Abstract: Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

Abstract: The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4/6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4/6 inhibitor therapy for HR+ metastatic breast cancer.

Abstract: The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.

Abstract: Various embodiments of the present disclosure are directed towards a method for predicting a response to treatment of small cell lung cancer (SCLC). The method includes generating a radiomic risk score (RRS) for the patient based on a plurality of radiomic features, wherein the RRS is prognostic of overall survival (OS) of the patient. The RRS is provided to a machine learning classifier that is trained to predict a response of the patient to a SCLC chemotherapy treatment based, at least in part, on the RRS. The machine learning classifier provides a classification of the patient into either a responder group (RG) or a non-responder group (NRG), where the NRG indicates the patient will not respond to the SCLC chemotherapy treatment and the RG indicates that the patient will respond to the SCLC chemotherapy treatment.

Abstract: Embodiments include accessing an image of a region of tissue demonstrating cancerous pathology; detecting a plurality of cells represented in the image; segmenting a cellular nucleus of a first member of the plurality of cells and a cellular nucleus of at least one second, different member of the plurality of cells; extracting a set of nuclear morphology features from the plurality of cells; constructing a feature driven local cell graph (FeDeG) based on the set of nuclear morphology features and a spatial relationship between the cellular nuclei using a mean-shift clustering approach; computing a set of FeDeG features based on the FeDeG; providing the FeDeG features to a machine learning classifier; receiving, from the machine learning classifier, a classification of the region of tissue as a long-term or a short-term survivor, based, at least in part, on the set of FeDeG features; and displaying the classification.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.

Abstract: Embodiments include controlling a processor to access an image of a region of tissue demonstrating cancerous pathology; segment a cellular nucleus represented in the image; extract a first set of features from the segmented cellular nucleus; classify the segmented nucleus as a lymphocyte or non-lymphocyte based on the first set of features; for a segmented nucleus classified as a lymphocyte: computing a set of contextual features; assign the segmented nucleus classified as a lymphocyte to one of a plurality of clusters based on the set of contextual features; compute a frequency distribution of the clustered segmented nuclei classified as lymphocytes; provide the frequency distribution to a machine learning classifier; receive, from the machine learning classifier, a classification of the region of tissue as likely to experience recurrence or unlikely to experience recurrence, based, at least in part, on the frequency distribution; and display the classification.

Abstract: Embodiments include accessing an image of a region of tissue demonstrating cancerous pathology; detecting a plurality of cells represented in the image; segmenting a cellular nucleus of a first member of the plurality of cells and a cellular nucleus of at least one second, different member of the plurality of cells; extracting a set of nuclear morphology features from the plurality of cells; constructing a feature driven local cell graph (FeDeG) based on the set of nuclear morphology features and a spatial relationship between the cellular nuclei using a mean-shift clustering approach; computing a set of FeDeG features based on the FeDeG; providing the FeDeG features to a machine learning classifier; receiving, from the machine learning classifier, a classification of the region of tissue as a long-term or a short-term survivor, based, at least in part, on the set of FeDeG features; and displaying the classification.

Abstract: The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.

Abstract: Embodiments discussed herein facilitate determining a diagnosis and/or prognosis for prostate cancer based at least in part on three-dimensional (3D) pathomic feature(s). One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing a three-dimensional (3D) optical image volume comprising a prostate gland of a patient; segmenting the prostate gland of the 3D optical image volume; extracting one or more features from the segmented prostate gland, wherein the one or more features comprise at least one 3D pathomic feature; and generating, via a model based at least on the one or more features, one or more of the following based at least on the extracted one or more features: a classification of the prostate gland as one of benign or malignant, a Gleason score associated with the prostate gland, or a prognosis for the patient.

Abstract: Embodiments generate an early stage NSCLC recurrence prognosis, and predict added benefit of adjuvant chemotherapy. Embodiments include processors configured to access a radiological image of a region of tissue demonstrating early stage NSCLC; segment a tumor represented in the radiological image; define a peritumoral region based on a morphological dilation of a boundary of the tumor; extract a radiomic signature that includes a set of tumoral radiomic features extracted from the tumoral region, and a set of peritumoral radiomic features extracted from the peritumoral region, based on a continuous time to event data; compute a radiomic score based on the radiomic signature; compute a probability of added benefit of adjuvant chemotherapy based on the radiomic score; and generate an NSCLC recurrence prognosis based on the radiomic score. Embodiments may display the radiomic score, or generate a personalized treatment plan based on the radiomic score.

