In iScience
A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.
Pan Xipeng, Lin Huan, Han Chu, Feng Zhengyun, Wang Yumeng, Lin Jiatai, Qiu Bingjiang, Yan Lixu, Li Bingbing, Xu Zeyan, Wang Zhizhen, Zhao Ke, Liu Zhenbing, Liang Changhong, Chen Xin, Li Zhenhui, Cui Yanfen, Lu Cheng, Liu Zaiyi
2022-Dec-22
Artificial intelligence, Cancer, Health sciences, Immunology
