多模态超声放射组学模型结合临床模型鉴别甲状腺滤泡性腺瘤与癌。

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Qianqian Zhao, Shiyan Guo, Yan Zhang, Jinguang Zhou, Ping Zhou
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引用次数: 0

摘要

目的:本研究旨在建立一种结合对比增强超声(CEUS)和b超(B-US)放射组学特征与临床特征的影像学图,以提高术前对滤泡性甲状腺癌(FTC)和滤泡性甲状腺腺瘤(FTA)的鉴别。准确的术前诊断对于指导适当的治疗策略和减少不必要的干预至关重要。方法:我们回顾性地纳入201例经组织病理学证实的FTC (n = 133)或FTA (n = 68)。从B-US和CEUS图像中提取放射组学特征,然后进行特征选择和机器学习模型开发。对5个模型进行评估,采用曲线下面积(AUC)最高的模型构建放射组学特征。采用具有统计学意义的临床特征建立临床风险模型,该模型在训练组和测试组均优于传统的中国甲状腺影像学报告和数据系统(C-TIRADS)。将放射组学特征和临床风险模型整合到nomogram中,评估其诊断性能、校准和临床效用。结果:与C-TIRADS模型相比,临床风险模型的诊断性能优于C-TIRADS模型,训练组的auc为0.802比0.719,试验组为0.745比0.703。nomogram进一步提高了诊断效能,训练组的auc为0.867 (95% CI, 0.800-0.933),试验组的auc为0.833 (95% CI, 0.729-0.937)。它也证明了良好的校准。决策曲线分析(Decision curve analysis, DCA)也显示该图具有良好的临床应用价值。结论:通过将超声造影和B-US放射组学特征与临床数据相结合,我们建立了一个稳健的nomogram来区分FTC和FTA。与现有方法相比,该模型显示出优越的诊断性能,并有望提高甲状腺结节管理的临床决策。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal ultrasound radiomics model combined with clinical model for differentiating follicular thyroid adenoma from carcinoma.

Objective: This study aimed to develop a nomogram integrating radiomics features derived from contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (B-US) with clinical features to improve preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA). Accurate preoperative diagnosis is critical for guiding appropriate treatment strategies and reducing unnecessary interventions.

Methods: We retrospectively included 201 patients with histopathologically confirmed FTC (n = 133) or FTA (n = 68). Radiomics features were extracted from B-US and CEUS images, followed by feature selection and machine-learning model development. Five models were evaluated, and the one with the highest area under the curve (AUC) was used to construct a radiomics signature. A Clinical Risk model was developed using statistically significant clinical features, which outperformed the conventional Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) in both training and test groups. The radiomics signature and Clinical Risk model were integrated into a nomogram, whose diagnostic performance, calibration and clinical utility were assessed.

Results: The Clinical Risk model achieved superior diagnostic performance compared to the C-TIRADS model, with AUCs of 0.802 vs. 0.719 in the training group and 0.745 vs. 0.703 in the test group. The nomogram further improved diagnostic efficacy, with AUCs of 0.867 (95% CI, 0.800-0.933) in the training group and 0.833 (95% CI, 0.729-0.937) in the test group. It also demonstrated excellent calibration. Decision curve analysis (DCA) also indicated that the nomogram showed good clinical utility.

Conclusion: By combining CEUS and B-US radiomics features with clinical data, we developed a robust nomogram for distinguishing FTC from FTA. The model demonstrated superior diagnostic performance compared to existing methods and holds promise for enhancing clinical decision-making in thyroid nodule management.

Clinical trial number: Not applicable.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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