Risk stratification of papillary thyroid cancers using multidimensional machine learning

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, we first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. The 558 patients collected from June 2013 to November 2020 were allocated to three groups: discovery set (274 patients, 274 FFPE), retrospective test set (166 patients, 166 FFPE) and prospective test set (118 patients, 118 FNA). Proteomic profiling was conducted by formalin-fixed paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system (ARMS). We developed a machine learning model of 17 variables based on multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI 82.9-84.4) and 83.5% (95% CI 82.2-84.2) in the retrospective and prospective test sets, respectively. This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multi-centre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.