BACKGROUND OF THE SUBJECT DISCLOSURE

Field of the Subject Disclosure

The present subject disclosure relates to computational pathology. More particularly, the present subject disclosure relates to predicting the risk of cancer recurrence among early-stage patients using histopathological images of tissue sections and patient survival outcome data.

Background of the Subject Disclosure

Biological specimens such as tissue sections, blood, cell cultures and the like may be stained with one or more stains to identify and quantify biomarker expressions in the tissue and subsequently analyzed by viewing or imaging the stained specimens. Observing the stained specimens, in combination with additional clinical information, enables diagnosis of disease, prognostic and/or predictive assessment of response to treatment, and assists in the development of new drugs to fight disease. As used herein, a target or target object is a feature of the specimen that a stain identifies. A target or target object may be a protein, protein fragment, nucleic acid, or other object of interest recognized by an antibody, a molecular probe, or a non-specific stain. Those targets that are specifically recognized may be referred to as biomarkers in this subject disclosure. Some stains do not specifically target a biomarker (e.g., the counterstain Hematoxylin which stains all the nuclei in tissue). While hematoxylin has a fixed relationship to its target, most immunohistochemical biomarkers can be identified with a choice of stain. A particular biomarker could be visualized using a variety of stains depending on the particular needs of the assay.

Patients with localized (early stage, resectable) breast cancer undergoing curative surgery and/or therapy have an underlying risk of local or distant cancer recurrence while those patients who experience recurrence exhibit an increased mortality rate. Depending on the size of risk, different treatment options exist. Thus, an assay that allows one to reliably identify patients with a low or high risk of cancer recurrence is needed. Accordingly, technologies are also needed that can reliably discriminate between high and low risk patients and provide healthcare providers with additional information to consider when determining a patient's treatment options.

SUMMARY OF THE SUBJECT DISCLOSURE

The present invention provides for an computational pathology system, where a digital pathology system is used to digitizing cancer biopsy tissue samples followed with using image analysis workflow methods for analyzing the digitized tissue slides and statistical analysis methods to correlate the obtained biomarker expressions in the tissue samples with the patient survival outcome information to construct and clinical use a prognostic model for a prognostic and predictive evaluation of cancer tissue samples, such as early stage cancer prognosis, as claimed in the independent claims. Embodiments of the invention are given in the dependent claims.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage breast cancer patients by constructing a prognostic model to predict a recurrence risk of the patient and to categorize the patient into a high or low risk group. A risk stratification system may be trained using training cohort that includes histopathological (H&E and IHC) tissue slides from several patients along with survival outcome data for said patients. The tissue slides may be processed according to a specific staining protocol, digitized on a bright field or fluorescent microscopic or whole slide scanner, and analyzed using automated image analysis algorithms to quantify stains or biomarker expressions in the tissue slides. Quantifying expression may be accomplished using a specific scoring protocol wherein the multiple slide scores may be combined to determine a risk stratification score. The risk stratification system may include a proportional hazards regression module that is used to generate an overall scoring algorithm for the patient which combines the information from all the tissue slide scores and statistically correlating against the survival outcome data for the patients in the training cohort. The proportional hazards regression module may include a Cox proportional hazards model A cutoff point may be computed on the risk stratification score which may comprise optimally stratifying the training patient sample set into low and high risk groups by maximizing the separation between the Kaplan-Meier survival probability estimate curves between them. Subsequently, any single patient's tissue slides that are processed using the same staining protocol, digitized, followed with image analysis and/or scores generated according to the scoring protocol may be combined and analyzed using the risk stratification scoring algorithm generated during the training process, and stratified using the generated cutoff point to predict a survival probability and/or prognosis for the single patient.

