Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer
(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 evalu...
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MDPI AG
2022-11-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/23/7081 |
| _version_ | 1797462906936229888 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Qingyuan Zheng |
| collection | DOAJ |
| description | (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 (<i>p</i> < 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 (<i>p</i> < 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 (<i>p</i> < 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. |
| first_indexed | 2024-03-09T17:43:10Z |
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| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T17:43:10Z |
| publishDate | 2022-11-01 |
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| series | Journal of Clinical Medicine |
| spelling | doaj.art-0260bd6fcf5946598c9735e96161b1692023-11-24T11:22:34ZengMDPI AGJournal of Clinical Medicine2077-03832022-11-011123708110.3390/jcm11237081Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder CancerQingyuan Zheng0Rui Yang1Xinmiao Ni2Song Yang3Panpan Jiao4Jiejun Wu5Lin Xiong6Jingsong Wang7Jun Jian8Zhengyu Jiang9Lei Wang10Zhiyuan Chen11Xiuheng Liu12Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China(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 (<i>p</i> < 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 (<i>p</i> < 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 (<i>p</i> < 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.https://www.mdpi.com/2077-0383/11/23/7081tumor-infiltrating lymphocyteswhole-slide imagemachine learningprognostic markermuscle-invasive bladder cancer |
| spellingShingle | 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 Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer Journal of Clinical Medicine tumor-infiltrating lymphocytes whole-slide image machine learning prognostic marker muscle-invasive bladder cancer |
| title | Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer |
| title_full | Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer |
| title_fullStr | Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer |
| title_full_unstemmed | Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer |
| title_short | Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer |
| title_sort | quantitative assessment of tumor infiltrating lymphocytes using machine learning predicts survival in muscle invasive bladder cancer |
| topic | tumor-infiltrating lymphocytes whole-slide image machine learning prognostic marker muscle-invasive bladder cancer |
| url | https://www.mdpi.com/2077-0383/11/23/7081 |
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