Abstract: One embodiment include an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment radiomic features from the registered image; a delta radiomics circuit that generates a set of delta radiomic features by computing a difference between the set of post-treatment radiomic features and the set of pre-treatment radiomic features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based, at least in part, on the classification.

Abstract: Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.

Abstract: Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.

Abstract: Embodiments predict prostate cancer (PCa) biochemical recurrence (BCR) employing an image acquisition circuit that accesses a first pre-treatment image and a second pre-treatment image of a region of tissue demonstrating PCa, a distension feature circuit that extracts a set of distension features from the first pre-treatment image, and computes a first probability of PCa BCR based on the set of distension features, a radiomics circuit that extracts a set of radiomics features from the second pre-treatment image, and computes a second probability of PCa recurrence based on the set of radiomics feature, a combined tumor induced organ distension with tumor radiomics (COnTRa) circuit that computes a joint probability that the region of tissue will experience PCa BCR based on the first probability and the second probability, and a display circuit that displays the joint probability.

Abstract: Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Abstract: Methods, apparatus, and other embodiments predict heart failure from WSIs of cardiac histopathology using a deep learning convolutional neural network (CNN). One example apparatus includes a pre-processing circuit configured to generate a pre-processed WSI by downsampling a digital WSI; an image acquisition circuit configured to randomly select a set of non-overlapping ROIs from the pre-processed WSI, and configured to provide the set of non-overlapping ROIs to a deep learning circuit; a deep learning circuit configured to generate an image-level probability that a member of the set of non-overlapping ROIs is a failure/abnormal pathology ROI using a CNN; and a classification circuit configured to generate a patient-level probability that the patient from which the region of tissue represented in the WSI was acquired is experiencing failure or non-failure based, at least in part, on the image-level probability.

Abstract: Embodiments facilitate stratification of a patient according to prostate cancer (PCa) risk. A first set of embodiments relates to training of a machine learning classifier to compute a probability that a patient has a low-risk of PCa progression based on intratumoral radiomic features and peritumoral radiomic features extracted from multi-parametric magnetic resonance imaging (mpMRI) images. A second set of embodiments relates to classifying a patient as low-risk of PCa progression, or high-risk of PCa progression, based on radiomic features extracted from mpMRI imagery of the patient.

Abstract: Embodiments facilitate predicting a patient prostate cancer (PCa) DECIPHER risk group. A first set of embodiments relates to training of a machine learning classifier to compute a probability that a patient is a member of a DECIPHER low/intermediate risk group based on radiomic features extracted from bi-parametric magnetic resonance imaging (bpMRI) images. A second set of embodiments relates to classifying a patient as a member of DECIPHER low/intermediate risk group, or DECIPHER high-risk group, based on radiomic features extracted from bpMRI imagery of the patient.

Abstract: Embodiments include an image acquisition circuit configured to access an image of a region of tissue demonstrating cancerous pathology, a nuclei detection and graphing circuit configured to detect cellular nuclei represented in the image; and construct a nuclear sub-graph based on the detected cellular nuclei, where a node of the sub-graph is a nuclear centroid of a cellular nucleus; a cell run length (CRF) circuit configured to compute a CRF vector based on the sub-graph; compute a set of CRF features based on the CRF vector and the sub-graph; and generate a CRF signature based, at least in part, on the set of CRF features; and a classification circuit configured to compute a probability that the region of tissue will experience cancer progression, based, at least in part, on the CRF signature; and generate a classification of the region of tissue as a progressor or non-progressor.

Abstract: Embodiments classify a region of tissue demonstrating non-small cell lung cancer using quantified vessel tortuosity (QVT). One example apparatus includes annotation circuitry configured to segment a lung region from surrounding anatomy in the region of tissue represented in a radiological image and segment a nodule from the lung region by defining a nodule boundary; vascular segmentation circuitry configured to generate a three dimensional (3D) segmented vasculature by segmenting a vessel associated with the nodule, and to identify a center line of the 3D segmented vasculature; QVT feature extraction circuitry configured to extract a set of QVT features from the radiological image; and classification circuitry configured to compute a probability that the region of tissue will respond to immunotherapy and generate a classification that the region of tissue is a responder or a non-responder based, at least in part, on the probability.