In one exemplary embodiment, the subject disclosure includes an image analysis system and computer implemented method for early stage cancer prognosis. Digitized whole slide images of serial section tissue slides are stained with the desired set of histopathological assays (H&E, IHC) utilizing either simplex or multiplex methodologies to evaluate the tumor and immune marker expressions in the tissue. Such staining methods may include, for example, (1) mapping one or more regions (all the tumor regions on the whole slide, specific “marker hotspots” i.e., tumor sub-regions where a particular marker is over-expressed, immune specific regions from the tissue microenvironment, stromal regions which are adjacent to tumor regions) annotated on a digitized image of the first tissue slide (example, H&E slide or Ki67 slide) or subset of a selected few slides (like H&E and Ki-67) to digitized images of each of a plurality of tissue slides, wherein the plurality of tissue slides correspond to serial sections from a tissue block; (2) and computing a whole slide score for the plurality of tissue slides by scoring using marker-specific image analysis algorithms to quantify the expression of one or more tumor markers or immune markers on each tissue slide; where the computed marker expression is quantified in terms of commonly used whole slide score metric (marker percentage positivity, H-score, absolute or relative count or density of particular cell type etc.); and (3) computing the risk stratification score by combining the whole slide scores using a mathematical formula and with a set of combining coefficients as determined by the prognostic model wherein a patient may be stratified into a low or high recurrence risk group based on a cut-off point of the risk stratification score.

In another exemplary embodiment, the subject disclosure comprises a system for early stage cancer prognosis. The system includes a processor; and a memory coupled to the processor, the memory configured to store computer-readable instructions that, when executed by the processor, cause the processor to perform operations comprising: generating a set of whole tumor regions on a H&E tissue slide, where the whole tumor regions can be either manually annotated by a pathologist on a digitized whole slide in a whole slide image reviewing system using the annotation tools or generated using an image analysis algorithm to automatically detect and identify the whole tumor regions; registering a whole-tumor region annotated on an H&E slide with each of a plurality of adjacent tissue slides and mapping the annotations to each of them; and using image analysis algorithms to analyze and generating a whole slide score for marker expression on each of the plurality of adjacent IHC tissue slides such as percentage positivity, H-score, total cell counts; generating a risk stratification score by combining the whole slide scores from the IHC slides using a mathematical formula with a set of coefficients, where the coefficient values are determined based on a statistical fit of the risk stratification score against the survival data for a plurality of patients using a Cox proportional hazard regression model to construct a prognostic model; and applying the derived prognostic model to a test series of images associated with a single patient, the test series of images also being annotated with the whole tumor workflow.

In yet another exemplary embodiment, the subject disclosure is comprises a tangible non-transitory computer-readable medium to store computer-readable code that is executed by a processor to perform operations. The system includes a processor and a memory coupled to the processor, the memory configured to store computer-readable instructions that, when executed by the processor, cause the processor to perform operations comprising: generating a set of “hot spots” regions on Ki67 tissue slide, where the hot spots reflect the invasive and aggressive tumor regions with high Ki67 marker expression and the spatial variability of marker expression in the tumor regions in the slide; the “hot spots” can be manually annotated by a pathologist on a digitized whole slide in a whole slide image reviewing system using the annotation tools or automatically generated by an image analysis algorithm to detect Ki67 stained tumor cells and over expressive regions; registering these hot spots annotated on Ki67 tissue slide with each of a plurality of adjacent tissue slides to map the annotated regions; generating a risk stratification score by combining the whole slide scores from the plurality of whole slide scores using a mathematical formula with a set of coefficients, where the coefficient values are specific to the type of annotations and workflow and are determined based on operations involving a statistical fit of the risk stratification score against the survival data using on Cox proportional hazards model for a plurality of training patients to construct a prognostic model; and applying the prognostic model to a test series of images associated with a single patient, the test series of images also being annotated with the Ki-67 hotspot workflow.

A ‘tissue sample’ as understood herein is any biological sample obtained from a tissue region, such as a surgical biopsy specimen that is obtained from a human or animal body for anatomic pathology. The tissue sample may be a prostrate tissue sample, a breast tissue sample, a colon tissue sample or a tissue sample obtained from another organ or body region.

A ‘multi-channel image’ as understood herein encompasses a digital image obtained from a biological tissue sample in which different biological structures, such as nuclei and tissue structures, are simultaneously stained with specific fluorescent dyes, each of which fluoresces in a different spectral band thus constituting one of the channels of the multi-channel image. The biological tissue sample may be stained by a plurality of stains and/or by a stain and a counterstain, the later being also referred to as a “single marker image”.

