Construction and Validation of a Predictive Nomogram Based on Ultrasound for Lymph Node Metastasis of Papillary Thyroid Carcinoma in the Cervical Central Region

Abstract

To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680-0.869) and adequate calibration ( P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast

“…Nomograms, created by combining selected target variables, provide a clinical prediction model in a graphical manner that can be used to predict breast lesion diagnoses and prognoses for individual management. [36][37][38] In this study, the Clin + CUS model nomogram incorporating patient age and information on lesion size, peripheral hyperechoic halo, and BI-RADS category showed only moderate diagnostic performance in both the training and validation cohorts (AUC = 0.851, 0.863, respectively). Therefore, further approach for improving the diagnostic performance of CUS is advocated.…”

“…Although clinical and CUS features of breast lesions can provide some discrimination value, only when they are combined can they be used to accurately assess whole lesions and facilitate precision medicine. Nomograms, created by combining selected target variables, provide a clinical prediction model in a graphical manner that can be used to predict breast lesion diagnoses and prognoses for individual management 36–38 . In this study, the Clin + CUS model nomogram incorporating patient age and information on lesion size, peripheral hyperechoic halo, and BI-RADS category showed only moderate diagnostic performance in both the training and validation cohorts (AUC = 0.851, 0.863, respectively).…”

Prediction of papillary thyroid metastases to the central compartment: proposal of a model taking into consideration other thyroid conditions

“…Shen et al. ( 12 ) identified gender (male), multifocality, maximum lesion diameter, and American College of Radiology Thyroid Imaging Reporting & Data System (ACR TI-RADS) score as independent risk factors for CLNM. Kim et al.…”

“…The study by Mao et al (11) identified age (<45 years old), gender (male), multifocality, tumor size (>1 cm), and thyroid extracapsular invasion as factors that increase the risk of lymph node metastasis in PTC patients. Shen et al (12) identified gender (male), multifocality, maximum lesion diameter, and American College of Radiology Thyroid Imaging Reporting & Data System (ACR TI-RADS) score as independent risk factors for CLNM. Kim et al (13) concluded that Hashimoto's thyroiditis (HT) and PTC tend to occur at the same time and HT is known to increase the risk of PTC, but HT may also attenuate tumor aggressiveness and metastasis.…”