苏木精-伊红（H&E）染色图像上计算机化三级淋巴结构密度是可切除肺腺癌的一种预后生物标志物。

Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma.

作者信息

Wang Yumeng, Lin Huan, Yao Ningning, Chen Xiaobo, Qiu Bingjiang, Cui Yanfen, Liu Yu, Li Bingbing, Han Chu, Li Zhenhui, Zhao Wei, Wang Zimin, Pan Xipeng, Lu Cheng, Liu Jun, Liu Zhenbing, Liu Zaiyi

机构信息

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.

出版信息

iScience. 2023 Aug 16;26(9):107635. doi: 10.1016/j.isci.2023.107635. eCollection 2023 Sep 15.

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.

三级淋巴结构（TLSs）数量增加与肺腺癌（LUAD）患者的良好预后相关。然而，手动评估TLSs是一个依赖经验且耗时的过程，这限制了其临床应用。在这项多中心研究中，我们开发了一种自动化计算流程，用于量化常规苏木精和伊红（H&E）染色的全切片图像（WSIs）肿瘤区域中的TLS密度。在三个队列的802例可切除LUAD患者中，进一步探讨了计算机化TLS密度与无病生存期（DFS）之间的关联。此外，建立了一个纳入临床病理变量和TLS密度的Cox比例风险回归模型，以评估其预后能力。计算机化TLS密度是可切除LUAD患者的独立预后生物标志物。将TLS密度与临床病理变量相结合，可以通过改善预后分层来支持个体化临床决策。