An ‘unmixed image’ as understood herein encompasses a grey-value or scalar image obtained for one channel of a multi-channel image. By unmixing a multi-channel image one unmixed image per channel is obtained.

A ‘color channel’ as understood herein is a channel of an image sensor. For example, the image sensor may have three color channels, such as red (R), green (G) and blue (B).

A ‘hot spot’ as understood herein is a region in a stained image that has a high intensity value and/or high variation of intensity values which signals a high tumor growth rate.

For example, Ki-67 hot spot detection is as such known from the prior art, cf. Khan Niazi M K, Yearsley M M, Zhou X, Frankel W L, Gurcan M N. Perceptual clustering for automatic hotspot detection from Ki-67-stained neuroendocrine tumour images. J Microsc. 2014 December; 256(3):213-25. doi: 10.1111/jmi.12176. Epub 2014 Sep 16. PMID: 25228134.

A ‘field of view (FOV)’ as understood herein encompasses an image portion that has a predetermined size and shape, such as a rectangular or circular shape. In accordance with embodiments of the invention a tissue region of a cancer biopsy tissue sample is sliced into neighboring tissue slides resulting in respective tissue slides.

The tissue slices may be marked by single or multiple stains for the identification of respective biological features. A digital image is acquired from each of the marked tissue slices by means of an image sensor that has a number of color channels, such as an RGB image sensor.

Embodiments of the present invention are particularly advantageous due to the limitation of the scoring to the tumor or hotspot regions within the images. This way a surprisingly reliable result is obtained from the statistical model which enables to predict whether the administration of adjuvant chemotherapy is required for a given patient for the prevention of cancer reoccurrence or not. As a consequence, embodiments of the present invention provide a significant progress in the treatment of cancer patients as the unnecessary administration of chemotherapy can be avoided for those patients where the statistical model predicts a low risk of cancer reoccurrence.

An inter-marker image registration algorithm may be performed with respect to the acquired multiple digital images.

Various suitable image registration algorithms that are as such known from the prior art can be used for performing the image registration. In particular, an affine or deformable transformation can be utilized to perform the image registration.

The image registration algorithm generates a geometrical transformation that aligns corresponding points of the images. The geometrical transformation can be provided in the form of mappings, where each mapping maps the points of one of the images to corresponding points of another one of the images.

The images are aligned in accordance with the image registration. In other words, the geometrical transformations that are generated by the image registration algorithm are applied to the images for aligning the images such as to display the aligned images on a display in a two-dimensional plane.

In accordance with embodiments of the invention at least one of the tissue slices is marked by multiple stains for the acquisition of a multi-channel image. The multi-channel image is unmixed to provide a set of unmixed images. The unmixed images do not need to be registered with respect to each other or with respect to the multi-channel image as they are all based on the identical dataset that is acquired by the optical sensor from one of the tissue slices. The multi-channel image is selected as a reference image for performing the image registration algorithm with respect to the multiple images excluding the set of unmixed images. This provides a mapping of each one of the multiple images to the reference image, except for the unmixed images.

Using the multi-channel image as a reference image for the image registration is advantageous as it reduces the computational cost of performing the image registration and the alignment of the images as no image registration and alignment is required for the unmixed images.

Prognosis of hormone-positive early-stage breast cancer patients offers the opportunity to make more informed follow-up choices, for example the addition of adjuvant chemotherapy. Traditionally, pathologists have prognosticated these cancers using conventional staging, tumor proliferation index, and a small set of morphological features (gland formation, nuclear grade, and mitosis) that are manually scored from H&E slides.

Surprisingly embodiments of the invention enable to predict whether a given patient belongs to a low risk group such that e.g. hormone therapy alone is sufficient and chemotherapy and its adverse side-effects can be avoided.

It is to be noted that in the prior art an adjuvant chemotherapy is always administered to breast cancer patients in addition to hormone therapy as about 15% of breast cancer patients are not responsive to hormone therapy. As there is no way in the prior art to reliably predict whether a patient belongs to a low or high risk group, chemotherapy is always given as an adjuvant therapy. Thus, the present invention greatly reduces unnecessary hardship experienced by cancer patients as embodiments of the invention enable to identify those patients that actually do require chemotherapy such that administration of chemotherapy can be avoided for the majority of patients.

