放射组学在识别甲状腺乳头状癌甲状腺外侵犯和BRAF突变中的准确性：一项系统评价和荟萃分析

Accuracy of Radiomics in the Identification of Extrathyroidal Extension and BRAF Mutations in Papillary Thyroid Carcinoma: A Systematic Review and Meta-analysis.

作者信息

Liu Yan, Xiang Ling, Liu Fang-Yue, Yahya Noorazrul, Chai Jia-Ning, Hamid Hamzaini Abdul, Lu Qiang, Manan Hanani Abdul

机构信息

Department of Radiology and Intervention, Hospital Pakar Kanak-Kanak (UKM Specialist Children's Hospital), Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, 56000, Kuala Lumpur, Malaysia (Y.L., F.Y.L., J.N.C., H.A.H., H.A.M.); Department of Ultrasound, Affiliated Hospital of Pan Zhihua University, Panzhihua, 61700, Sichuan Province, China (Y.L., L.X.); Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 641400, China (Y.L., Q.L.).

Department of Ultrasound, Affiliated Hospital of Pan Zhihua University, Panzhihua, 61700, Sichuan Province, China (Y.L., L.X.).

出版信息

Acad Radiol. 2025 Mar;32(3):1385-1397. doi: 10.1016/j.acra.2024.11.014. Epub 2025 Jan 6.

DOI:10.1016/j.acra.2024.11.014

PMID:39765435

Abstract

RATIONALE AND OBJECTIVES

Extrathyroidal extension (ETE) and BRAF mutation in papillary thyroid cancer (PTC) increase mortality and recurrence risk. Preoperative identification presents considerable challenges. Although radiomics has emerged as a potential tool for identifying ETE and BRAF mutation, systematic evidence supporting its effectiveness remains insufficient. Therefore, this paper aims to determine the effectiveness of radiomics in detecting ETE and BRAF mutations in PTC.

MATERIALS AND METHODS

PubMed, Web of Science, Cochrane, and Embase databases were searched until May 7th, 2024. The Radiomics Quality Score tool assessed bias risk. Subgroup analyses based on radiomics and clinical characteristics were conducted.

RESULTS

Our systematic review included 19 studies, encompassing 5337 PTC cases. Among these, 12 articles focused on ETE and seven articles focused on BRAF mutations. For the identification of ETE in the validation set, the summarized machine learning (ML) models demonstrated 0.80c-index (95%CI: 0.77-0.83), 0.77 sensitivity (95%CI: 0.72-0.81), and 0.78 specificity (95%CI: 0.73-0.82). Radiomics based on ultrasound demonstrated 0.82c-index (95%CI: 0.78-0.86), 0.77 sensitivity (95%CI: 0.68-0.84), and 0.84 specificity (95%CI: 0.75-0.91). For the identification of BRAF mutations in the validation set, the summarized ML models showed 0.80c-index (95%CI: 0.72-0.87), 0.76 sensitivity (95%CI: 0.67-0.84), and 0.88 specificity (95%CI: 0.77-0.94). ML models based on ultrasound-guided radiomics had 0.81c-index (95%CI: 0.74-0.89), 0.79 sensitivity (95%CI: 0.71-0.86), and 0.87 specificity (95%CI: 0.74-0.94).

CONCLUSION

Radiomics in identifying ETE and BRAF mutation have high c-index, sensitivity, and specificity, especially images from ultrasound, demonstrating the potential for diagnosing ETE and BRAF mutations in PTC.

摘要

原理与目的

甲状腺乳头状癌（PTC）中的甲状腺外侵犯（ETE）和BRAF突变会增加死亡率和复发风险。术前识别存在相当大的挑战。尽管影像组学已成为识别ETE和BRAF突变的潜在工具，但支持其有效性的系统证据仍然不足。因此，本文旨在确定影像组学在检测PTC中ETE和BRAF突变方面的有效性。

材料与方法

检索了PubMed、Web of Science、Cochrane和Embase数据库，直至2024年5月7日。使用影像组学质量评分工具评估偏倚风险。基于影像组学和临床特征进行亚组分析。

结果

我们的系统评价纳入了19项研究，涵盖5337例PTC病例。其中，12篇文章聚焦于ETE，7篇文章聚焦于BRAF突变。在验证集中识别ETE时，汇总的机器学习（ML）模型显示c指数为0.80（95%CI：0.77 - 0.83），灵敏度为0.77（95%CI：0.72 - 0.81），特异度为0.78（95%CI：0.73 - 0.82）。基于超声的影像组学显示c指数为0.82（95%CI：0.78 - 0.86），灵敏度为0.77（95%CI：0.68 - 0.84），特异度为0.84（95%CI：0.75 - 0.91）。在验证集中识别BRAF突变时，汇总的ML模型显示c指数为0.80（95%CI：0.72 - 0.87），灵敏度为0.76（95%CI：0.67 - 0.84），特异度为0.88（95%CI：0.77 - 0.94）。基于超声引导影像组学的ML模型c指数为0.81（95%CI：0.74 - 0.89），灵敏度为0.79（95%CI：0.71 - 0.86），特异度为0.87（95%CI：0.74 - 0.94）。