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

Lisha Lai, Qianwen Guan, Yingying Liang, Junwei Chen, Yuting Liao, XU Hong-gang, Xinhua Wei

2021Acta Radiologica10 citationsDOI

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.

Topics & Concepts

NomogramMedicineReceiver operating characteristicStage (stratigraphy)RadiologyRadiomicsThyroid carcinomaConfidence intervalLymph nodeT-stageNuclear medicineOncologyCancerThyroidInternal medicinePaleontologyBiologyThyroid Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingRenal cell carcinoma treatment