Radiomics analysis of dual-energy CT-derived iodine maps for differentiating malignant from benign thyroid nodules

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2024-11-12 DOI:10.1002/mp.17510

Ni Liu, Zengfa Huang, Jun Chen, Yang Yang, Zuoqin Li, Yuanzhi Liu, Yuanliang Xie, Xiang Wang

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引用次数: 0

Abstract

Background

Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual-energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking.

Purpose

To determine the ability of an iodine map-based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules.

Methods

A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad-score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed.

Results

According to the clinical model, age (0.989 [0.984, 0.995]; p < 0.001), NIC (0.778 [0.640, 0.995]; p = 0.01), and cyst changes (0.617 [0.507, 0.751]; p < 0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p = 0.01), NIC (0.797 [0.674, 0.941]; p = 0.008), cyst changes (0.786 [0.653, 0.947]; p = 0.01), and the rad-score (1.106 [1.070, 1.143]; p < 0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96 vs. 0.87; p = 0.01) and test (AUC, 0.90 vs. 0.79; p = 0.04) cohorts than did the clinical model.

Conclusions

The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.

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用于区分恶性和良性甲状腺结节的双能 CT 导出碘图的放射组学分析。

背景：随着敏感成像技术的广泛应用，许多甲状腺结节被偶然发现；然而，这些结节中只有一小部分是恶性的，从而导致不必要的医疗支出和焦虑。如何区分良性甲状腺结节和恶性结节是一大挑战。双能计算机断层扫描（DECT）和放射组学的应用提供了一种新的诊断方法。目的：确定基于碘图的放射组学提名图在静脉期区分恶性甲状腺结节和良性结节的能力：2018年1月至2019年1月期间，共有141名接受DECT检查的甲状腺结节患者入组并随机分配到训练组和测试组。放射学评分（Rad-score）由碘图的九个定量特征得出。逐步逻辑回归分析用于建立放射学、临床和综合模型。年龄、归一化碘浓度（NIC）和囊肿变化用于构建临床模型。通过接收者操作特征（ROC）曲线分析、灵敏度和特异性来分析模型预测恶性甲状腺结节的能力。校准分析用于测试模型的适配性。此外，还进行了决策曲线分析（DCA）和提名图构建：根据临床模型，年龄（0.989 [0.984, 0.995]；P基于碘图的放射组学提名图有助于区分恶性甲状腺结节和良性甲状腺结节。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.