Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study

作者：Yumeng Wang, Xipeng Pan, Huan Lin, Chu Han, Yajun An, Bingjiang Qiu, Zhengyun Feng, Xiaomei Huang, Zeyan Xu, Zhenwei Shi, Xin Chen, Bingbing Li, Li‐Xu Yan, Cheng Lu, Zhenhui Li, Yanfen Cui, Zaiyi Liu, Zhenbing Liu · 发表于：Journal of Translational Medicine · 年份：2022 · DOI：10.1186/s12967-022-03777-x · 被引用次数：19 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、AI in cancer detection

Abstract Background Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. Methods In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V 1 , n = 115; V 2 , n = 116; and V 3 , n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. Results A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%C...