Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

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

doi:10.3390/ijms24032746

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Int J Mol Sci. 2023 Feb 1;24(3):2746. doi: 10.3390/ijms24032746.

Authors

Qingyuan Zheng^{1

2}, Zhengyu Jiang^{1

2}, Xinmiao Ni^{1

2}, Song Yang^{1

2}, Panpan Jiao^{1

2}, Jiejun Wu^{1

2}, Lin Xiong³, Jingping Yuan³, Jingsong Wang^{1

2}, Jun Jian^{1

2}, Lei Wang^{1

2}, Rui Yang^{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

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.

Keywords: machine learning; muscle-invasive bladder cancer; prognosis prediction; tumor-stroma ratio; whole slide image.

MeSH terms

Humans
Machine Learning
Multivariate Analysis
Muscles
Retrospective Studies
Urinary Bladder Neoplasms*

Abstract

MeSH terms

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