Using Deep Learning to Predict Anti-PD-1 Response in Melanoma and Lung Cancer Patients from Histopathology Images

Overview

Journal Transl Oncol

Specialty Oncology

Date 2020 Oct 31

PMID 33129113

Citations 24

Authors

Jing Hu

Chuanliang Cui

Wenxian Yang

Lihong Huang

Rongshan Yu

Siyang Liu

Yan Kong

Affiliations

Soon will be listed here.

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 melanoma testing samples with 15(65.2%) responders and 23(74.2%) non-responders correctly classified. We also obtained an AUC of 0.645(95% CI: 49.4%-78.4%) for 55 lung cancer samples.

Interpretation: To our knowledge, this is the first study of using deep learning to determine patients' anti-PD-1 response from H&E slides directly. Our CNN model achieved the state-of-the-art performance and has the potential to screen ICB beneficial patients in routine clinical practice.

Citing Articles

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Wang Q, Deng X, Huang P, Ma Q, Zhao L, Feng Y Front Artif Intell. 2025; 7:1452563.

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Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images.

Patkar S, Chen A, Basnet A, Bixby A, Rajendran R, Chernet R NPJ Precis Oncol. 2024; 8(1):280.

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An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.

Comes M, Fucci L, Strippoli S, Bove S, Cazzato G, Colangiuli C J Transl Med. 2024; 22(1):838.

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Yeghaian M, Bodalal Z, Tareco Bucho T, Kurilova I, Blank C, Smit E Immunooncol Technol. 2024; 24:100723.

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