Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4.

IF 2.5 4区 医学 Q1 ACOUSTICS Ultrasonic Imaging Pub Date : 2024-11-01 Epub Date: 2024-08-20 DOI:10.1177/01617346241271184
Siru Wu, Linfeng Shu, Zhaoyu Tian, Jiajia Li, Yunfeng Wu, Xiaoxia Lou, Zuohui Wu
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Abstract

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p < .001, 95% CI: 0.952-0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.

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基于多模态超声特征的提名图模型对 C-TIRADS 第 4 类良性和恶性甲状腺结节的预测价值
探讨基于多模态超声特征的提名图模型对C-TIRADS第4类良性和恶性甲状腺结节的预测价值。对2020年4月至2023年4月在遵义医学院附属医院接受甲状腺超声检查和细针穿刺活检(FNA)或甲状腺切除术的患者的一般情况和超声特征进行回顾性分析。通过LASSO回归和多元Logistic回归分析筛选甲状腺C-TIRADS 4类良性结节和恶性结节的预测征象,构建提名图预测模型。通过 ROC 曲线和校正曲线评估了模型的预测效率和准确性。C-TIRADS第4类良性和恶性甲状腺结节预测模型中的七个独立风险因素分别是生长模式、形态、微钙化、SR、动脉期增强强度、初始灌注时间和PE[%]。根据这些特征构建的预测模型的曲线下面积(AUC)为 0.971(P < 0.001,95% CI:0.952-0.989),预测准确率为 93.1%。内部验证表明,提名图校准曲线与实际情况相符,决策曲线分析表明该模型具有较高的临床应用价值。基于C-TIRADS第4类甲状腺结节多模态超声特征构建的提名图预测模型具有较高的临床应用价值。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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