Abstract: Described herein are methods and computer systems for classification of CD8 T-cell topology using a patient response-based linear cutoff model. A plurality of histology images of tissue samples in a plurality of patients are received by a computer system. An image analysis of the plurality of histology images is performed to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of histology images. Real inflammation scores and tumor infiltration scores are determined based on a polar coordinate transformation of the CD8+ T-cell abundance in the tumor parenchyma and stroma. Based on the real inflammation scores and tumor infiltration scores, a feature space is generated, and linear boundaries or linear cutoffs between a plurality of classifications in the feature space are identified based on the real inflammation scores, the tumor infiltration scores, and patient response data.

Abstract: Described herein are methods and computer systems for classification of CD8 T-cell topology using artificial intelligence and machine learning. A plurality of histology images of tissue samples in a plurality of patients are received by a computer system. An image analysis of the plurality of histology images is performed to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of histology images. A machine learning algorithm is then trained using results of the image analysis and the CD8+ T-cell abundance in the tumor parenchyma and stroma. Based on the training, a machine learning feature space comprising a plurality of classifications is generated, and boundaries between the plurality of classifications in the machine learning feature space are identified.

Abstract: The present disclosure provides methods of identifying a subject suitable for an anti-PD-?PD-L1 antagonist therapy comprising measuring assay CD8 localization and PD-L1 expression in a tumor sample obtained from the subject. In some aspects, method further comprises administering (i) an anti-PD-?PD-L1 antagonist therapy or (ii) an anti-PD-?PD-L1 antagonist and anti-CT-LA-4 antagonist combination therapy to a subject identified as having a tumor exhibiting an excluded CD8 localization phenotype, wherein the tumor is PD-L1 negative.

Abstract: Image quality is assessed for a digital image that is a composite of tiles or other image segments, especially focus accuracy for a microscopic pathology sample. An algorithm or combination of algorithms correlated to image quality is applied to pixel data at margins where adjacent image segments overlap and thus contain the same content in separately acquired images. The margins may be edges merged to join the image segments smoothly into a composite image, and typically occur on four sides of the image segments. The two versions of the same image content at each margin are processed by the quality algorithm, producing two assessment values. A sign and difference value are compared with other image segments, including by subsets selected for the orientation of the margins on sides on the image segments. The differences are mapped to displays. Selection criteria determine segments to be re-acquired.

Abstract: Digital image quality is assessed, especially focus accuracy for a microscopic pathology sample image, using quality assessment criteria that differ among zones distinguished by a structural classification process. An image or area is divided into sub-regions such as adjacent pixel blocks. At least one metric or algorithm is applied, such as a Brenner gradient correlated with focus quality or a Laplacian transform correlated with structural difference. Spatial zones are distinguished by associating blocks of pixels that produced comparable values from the metric. The quality results of an objective metric such as the Brenner gradient are graded separately using statistical or pass/fail grading criteria in each spatial zone, independent of other zones. Focus quality across the image is mapped for display and/or used to queue a re-imaging step.

Abstract: Image quality is assessed for a digital image that is a composite of tiles or other image segments, especially focus accuracy for a microscopic pathology sample. An algorithm or combination of algorithms correlated to image quality is applied to pixel data at margins where adjacent image segments overlap and thus contain the same content in separately acquired images. The margins may be edges merged to join the image segments smoothly into a composite image, and typically occur on four sides of the image segments. The two versions of the same image content at each margin are processed by the quality algorithm, producing two assessment values. A sign and difference value are compared with other image segments, including by subsets selected for the orientation of the margins on sides on the image segments. The differences are mapped to displays. Selection criteria determine segments to be re-acquired.