A computed tomography-based radiomic nomogram for predicting lymph node metastasis in patients with early-stage papillary thyroid carcinoma. (September 2022)

• Record Type: Journal Article
• Authors: Lai, Lisha; Guan, Qianwen; Liang, Yingying; Chen, Junwei; Liao, Yuting; Xu, Honggang; Wei, Xinhua
• Is Part Of: Acta radiologica. Volume 63:Number 9(2022)
• Page Start: 1187
• Page End: 1195
• Publication Date: 2022-09
• Subjects: Radiomics -- papillary thyroid carcinoma -- lymph node -- metastasis; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/02841851211054194
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Accurate assessment of lymph node metastasis (LNM) is important for the selection of the optimal therapeutic strategy in patients with papillary thyroid carcinoma (PTC). Purpose: To develop and validate a radiomics nomogram based on computed tomography (CT) for predicting LNM in patients with early-stage PTC. Material and Methods: A total of 92 patients with pathologically confirmed PTC were divided into a training cohort (n = 64) and validation cohort (n = 28). Radiomic features of the tumor and peritumoral interstitium were extracted from contrast-enhanced CT images. The radiomic signature was constructed and the radiomic score (Rad-score) was calculated. Combined with the Rad-score and independent clinical factors, a radiomic nomogram was constructed and its performance was assessed by receiver operating characteristic (ROC) curves and calibration plots. The comparison of ROC curves was performed with DeLong's test Results: A combined nomogram model of the thyroid tumor and peritumoral interstitium was constructed based on the Rad-score, tumor location, maximum diameter, and T stage, and it had areas under the ROC curve of 0.956 (95% confidence interval [CI] = 0.913–1.000) and 0.876 (95% CI = 0.741–1.000) in the training and validation cohorts, respectively. Decision curve analysis suggested that the combined nomogram model had better clinical usefulness than the other models. Conclusion: A CT-based radiomics nomogram incorporating the radiomic signature and the selected clinical predictors can be a reliable approach to preoperatively predict the LNM status in patients with early-stage PTC, which is helpful for treatment decisions and prognosis.