A pathology-based diagnosis and prognosis intelligent system for oral squamous cell carcinoma using semi-supervised learning

• Published in: Expert systems with applications Vol. 254; p. 124242
• Main Authors: Zhou, Jiaying, Wu, Haoyuan, Hong, Xiaojing, Huang, Yunyi, Jia, Bo, Lu, Jiabin, Cheng, Bin, Xu, Meng, Yang, Meng, Wu, Tong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2024
• Subjects: Intelligent system; Oral squamous cell carcinoma; Pathological image recognition; Prognosis prediction; Semi-supervised learning; Whole slide images; Pathological image recognition; Whole slide images; Prognosis prediction; Oral squamous cell carcinoma; Semi-supervised learning; Intelligent system
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124242

Pathological images are important for diagnosis and prognosis of oral squamous cell carcinoma (OSCC). However, it is difficult for pathologists to directly apply intuitive pathological image information to predict prognosis. Applying supervised learning (SL) to whole slide images (WSIs) analysis is labor-consuming and time-costing, and semi-supervised learning (SmSL) has provided a new opportunity to revisit classical approaches in digital pathology. In this study, we designed an intelligent SmSL system based on Self-supervised Pretraining (SP) and Adaptive Threshold (AT), named SPAT_SmSL, for the diagnosis and prognosis of OSCC on multicenters. Firstly, we used the SP technique and AT strategy to fully exploit the unlabeled data, both of which were integrated into the SPAT_SmSL algorithm to recognize tumor, stroma, and tumor-infiltrating lymphocytes (TILs) regions. Secondly, pathological variables including TIL-score and depth of invasion (DOI) were digitally quantified based on the results of image recognition. Finally, multivariable Cox analysis was performed to identify independent prognostic factors affecting overall survival and establish a comprehensive predictive model for OSCC patients. The new SPAT_SmSL paradigm demonstrates superior performance in WSIs recognition and survival prediction, which potentially serves as a novel tool to build an expert digital pathological platform to meet the demand of intelligent diagnosis and prognosis, as well as facilitating clinicians with complementary information for individualized treatment in the future. •An intelligent system for the diagnosis and prognosis of OSCC was designed.•The new SPAT_SmSL paradigm demonstrated superior performance in WSIs recognition.•SPAT_SmSL-based TIL-score exhibited strong predictive ability of overall survival.•SPAT_SmSL driven predictive model achieved great prognostic performance.