Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples.

Replicating and identifying large cell neuroblastoma using high-dose intra-tumoral chemotherapy and automated digital analysis.

Purpose: Large cell neuroblastomas (LCN) are frequently seen in recurrent, high-risk neuroblastoma but are rare in primary tumors. LCN, characterized by large nuclei with prominent nucleoli, predict a poor prognosis. We hypothesize that LCN can be created with high-dose intra-tumoral chemotherapy and identified by a digital analysis system.

Integrating RNA expression and visual features for immune infiltrate prediction.

Patient responses to cancer immunotherapy are shaped by their unique genomic landscape and tumor microenvironment. Clinical advances in immunotherapy are changing the treatment landscape by enhancing a patient's immune response to eliminate cancer cells. While this provides potentially beneficial treatment options for many patients, only a minority of these patients respond to immunotherapy.

Computer vision detects subtle histological effects of dutasteride on benign prostate.

Objective: To determine whether a computer vision-based approach applied to haematoxylin and eosin (H&E) prostate biopsy images can distinguish dutasteride-treated tissue from placebo, and identify features associated with degree of responsiveness to 5α-reductase inhibitor (5ARI) therapy.

Subjects And Methods: Our study population comprised 100 treatment-adherent men without prostate cancer assigned to dutasteride or placebo in the REDUCE trial, who had slides available from mandatory year-4 biopsies. Half of the men also provided slides from a year-2 biopsy.

Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images.

Context: Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines.

Aims: We compared two contemporary techniques for achieving a common intermediate goal - epithelial-stromal classification.

Settings And Design: Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images.