甲状腺乳头状癌（PTC）是最常见的内分泌恶性肿瘤之一，具有不同的风险级别。然而，PTC的术前风险评估在全球范围内仍然是一个挑战。在此，我们首次报告了一种通过临床指标、免疫指标、遗传特征和蛋白质组学等多维特征的PTC术前风险评估分类器（PRAC-PTC）。2013年6月至2020年11月收集的558名患者被分为三组：测试集（274 名患者，274 FFPE）、回顾性测试集（166 名患者，166 FFPE）和前瞻性测试集（118 名患者，118 FNA）。通过患者的福尔马林固定石蜡包埋 (FFPE) 和细针抽吸 (FNA) 组织进行蛋白质组分析。收集术前临床资料和血液免疫学指标。 BRAFV600E 突变是通过扩增阻滞突变系统 (ARMS) 检测到的。我们根据回顾性队列中 274 名 PTC 患者的多维特征，开发了一个包含 17 个变量的机器学习模型。 PRAC-PTC 在发现组中实现了 0.925 的曲线下面积 (AUC)，并通过对 166 名 PTC 患者的回顾性队列 (0.787 AUC) 和来自两个国家的 118 名 PTC 患者的前瞻性队列 (0.799 AUC) 进行盲法分析进行外部验证。独立的临床中心。同时，在PRAC-PTC的辅助下，临床医生的术前预测风险有效性得到提高，回顾性和前瞻性准确率分别达到84.4%（95% CI 82.9-84.4）和83.5%（95% CI 82.2-84.2）。本研究证明，整合多中心回顾性和前瞻性临床队列中的临床数据、基因突变信息、免疫指标、高通量蛋白质组学和机器学习技术的PRAC-PTC能够有效分层PTC的术前风险并可能减少不必要的手术或过度治疗。版权所有 © 2023 作者。由 Wolters Kluwer Health, Inc. 出版

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.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.