logical AND, OR, XOR, NAND, NOR, and other logical operations.
  • Logic may be formed from combinations of software and hardware.
  • On a network logic may be programmed on a server, or a complex of servers. A particular logic unit is not limited to a single logical location on the network.
  • a computer having a display device, e.g., an LCD (liquid crystal display), LED (light emitting diode) display, or OLED (organic light emitting diode) display, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., an LCD (liquid crystal display), LED (light emitting diode) display, or OLED (organic light emitting diode) display
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • a touch screen can be used to display information and receive input from a user.
  • chromosomal rearrangements involving the SYT gene located in the breakpoint region of chromosome 18ql 1.2 are common among synovial sarcoma soft tissue tumors.
  • the t(18ql 1.2) translocation can be identified, for example, using probes with different labels: the first probe includes FPC nucleic acid molecules generated from a target nucleic acid sequence that extends distally from the SYT gene, and the second probe includes FPC nucleic acid generated from a target nucleic acid sequence that extends 3' or proximal to the SYT gene.
  • a target protein produced from a nucleic acid sequence is selected that is a tumor suppressor gene that is deleted (lost) in malignant cells.
  • the target protein is produced from a nucleic acid sequence

Abstract

L'immunocytochimie (IHC) et l'hybridation in situ (ISH) ont pour but de détecter, localiser et quantifier certains analytes à diverses fins de diagnostic. La qualité des colorants qui sont analysés peut varier pour diverses raisons. Par conséquent, la présente invention concerne une méthode et un système pour l'évaluation de la qualité de la coloration et l'établissement des critères objectifs pour l'évaluation de la qualité de la coloration, dans le cadre d'une application dans les domaines de l'immunocytochimie et de l'hybridation in situ. Dans un mode de réalisation possible, l'invention comprend les étapes consistant à séparer des données d'images multi-spectrales provenant d'un échantillon de tissu afin d'obtenir des images d'intensité d'analyte, chaque image d'intensité d'analyte comprenant des signaux provenant d'une coloration unique, calculer des paramètres sur la base des images d'intensité d'analyte, les paramètres étant l'uniformité, la répartition et/ou la dispersion des valeurs d'intensité de pixels dans les images d'intensité d'analyte, et évaluer la qualité de la coloration d'une lame en comparant les paramètres calculés à des valeurs seuils pré-définies concernant l'uniformité, la répartition et/ou la dispersion des valeurs d'intensité de pixels, la qualité de la coloration de la lame étant évaluée comme étant acceptable si les paramètres calculés satisfont ou dépassent les valeurs seuils pré-définies, et la qualité de la coloration de la lame étant évaluée comme inacceptable si les paramètres calculés ne satisfont pas aux valeurs seuils pré-définies. Afin d'établir des critères objectifs d'évaluation de la qualité de la coloration, dans un mode de réalisation possible, la méthode et le système comprennent une étape consistant à dériver des valeurs seuils pour l'uniformité, la répartition et/ou la dispersion de l'intensité des pixels en associant les paramètres calculés sur la base des images d'intensité d'analyte avec des données pré-définies quantifiant la qualité de la coloration.
PCT/EP2016/061859 2015-05-26 2016-05-25 Méthode et système d'évaluation de la qualité de la coloration pour l'immunocytochimie et l'hybridation in situ WO2016189065A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
JP2017561410A JP6673941B2 (ja) 2015-05-26 2016-05-25 イン・サイチュー・ハイブリダイゼーションおよび免疫組織化学のための染色品質評価方法およびシステム
EP16725132.1A EP3304415B1 (fr) 2015-05-26 2016-05-25 Méthode et système d'évaluation de la qualité de la coloration pour l'immunocytochimie et l'hybridation in situ
CA2981155A CA2981155C (fr) 2015-05-26 2016-05-25 Methode et systeme d'evaluation de la qualite de la coloration pour l'immunocytochimie et l'hybridation in situ
AU2016269085A AU2016269085B2 (en) 2015-05-26 2016-05-25 Method and system for assessing stain quality for in-situ hybridization and immunohistochemistry
US15/819,676 US10614284B2 (en) 2015-05-26 2017-11-21 Descriptive measurements and quantification of staining artifacts for in situ hybridization

Applications Claiming Priority (5)

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US201562166115P 2015-05-26 2015-05-26
US62/166,155 2015-05-26
US62/166,115 2015-05-26
US201662328041P 2016-04-27 2016-04-27
US62/328,041 2016-04-27

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Cited By (5)

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US20180143111A1 (en) * 2016-10-25 2018-05-24 Abbott Laboratories Assessment and Control of Reagents in Automated Slide Preparation
CN111986157A (zh) * 2020-07-21 2020-11-24 万达信息股份有限公司 一种数字病理图像质量评价系统
WO2021189771A1 (fr) * 2020-07-30 2021-09-30 平安科技（深圳）有限公司 Procédé et appareil de test de qualité d'informations de numérisation de diapositives, et dispositif et support
CN114383913A (zh) * 2021-12-31 2022-04-22 迈克医疗电子有限公司 样本制作仪的高低速控制方法及控制系统
CN114511559A (zh) * 2022-04-18 2022-05-17 杭州迪英加科技有限公司 染色鼻息肉病理切片质量多维评价方法、系统及介质

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Cited By (8)

* Cited by examiner, † Cited by third party
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US20180143111A1 (en) * 2016-10-25 2018-05-24 Abbott Laboratories Assessment and Control of Reagents in Automated Slide Preparation
US10871426B2 (en) * 2016-10-25 2020-12-22 Abbott Laboratories Assessment and control of reagents in automated slide preparation
CN111986157A (zh) * 2020-07-21 2020-11-24 万达信息股份有限公司 一种数字病理图像质量评价系统
CN111986157B (zh) * 2020-07-21 2024-02-09 万达信息股份有限公司 一种数字病理图像质量评价系统
WO2021189771A1 (fr) * 2020-07-30 2021-09-30 平安科技（深圳）有限公司 Procédé et appareil de test de qualité d'informations de numérisation de diapositives, et dispositif et support
CN114383913A (zh) * 2021-12-31 2022-04-22 迈克医疗电子有限公司 样本制作仪的高低速控制方法及控制系统
CN114383913B (zh) * 2021-12-31 2023-10-13 迈克医疗电子有限公司 样本制作仪的高低速控制方法及控制系统
CN114511559A (zh) * 2022-04-18 2022-05-17 杭州迪英加科技有限公司 染色鼻息肉病理切片质量多维评价方法、系统及介质

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