TNM Staging for Adrenocortical Carcinoma using SimCLR: A Deep Learning Approach: SimCLR-Based TNM Staging for Adrenocortical CarcinomaA Comprehensive Deep Learning Approach for Adrenocortical Carcinoma TNM Staging

Traditional TNM staging methods suffer from the drawbacks of surgical and puncture invasion risks, as well as the potential for subjective biases in doctors’ diagnoses. Additionally, current detection methods based on deep learning often necessitate and heavily rely on a substantial quantity of high-quality annotations. To address these challenges, We introduce the simCLR method into the TNM staging task firstly, creating a rapid and accurate TNM staging system with outstanding generalization capabilities. We conducted extensive experiments using a publicly available large dataset to train and validate the performance of our proposed method. In comparison to the baseline methods, our approach achieves higher accuracy and training effectiveness. Specifically, we observed substantial improvements in the classification accuracy rates for T staging (71.06%), N staging (96.89%) and M staging (81.17%), with increases of 10.48%, 4.06% and 4.36%, respectively. By utilizing this model, healthcare professionals can rely on a more accurate prediction tool for TNM staging, which enables them to devise personalized treatment plans and nursing measures for affected patients.