Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images.

ABSTRACT:

Background

Recent studies showed that immune-checkpoint blockade (ICB) has significantly improved clinical outcomes of melanoma and lung cancer patients. However, only a small subset of patients can benefit from ICB. Deep learning has been successfully implemented in complementary clinical diagnosis. The aim of this study is to demonstrate the potential of deep learning to facilitate the prediction of anti-PD-1 response from H&E images directly.

Methods

In this study, 190 H&E slides of melanoma were segmented into 256 × 256 tiles which were used as the training set for the convolutional neural network (CNN). Additional 54 melanoma and 55 lung cancer H&E slides were collected as independent testing sets.

Findings

An AUC of 0.778(95% CI: 63.8%-90.5%) was achieved for 54

SUBMITTER: Hu J 

PROVIDER: S-EPMC7595938 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature