Risk stratification of papillary thyroid cancers using multidimensional machine learning

Abstract

Background: 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. Materials and methods: 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 BRAF V600E mutation were detected by the amplification refractory mutation system (ARMS). Results: 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. Conclusion: 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.