Abstract: Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

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 access a pre-immunotherapy image of tissue demonstrating NSCLC including a tumor and a peritumoral region; extract a first set of radiomic features from the image; provide the first set of radiomic features to a first machine learning classifier; receive a first probability from the first classifier that the tissue is hyperprogressor (HP) or non-responder (R); if the first probability that the tissue is within a threshold: generate a first classification of the ROT as HP or non-R based on the first probability; if the first probability is not within the threshold: extract a second set of radiomic features from the peritumoral region and provide the second set to a second machine learning classifier; receive a second probability from the second classifier that the tissue is HP or R; generate a second classification of the tissue as HP or R based on the second probability; and display the classification.

Abstract: Embodiments associated with classifying a region of tissue using features extracted from nodules and surrounding structures. One example apparatus includes a feature extraction circuit configured to automatically extract a first set of quantitative features from a nodule represented in at least one CT image, and automatically extract a second set of quantitative features from the lung parenchyma region immediately surrounding the nodule represented in the at least one CT image; a feature selection circuit configured to select an optimally predictive feature set from the first set of quantitative features and the second set of quantitative features; and a training circuit configured to train a classifier using the optimally predictive feature set to assign malignancy risk to a lung nodule represented in a CT image of a region of tissue demonstrating lung nodules. A prognosis or treatment plan may be provided based on the malignancy risk.

Abstract: Methods, apparatus, and other embodiments predict response to pemetrexed based chemotherapy. One example apparatus includes an image acquisition circuit that acquires a radiological image of a region of tissue demonstrating NSCLC that includes a region of interest (ROI) defining a tumoral volume, a peritumoral volume definition circuit that defines a peritumoral volume based on the boundary of the ROI and a distance, a feature extraction circuit that extracts a set of discriminative tumoral features from the tumoral volume, and a set of discriminative peritumoral features from the peritumoral volume, and a classification circuit that classifies the ROI as a responder or a non-responder using a machine learning classifier based, at least in part, on the set of discriminative tumoral features and the set of discriminative peritumoral features.

Abstract: Embodiments include operations, apparatus, methods and other embodiments that access a baseline CT image of a region of tissue (ROT) demonstrating non-small cell lung cancer (NSCLC), segment a tumoral region represented in the baseline CT image; define a peritumoral region by dilating the tumoral boundary; extract a set of tumoral radiomic features from the tumoral region, a set of peritumoral radiomic features from the peritumoral region, and a set of clinico-pathologic features from the baseline CT image; provide the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features to a machine learning classifier; receive, from the machine learning classifier, a time-to-recurrence post trimodality therapy (TMT) prediction, based on the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features; generate a classification of the ROT as an MPR responder or MPR non-responder based, at least in part, on the time-to-recurrence post-TMT predicti

Abstract: One embodiment includes an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment quantitative vessel tortuosity (QVT) features from the registered image; a delta-QVT circuit that generates a set of delta-QVT features by computing a difference between the set of post-treatment QVT features and the set of pre-treatment QVT features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based on the classification.

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: Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.

Abstract: Embodiments access a radiological image of tissue having a tumoral volume and a peritumoral volume; define a vasculature associated with the tumoral volume; generate a Cartesian two-dimensional (2D) vessel network representation; compute a first set of localized Hough transforms based on the Cartesian 2D vessel network representation; generate a first aggregated set of peak orientations based on the first set of Hough transforms; generate a spherical 2D vessel network representation; compute a second set of localized Hough transforms based on the spherical 2D vessel network representation; generate a second aggregated set of peak orientations based on the second set of Hough transforms; generate a vascular network organization descriptor based on the aggregated peak orientations; compute a probability that the tissue is a member of a positive class based on the vascular network organization descriptor; classify the ROI based on the probability; and display the classification.