Artificial Intelligence Meets Whole Slide Images: Deep Learning Model Shapes an Immune-Hot Tumor and Guides Precision Therapy in Bladder Cancer

Yiheng Jiang; Shengbo Huang; Xinqing Zhu; Liang Cheng; Wenlong Liu; Qiwei Chen; Deyong Yang

doi:10.1155/2022/8213321

Artificial Intelligence Meets Whole Slide Images: Deep Learning Model Shapes an Immune-Hot Tumor and Guides Precision Therapy in Bladder Cancer

J Oncol. 2022 Sep 19:2022:8213321. doi: 10.1155/2022/8213321. eCollection 2022.

Authors

Yiheng Jiang¹, Shengbo Huang², Xinqing Zhu¹, Liang Cheng³, Wenlong Liu^{1

2}, Qiwei Chen^{1

4}, Deyong Yang¹

Affiliations

¹ Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China.
² School of Information and Communication Engineering, Dalian University of Technology, Dalian, China.
³ Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Lifespan Academic Medical Center, Providence, RI 02903, USA.
⁴ School of Information Science and Technology, Dalian Maritime University, 116000 Dalian City, Liaoning Province, China.

Abstract

Background: To construct and validate a deep learning cluster from whole slide images (WSI) for depicting the immunophenotypes and functional heterogeneity of the tumor microenvironment (TME) in patients with bladder cancer (BLCA) and to explore an artificial intelligence (AI) score to explore the underlying biological pathways in the developed WSI cluster.

Methods: In this study, the WSI cluster was constructed based on a deep learning procedure. Further rerecognition of TME features in pathological images was applied based on a neural network. Then, we integrated the TCGA cohort and several external testing cohorts to explore and validate this novel WSI cluster and a corresponding quantitative indicator, the AI score. Finally, correlations between the AI cluster (AI score) and classical BLCA molecular subtypes, immunophenotypes, functional heterogeneity, and potential therapeutic method in BLCA were assessed.

Results: The WSI cluster was identified associated with clinical survival (P < 0.001) and was proved as an independent predictor (P = 0.031), which could also predict the immunology and the clinical significance of BLCA. Rerecognition of pathological images established a robust 3-year survival prediction model (with an average classification accuracy of 86%, AUC of 0.95) for BLCA patients combining TME features and clinical features. In addition, an AI score was constructed to quantify the underlying logic of the WSI cluster (AUC = 0.838). Finally, we hypothesized that high AI score shapes an immune-hot TME in BLCA. Thus, treatment options including immune checkpoint blockade (ICB), chemotherapy, and ERBB therapy can be used for the treatment of BLCA patients in WSI cluster1 (high AI score subtype).

Conclusions: In general, we showed that deep learning can predict prognosis and may aid in the precision medicine for BLCA directly from H&E histology, which is more economical and efficient.