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Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model

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机构： [1]Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan Province, China. [2]Department of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan Province, China. [3]Hyperbaric Oxygen Therapy Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China. [4]Department of Oncology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China. [5]Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China. [6]Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China

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关键词： Deep learning Non-small cell lung cancer Surgery Prognosis Biomarker

摘要：

Currently, there remains a critical need for reliable tools to accurately predict post-surgical outcomes in non-small cell lung cancer (NSCLC) patients in clinical practice. We aimed to develop and validate a deep learning-based model utilizing histopathological slides to accurately predict post-surgical outcomes in NSCLC patients.In this study, we analyzed histopathological slides and comprehensive clinical data from 337 Local-NSCLC patients for model development, and further validated the model using an independent cohort of 554 NSCLC patients from The Cancer Genome Atlas (TCGA) database. Utilizing the advanced Res2Net deep learning architecture, we developed and optimized a novel Surgical Prognosis Prediction Score (Sr-PPS) system.The Sr-PPS model demonstrated significantly enhanced predictive accuracy for both disease-free survival (DFS) and overall survival (OS) in NSCLC patients. Multivariate Cox regression analysis validated Sr-PPS as a robust independent predictor of post-surgical outcomes in NSCLC patients. Patients with low Sr-PPS scores exhibited enhanced anti-tumor immune microenvironment characteristics, characterized by significant upregulation of immune activation pathways (particularly T-cell migration and B-cell receptor signaling), coupled with marked downregulation of oncogenic pathways, including insulin-like growth factor receptor signaling and STAT protein phosphorylation. Further genomic analyses revealed significant associations between Sr-PPS scores and mutations in key oncogenic driver genes, including CTNND2, PRRX1, and ALK.Our deep learning-based Sr-PPS model not only demonstrates robust predictive capability for post-surgical outcomes in NSCLC patients but also elucidates underlying molecular mechanisms, thereby providing a valuable framework for personalized treatment stratification.Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.

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出版当年[2025]版：

大类 | 2 区医学

小类 | 2 区外科

最新[2025]版：

大类 | 2 区医学

小类 | 2 区外科

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出版当年[2024]版：

Q1 SURGERY

最新[2024]版：

Q1 SURGERY

影响因子： 10.3 最新[2024版] 10.4 最新五年平均 10.3 出版当年[2024版] 0 出版当年五年平均 10.3 出版前一年[2024版]

第一作者：

第一作者机构： [1]Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan Province, China.

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通讯作者：

通讯机构： [2]Department of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan Province, China. [6]Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China

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Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model

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