Liu Chunrui, Li Wenxian, Wen Baojie, Xue Haiyan, Zhang Yidan, Wei Shuping, Gong Jinxia, Huang Li, He Jian, Yao Jing, Zhou Zhengyang
Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.
Department of Ultrasound, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Front Endocrinol (Lausanne). 2025 Aug 11;16:1617032. doi: 10.3389/fendo.2025.1617032. eCollection 2025.
Parathyroid carcinoma (PC) and atypical parathyroid tumors (APT), constituting rare endocrine malignancies, demonstrate overlapping clinical-radiological presentations with benign adenomas. This study aimed to investigate the predictive performance of three radiomics-based machine learning models for the identification of PC/APT from solitary parathyroid lesions using ultrasound.
This retrospective diagnostic study analyzed 913 surgically-confirmed parathyroid neoplasms (mean age 54.2 ± 13.7 years; 694 females, 219 male) from Nanjing Drum Tower Hospital (n = 730) and Jinling Hospital (n = 183). The cohort comprised 90 malignant lesions and 823 benign adenomas, divided into training (Hospital I) and external test cohort (Hospital II). A radiomic signature derived from 544 quantitative ultrasound features was developed using three machine learning classifiers: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). The performance of the predictive models was evaluated based on the pathological diagnosis.
The RF-based radiomics model showed excellent diagnostic performance. The AUC of this model (0.933) was higher than that of SVM (0.900, < 0.05) and LR (0.901, < 0.05). The accuracy, precision, recall, and F1-score of RF model in distinguishing PA from APT/PC were 0.940, 0.683, 0.638 and 0.660. The explainable bar chart, heatmap and Shapley Additive exPlanations (SHAP) values were used to explain and visualize the main predictors of the optimal model.
This radiomics framework provides a promising tool to support doctors in the clinical management of parathyroid lesions.
甲状旁腺癌（PC）和非典型甲状旁腺肿瘤（APT）是罕见的内分泌恶性肿瘤，其临床影像学表现与良性腺瘤重叠。本研究旨在探讨三种基于放射组学的机器学习模型在利用超声从孤立性甲状旁腺病变中识别PC/APT的预测性能。
这项回顾性诊断研究分析了来自南京鼓楼医院（n = 730）和金陵医院（n = 183）的913例经手术确诊的甲状旁腺肿瘤（平均年龄54.2±13.7岁；女性694例，男性219例）。该队列包括90例恶性病变和823例良性腺瘤，分为训练组（医院I）和外部测试组（医院II）。使用三种机器学习分类器：随机森林（RF）、支持向量机（SVM）和逻辑回归（LR），从544个定量超声特征中开发了一种放射组学特征。基于病理诊断评估预测模型的性能。
基于RF的放射组学模型显示出优异的诊断性能。该模型的AUC（0.933）高于SVM（0.900，<0.05）和LR（0.901，<0.05）。RF模型在区分PA与APT/PC时的准确率、精确率、召回率和F1分数分别为0.940、0.683、0.638和0.660。使用可解释的柱状图、热图和Shapley加性解释（SHAP）值来解释和可视化最佳模型的主要预测因子。
该放射组学框架为临床医生管理甲状旁腺病变提供了一个有前景的工具。
