An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

Abstract

This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808-0.824) in the primary cohort and 0.858 (95% CI, 0.849-0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785-0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.

Predictive Factor of Large‐Volume Central Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients Underwent Total Thyroidectomy

“…In presence studies, radiomics and deep machinery learning are gradually conducted to investigate the CLNM. The highest sensitivity has reach 0.858 in the existing predictive models of CLNM, which is significantly higher than the current situation (lower than 0.5 for predicting CLNM) ( 27 ). It may provide more dependable evidence for performing central lymph node dissection.…”

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

“…Lateral LN metastasis exhibits a higher recurrence rate and a poorer prognosis than central LN metastasis [ 63 , 64 ]. A recent study developed an ultrasound-based radiomics nomogram to assess its predictive value for central neck lymph node metastasis in PTC patients [ 65 ]. The prediction model showed good accuracy, sensitivity, specificity, and AUC values in both the training dataset and validation dataset.…”

A Nomogram Predicting Central Lymph Node Metastasis for Patients with Papillary Thyroid Cancer

“…Reviewing similar works on predicting CLNM 13,26,27,35 , our study had a partial difference and takes it a step further. To our knowledge, compared with other works, our data indicated that HT was one of the independent risk factors in promoting CLNM which deserved further evaluation.…”

“…Neither multifocality nor BRAF V600E mutation was analyzed to be associated with CLNM which was believed to be the frequent risk factors in lymph node metastasis. These differences were potential due to that approximately 15% of patients did not undergo ne-needle aspiration (FNA) or BRAF V600E testing before the surgery which was a common limitation in recent studies 26,27 . Besides, a relatively smaller sample size compared with other large cohort studies also contributed to this difference.…”

“…There were only 747 patients enrolled in this study which were still not large enough for the deepening predicting of the clinical risk factors in CLNM. Secondly, body mass index (BMI), ultrasound signatures, and some laboratory testing results which were determined to be associated with CLNM in PTC patients 26,27 were not included in our study. Lastly, there were only four variables nally added to the nomogram, which indicated there might be potential variables waiting to be discovered that could make our nomogram complete and more reliable.…”