Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

Xipeng Pan; Huan Lin; Chu Han; Zhengyun Feng; Yumeng Wang; Jiatai Lin; Bingjiang Qiu; Lixu Yan; Bingbing Li; Zeyan Xu; Zhizhen Wang; Ke Zhao; Zhenbing Liu; Changhong Liang; Xin Chen; Zhenhui Li; Yanfen Cui; Cheng Lu; Zaiyi Liu

doi:10.1016/j.isci.2022.105605

Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

iScience. 2022 Nov 16;25(12):105605. doi: 10.1016/j.isci.2022.105605. eCollection 2022 Dec 22.

Authors

Xipeng Pan^{1

2

3

4}, Huan Lin^{1

3

5}, Chu Han^{1

3}, Zhengyun Feng⁴, Yumeng Wang⁴, Jiatai Lin^{1

3}, Bingjiang Qiu^{1

2

3}, Lixu Yan⁶, Bingbing Li⁷, Zeyan Xu^{1

3

5}, Zhizhen Wang⁴, Ke Zhao^{1

2

3}, Zhenbing Liu⁴, Changhong Liang^{1

3}, Xin Chen⁸, Zhenhui Li^{2

3

9}, Yanfen Cui^{1

2

3

10}, Cheng Lu^{1

3}, Zaiyi Liu^{1

3}

Affiliations

¹ Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China.
² Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China.
³ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
⁴ School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
⁵ School of Medicine, South China University of Technology, Guangzhou 510006, China.
⁶ Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
⁷ Department of Pathology, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), 49 Dagong Road, Ganzhou 341000, China.
⁸ Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China.
⁹ Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China.
¹⁰ Department of Radiology, 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.

Abstract

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.

Keywords: Artificial intelligence; Cancer; Health sciences; Immunology.