Outcome Supervised Deep Learning on Pathological Whole Slide Images for Survival Prediction of Immunotherapy in Non-Small Cell Lung Cancer Patients.

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Abstract

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in non-small cell lung cancer (NSCLC) patients, approximately 60% of patients still suffer from recurrence and metastasis after PD-(L)1 inhibitor treatment. To accurately predict the response to PD-(L)1 inhibitors, we presented a deep learning model using a Vision Transformer (ViT) network based on hematoxylin and eosin (H&E)-stained specimens of NSCLC patients. Two independent cohorts of NSCLC patients receiving PD-(L)1 inhibitors from Shandong Cancer Hospital and Institute and Shandong Provincial Hospital were enrolled for model training and external validation, respectively. Whole slide images (WSIs) of H&E-stained histological specimens were obtained from these patients and patched into 1024×1024 pixels. The patch-level model was trained based on ViT to identify the predictive patches, and patch-level probability distribution was performed. Then, we trained a patient-level survival model based on the ViT-RNN (ViT-Recursive Neural Network) framework and externally validated it in Shandong Provincial Hospital cohort. A total of 291 WSIs of H&E-stained histological specimens from 198 NSCLC patients in Shandong Cancer Hospital and 62 WSIs from 30 NSCLC patients in Shandong Provincial Hospital were included in the model training and validation. The model achieved an accuracy of 88.6% in the internal validation cohort and 81% in the external validation cohort. The survival model also remained a statistically independent predictor of survival from PD-(L)1 inhibitors. In conclusion, the outcome supervised ViT-RNN survival model based on pathological WSIs could be used to predict immunotherapy efficacy in NSCLC patients.Copyright © 2023. Published by Elsevier Inc.