AUTHOR=Ren Xiaofang , Zhang Jiayan , Song Zuhua , Li Qian , Zhang Dan , Li Xiaojiao , Yu Jiayi , Li Zongwen , Wen Youjia , Zeng Dan , Zhang Xiaodi , Tang Zhuoyue 

TITLE=Detection of malignant lesions in cytologically indeterminate thyroid nodules using a dual-layer spectral detector CT-clinical nomogram

JOURNAL=Frontiers in Oncology

VOLUME=14

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1357419

DOI=10.3389/fonc.2024.1357419

ISSN=2234-943X

ABSTRACT=<sec><title>Purpose</title><p>To evaluate the capability of dual-layer detector spectral CT (DLCT) quantitative parameters in conjunction with clinical variables to detect malignant lesions in cytologically indeterminate thyroid nodules (TNs).</p></sec><sec><title>Materials and methods</title><p>Data from 107 patients with cytologically indeterminate TNs who underwent DLCT scans were retrospectively reviewed and randomly divided into training and validation sets (7:3 ratio). DLCT quantitative parameters (iodine concentration (IC), NIC<sub>P</sub> (IC nodule/IC thyroid parenchyma), NIC<sub>A</sub> (IC nodule/IC ipsilateral carotid artery), attenuation on the slope of spectral HU curve and effective atomic number), along with clinical variables, were compared between benign and malignant cohorts through univariate analysis. Multivariable logistic regression analysis was employed to identify independent predictors which were used to construct the clinical model, DLCT model, and combined model. A nomogram was formulated based on optimal performing model, and its performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis. The nomogram was subsequently tested in the validation set.</p></sec><sec><title>Results</title><p>Independent predictors associated with malignant TNs with indeterminate cytology included NIC<sub>P</sub> in the arterial phase, Hashimoto’s Thyroiditis (HT), and BRAF V600E (all <italic>p</italic> &lt; 0.05). The DLCT-clinical nomogram, incorporating the aforementioned variables, exhibited superior performance than the clinical model or DLCT model in both training set (AUC: 0.875 vs 0.792 vs 0.824) and validation set (AUC: 0.874 vs 0.792 vs 0.779). The DLCT-clinical nomogram demonstrated satisfactory calibration and clinical utility in both training set and validation set.</p></sec><sec><title>Conclusion</title><p>The DLCT-clinical nomogram emerges as an effective tool to detect malignant lesions in cytologically indeterminate TNs.</p></sec>