Abstract: Embodiments discussed herein facilitate determination of tumor mutation status based on context and spatial information. One example embodiment can access a MRI scan of a tumor comprising voxels; extract radiomic feature(s) from the voxels; generate a spatial feature descriptor indicating probabilities the tumor has a first mutation status and a second mutation status, based on the MRI scan, a first population atlas for the first mutation status, and a second population atlas for the second mutation status; provide the radiomic feature(s) and the spatial feature descriptor to a machine learning model; and receive, via the machine learning model, a map indicating, for each voxel of the voxels, a probability of the first mutation status for that voxel and a probability of the second mutation status for that voxel, wherein the map is based at least on the one or more radiomic features and the spatial feature descriptor.

Abstract: Embodiments discussed herein facilitate determination of tumor mutation status based on context and spatial information. One example embodiment can access a MRI scan of a tumor comprising voxels; extract radiomic feature(s) from the voxels; generate a spatial feature descriptor indicating probabilities the tumor has a first mutation status and a second mutation status, based on the MRI scan, a first population atlas for the first mutation status, and a second population atlas for the second mutation status; provide the radiomic feature(s) and the spatial feature descriptor to a machine learning model; and receive, via the machine learning model, a map indicating, for each voxel of the voxels, a probability of the first mutation status for that voxel and a probability of the second mutation status for that voxel, wherein the map is based at least on the one or more radiomic features and the spatial feature descriptor.

Abstract: Embodiments discussed herein facilitate training deep learning models to generate synthetic versions of histological texture features and employing such deep learning models. One example embodiment is an apparatus configured to convert a stained histological image to grayscale; extract patches from the grayscale image; for each patch of the plurality of patches: provide that patch to a deep learning model trained to generate a synthetic version of a texture feature; and obtain an associated patch from the deep learning model that indicates an associated value of the synthetic version of the histology texture feature for each pixel of that patch; and merge the associated patches for each patch of the plurality of patches to generate an associated feature map for the stained histological image, wherein the associated feature map indicates the associated value of the synthetic version of the histology texture feature for each pixel of the plurality of pixels.

Abstract: Embodiments access a set of radiological images acquired from a population of subjects, where a member of the set of radiological images includes a left atrium (LA) region; construct a statistical shape differential atlas from the images; generate a template LA model from the statistical shape differential atlas, where the template LA model includes a site of interest (SOI); acquire a pre-ablation radiological image of a region of tissue in a patient demonstrating atrial fibrillation (AF) pathology; generate a patient LA model from the pre-ablation image; compute a deformation field that registers the SOI to the patient LA model using deformable registration; compute a patient feature vector based on the deformation field; generate an AF probability score for the patient based on the feature vector; generate a classification of the patient based, at least in part, on the AF probability score; and display the classification or the AF probability score.

Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.

Abstract: Embodiments discussed herein facilitate determination of responsiveness to chemoradiation treatment in rectal cancer patients based on structural deformation features obtained from a pre- or post-treatment medical imaging. One example embodiment can perform operations comprising: accessing an image volume of a rectum comprising a rectal tumor; generating a forward mapping based on non-rigidly registering a healthy rectal atlas to the image volume; inverting the forward mapping to generate an inverse mapping from the image volume to the healthy rectal atlas; determining, based on the inverse mapping, an associated deformation magnitude for each voxel of a plurality of voxels associated with the rectum; computing one or more structural deformation features based on the associated deformation magnitudes for the plurality of voxels; and predicting via a classifier whether or not the rectal tumor will respond to the chemoradiation treatment based at least in part on the one or more structural deformation features.

Abstract: Embodiments discussed herein facilitate determination of cancer stages based at least in part on shape, size, and/or texture features of cancer nuclei. One example embodiment is a method, comprising: accessing at least a portion of a digital whole slide image (WSI) comprising a tumor; segmenting (e.g., via a first deep learning model) the tumor on the at least the portion of the digital WSI; segmenting (e.g., via a second deep learning model) cancer nuclei in the segmented tumor; extracting one or more features from the segmented cancer nuclei; providing the one or more features extracted from the segmented cancer nuclei to a trained machine learning model; and receiving, from the machine learning model, an indication of a cancer stage of the tumor.

Abstract: Embodiments discussed herein facilitate determination of a likelihood of biochemical recurrence (BCR) of cancer (e.g., prostate cancer, etc.). One example embodiment is a method, comprising: accessing at least a portion of a digitized stained histology slide comprising a tumor; automatically segmenting, via a trained deep learning (DL) model, cribriform morphology in connection with the tumor on the at least the portion of the digitized stained histology slide; determining a cribriform-to-tumor area ratio (CAR) based at least in part on an area of the segmented cribriform morphology and an area of the tumor; and determining a risk of biochemical recurrence (BCR) of a cancer associated with the tumor based at least in part on the CAR.