Real-Time Elastography: A Web-Based Nomogram Improves the Preoperative Prediction of Central Lymph Node Metastasis in cN0 PTC

Abstract

BackgroundGiven the difficulty of accurately determining the central lymph node metastasis (CLNM) status of patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) before surgery, this study aims to combine real-time elastography (RTE) and conventional ultrasound (US) features with clinical features. The information is combined to construct and verify the nomogram to foresee the risk of CLNM in patients with cN0 PTC and to develop a network-based nomogram.MethodsFrom January 2018 to February 2020, 1,157 consecutive cases of cN0 PTC after thyroidectomy and central compartment neck dissection were retrospectively analyzed. The patients were indiscriminately allocated (2:1) to a training cohort (771 patients) and validation cohort (386 patients). Multivariate logistic regression analysis of US characteristics and clinical information in the training cohort was performed to screen for CLNM risk predictors. RTE data were included to construct prediction model 1 but were excluded when constructing model 2. DeLong’s test was used to select a forecast model with better receiver operator characteristic curve performance to establish a web-based nomogram. The clinical applicability, discrimination, and calibration of the preferable prediction model were assessed.ResultsMultivariate regression analysis showed that age, sex, tumor size, bilateral tumors, the number of tumor contacting surfaces, chronic lymphocytic thyroiditis, and RTE were risk predictors of CLNM in cN0 PTC patients, which constituted prediction model 1. Model 2 included the first six risk predictors. Comparison of the areas under the curves of the two models showed that model 1 had better prediction performance (training set 0.798 vs. 0.733, validation set 0.792 vs. 0.715, p < 0.001) and good discrimination and calibration. RTE contributed significantly to the performance of the prediction model. Decision curve analysis showed that patients could obtain good net benefits with the application of model 1.ConclusionA noninvasive web-based nomogram combining US characteristics and clinical risk factors was developed in the research. RTE could improve the prediction accuracy of the model. The dynamic nomogram has good performance in predicting the probability of CLNM in cN0 PTC patients.

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study

“…In this study, SRR was significantly higher in the CCLNM (+) group than in the CCLNM (-) group, and multivariate analysis showed that it was an independent predictor of occult CCLNM. Our results are similar to those of Huang et al., who reported that elasticity score on SUE was independently associated with CCLNM ( 29 ). They used a semi-quantitative parameter, whereas we preferred to use the quantitative SRR.…”

Drawing as a Way of Knowing: How a Mapping Model Assists Preoperative Evaluation of Patients with Thyroid Carcinoma

“…Another approach taken to improve the detection of metastasis in CC-LN could be the combination of US and elastosonography [ 16 , 17 ]. In recent years, radiologists have focused on the development and validation of radiomic ultrasound nomograms as preoperative assessments of LN status in PTC.…”

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

“…Grayscale ultrasound of PTC reveals the lesion characteristics from a morphological viewpoint, CDFI reveals the characteristics of the blood flow distribution in the lesion area, and elastography provides information on the relative stiffness of the lesion. These imaging tests can be implemented as noninvasive ultrasound examinations (21), and the combined application of these tests may provide more imaging information and indirectly reflect more characteristics associated with tumor growth, invasiveness, and metastasis. To our knowledge, the combination of multimodal ultrasound features of primary PTC tumors and their use to develop a radiomics model to predict CLNM has not been reported.…”