Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma
Yumeng Wang,
Huan Lin,
Ningning Yao,
Xiaobo Chen,
Bingjiang Qiu,
Yanfen Cui,
Yu Liu,
Bingbing Li,
Chu Han,
Zhenhui Li,
Wei Zhao,
Zimin Wang,
Xipeng Pan,
Cheng Lu,
Jun Liu,
Zhenbing Liu,
Zaiyi Liu
Affiliations
Yumeng Wang
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
Huan Lin
Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; School of Medicine, South China University of Technology, Guangzhou 510006, China
Ningning Yao
Department of Radiobiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan 030013, China
Xiaobo Chen
First Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
Bingjiang Qiu
Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Guangdong Cardiovascular Institute, Guangzhou 510080, China
Yanfen Cui
Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Department of Radiobiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan 030013, China; Guangdong Cardiovascular Institute, Guangzhou 510080, China
Yu Liu
Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
Bingbing Li
Department of Pathology, Ganzhou Hospital of Guangdong Provincial People’s Hospital, Ganzhou Municipal Hospital, 49 Dagong Road, Ganzhou 341000, China
Chu Han
Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
Zhenhui Li
Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
Wei Zhao
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China
Zimin Wang
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Xipeng Pan
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Corresponding author
Cheng Lu
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Corresponding author
Jun Liu
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; Corresponding author
Zhenbing Liu
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Corresponding author
Zaiyi Liu
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Corresponding author
Summary: The increased amount of tertiary lymphoid structures (TLSs) is associated with a favorable prognosis in patients with lung adenocarcinoma (LUAD). However, evaluating TLSs manually is an experience-dependent and time-consuming process, which limits its clinical application. In this multi-center study, we developed an automated computational workflow for quantifying the TLS density in the tumor region of routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The association between the computerized TLS density and disease-free survival (DFS) was further explored in 802 patients with resectable LUAD of three cohorts. Additionally, a Cox proportional hazard regression model, incorporating clinicopathological variables and the TLS density, was established to assess its prognostic ability. The computerized TLS density was an independent prognostic biomarker in patients with resectable LUAD. The integration of the TLS density with clinicopathological variables could support individualized clinical decision-making by improving prognostic stratification.
