Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer

Qingyuan Zheng; Rui Yang; Xinmiao Ni; Song Yang; Panpan Jiao; Jiejun Wu; Lin Xiong; Jingsong Wang; Jun Jian; Zhengyu Jiang; Lei Wang; Zhiyuan Chen; Xiuheng Liu

doi:10.3390/jcm11237081

Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer

J Clin Med. 2022 Nov 29;11(23):7081. doi: 10.3390/jcm11237081.

Authors

Qingyuan Zheng^{1

2}, Rui Yang^{1

2}, Xinmiao Ni^{1

2}, Song Yang^{1

2}, Panpan Jiao^{1

2}, Jiejun Wu^{1

2}, Lin Xiong³, Jingsong Wang^{1

2}, Jun Jian^{1

2}, Zhengyu Jiang^{1

2}, Lei Wang^{1

2}, Zhiyuan Chen^{1

2}, Xiuheng Liu^{1

2}

Affiliations

¹ Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
² Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China.
³ Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China.

Abstract

(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software. We defined four unique TILs variables based on ML to analyze TILs measurements. Pathological slide images from 133 MIBC patients were retrospectively collected as the discovery set to determine the optimal association of ML-read TILs variables with patient survival outcomes. For validation, we evaluated an independent external validation set consisting of 247 MIBC patients. (3) Results: We found that all four TILs variables had significant prognostic associations with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TILs score being associated with better prognosis. Univariate and multivariate Cox regression analyses demonstrated that electronic TILs (eTILs) variables were independently associated with overall survival after adjustment for clinicopathological factors including age, sex, and pathological stage (p < 0.001 for all analyses). Results analyzed in different subgroups showed that the eTILs variable was a strong prognostic factor that was not redundant with pre-existing clinicopathological features (p < 0.05 for all analyses). (4) Conclusion: ML-driven cell classifier-defined TILs variables were robust and independent prognostic factors in two independent cohorts of MIBC patients. eTILs have the potential to identify a subset of high-risk stage II or stage III-IV MIBC patients who might benefit from adjuvant immunotherapy.

Keywords: machine learning; muscle-invasive bladder cancer; prognostic marker; tumor-infiltrating lymphocytes; whole-slide image.

Abstract

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