使用双模超声放射学改进甲状腺髓样癌的术前诊断
Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics.
摘要(中文译文)
背景：甲状腺髓样癌（MTC）的术前诊断在临床上具有挑战性，因其超声表现与其他甲状腺肿瘤存在重叠。为解决这一问题，我们旨在开发一个多厂商、多模态的影像组学框架，以实现MTC的准确识别，并将其诊断性能与经验丰富的放射科医师进行比较。方法：本回顾性研究纳入了467个经病理证实的甲状腺结节（94个MTC，373个非MTC），这些结节采集自多个超声平台。数据集被随机划分为训练集（80%）和内部测试集（20%）。共从灰阶和彩色多普勒图像中提取2250个影像组学特征，随后进行Z-score标准化以减轻批次效应。采用稳健的特征选择策略（LASSO和递归特征消除）确定最佳特征标签，用于开发机器学习分类器（SVM、LR、RF）。最优模型进一步在一个独立的平衡队列（n=60；包括MTC、乳头状癌、滤泡状癌、滤泡状腺瘤和结节性甲状腺肿各12例）上进行验证，并在七项分类任务中与经验丰富的放射科医师进行比较。结果：RF模型在区分MTC与乳头状癌方面达到AUC 0.993。LR模型在从所有其他结节中识别MTC方面显示AUC 0.991。在独立验证队列中，模型保持了优越的判别能力，与放射科医师的图像判读相比表现出更好的诊断性能（AUC 0.993 vs. 0.488，P < 0.001）。结论：所提出的多厂商、多模态影像组学系统在MTC的诊断和分层中表现出良好的判别能力。通过整合灰阶和多普勒超声特征并克服扫描仪变异性，该模型显示出作为无创辅助工具的潜力。
Abstract
: Preoperative diagnosis of medullary thyroid carcinoma (MTC) is clinically challenging due to sonographic overlap with other thyroid tumors. To address this, we aimed to develop a multi-vendor, multimodal radiomic framework for accurate MTC identification, comparing its diagnostic performance with that of experienced radiologists. : This retrospective study included 467 pathologically confirmed thyroid nodules (94 MTCs, 373 non-MTCs) acquired across multiple ultrasound platforms. The dataset was randomly partitioned into training (80%) and internal testing (20%) sets. In total, 2250 radiomic features were extracted from grayscale and color Doppler images, followed by Z-score normalization to mitigate batch effects. A robust feature selection strategy (LASSO and recursive feature elimination) identified optimal signatures for developing machine learning classifiers (SVM, LR, RF). The optimal model was further validated on an independent, balanced cohort ( = 60; comprising 12 cases each of MTC, papillary carcinoma, follicular carcinoma, follicular adenoma, and nodular goiter) and compared with experienced radiologists across seven classification tasks. : The RF model achieved an AUC of 0.993 in distinguishing MTC from papillary carcinoma. The LR model showed an AUC of 0.991 for identifying MTC from all other nodules. In the independent validation cohort, the models maintained superior discriminatory ability, showing better diagnostic performance compared to the image interpretation by radiologists (AUC 0.993 vs. 0.488, < 0.001). : The proposed multi-vendor, multimodal radiomic system demonstrated good discriminative ability in the diagnosis and stratification of MTC. By integrating grayscale and Doppler ultrasound features while overcoming scanner variability, this model shows potential as a non-invasive adjunctive tool.
如何引用
AMA
Luying Gao, Naishi Li, Yu Xia, Liyuan Ma, Yuang An, Jiang Ji, et al. Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics.. Cancers. 2026; doi:10.3390/cancers18111738.
APA
Luying Gao, Naishi Li, Yu Xia, Liyuan Ma, Yuang An, Jiang Ji, et al (2026). Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics.. Cancers. https://doi.org/10.3390/cancers18111738
GB/T 7714
Luying Gao, Naishi Li, Yu Xia, Liyuan Ma, Yuang An, Jiang Ji, et al. Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics.[J]. Cancers, 2026 doi:10.3390/cancers18111738.
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