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: Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.

Abstract: Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.

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.