A pathology-based diagnosis and prognosis intelligent system for oral squamous cell carcinoma using semi-supervised learning
Jiaying Zhou, Haoyuan Wu, Xiao-Jing Hong, Yunyi Huang, Bo Jia, Jiabin Lu, Bin Cheng, Meng Xu, Meng Yang, Tong Wu
Abstract
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 multi centers. 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.