Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients. In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets. The radiomic signature was significantly associated with the lateral LNM in both cohorts (p< 0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application. The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients.

Tong Y ，Li J ，Huang Y ，Zhou J ，Liu T ，Guo Y ，Yu J ，Zhou S ，Wang Y ，Chang C ... -  《-》

Development and Validation of Clinical-Radiomics Nomogram for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Feng JW ，Liu SQ ，Qi GF ，Ye J ，Hong LZ ，Wu WX ，Jiang Y ... -  《-》

Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study.

An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Tong Y ，Zhang J ，Wei Y ，Yu J ，Zhan W ，Xia H ，Zhou S ，Wang Y ，Chang C ... -  《BMC MEDICAL IMAGING》

Nomogram Based on Shear-Wave Elastography Radiomics Can Improve Preoperative Cervical Lymph Node Staging for Papillary Thyroid Carcinoma.

Jiang M ，Li C ，Tang S ，Lv W ，Yi A ，Wang B ，Yu S ，Cui X ，Dietrich CF ... -  《-》

Radiomics from Primary Tumor on Dual-Energy CT Derived Iodine Maps can Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.

To develop and validate 2 iodine maps based radiomics nomograms for preoperatively predicting cervical lymph node metastasis (LNM) and central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC). A total of 346 patients with PTC were enrolled and allocated to training (242) and validation (104) sets. Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Aggregated machine-learning strategy was applied for features selection and construction of 2 radiomics scores (LN rad-score; CLN rad-score). Logistic regression model was employed to establish two radiomics nomograms (nomogram 1: predicting LNM; nomogram 2: predicting CLNM) after incorporating LN or CLN rad-score with clinical predictors. Nomograms performance was determined by discrimination, calibration and clinical usefulness. Nomogram 1 incorporated LN rad-score, age (categorized by 55) and CT reported LN status; Nomogram 2 incorporated CLN rad-score, capsule contact >25% and CT reported CLN status. 2 nomograms both showed good discrimination and calibration in the training (AUC = 0.847; AUC = 0.837) and validation cohorts (AUC = 0.807; AUC = 0.795). Significant improved AUC, net reclassification index (NRI) and integrated discriminatory improvement (IDI) confirmed additional great predictive value of 2 rad-scores, compared with clinical models without radiomics. Decision curve analysis indicated clinical utility of nomograms. 2 nomograms both demonstrated favorable predictive efficacy in CT reported LN or CLN negative subgroup (AUC = 0.766; AUC = 0.744). The presented 2 radiomics nomograms are useful tools for preoperative prediction of LNM and CLNM in PTC.

Zhou Y ，Su GY ，Hu H ，Tao XW ，Ge YQ ，Si Y ，Shen MP ，Xu XQ ，Wu FY ... -  《-》