Clinical characteristics and ultrasonographic features for predicting central lymph node metastasis in clinically node‐negative papillary thyroid carcinoma without capsule invasion

Xu, Shumao; Yao, Jiejie; Zhou, Wei; Lin, Chen; Zhan, Wei

doi:10.1002/hed.25941

Cited by 40 publications

(27 citation statements)

References 32 publications

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“…Preoperative neck US for cervical LNs plays an important role in deciding the extent of surgical resection, especially for determining the need for LN dissection. Unfortunately, the detection rate of cervical LN metastasis by US is unsatisfactory, especially for central neck LN metastasis (41). In our data, 113 tumors had LN metastasis, however, only 48.7% (55/113) of them were correctly reported as LN positive by US.…”

Section: Ln Metastasis Is One Of the Most Important Clinical Features Associated With Local Recurrence And Distant Metastasis (38)contrasting

confidence: 61%

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

Jiang

Tang

et al. 2020

Thyroid

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Background: Accurate preoperative prediction of cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC) provides a basis for surgical decision-making and the extent of tumor resection. This study aimed to develop and validate an ultrasound radiomics nomogram for the preoperative assessment of LN status. Methods: Data from 147 PTC patients at the Wuhan Tongji Hospital and 90 cases at the Hunan Provincial Tumor Hospital between January 2017 and September 2019 were included in our study. They were grouped as the training and external validation set. Radiomics features were extracted from shear-wave elastography (SWE) images and corresponding B-mode ultrasound (BMUS) images. Then, the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select LN status-related features and construct the SWE and BMUS radiomics score (Rad-score). Multivariate logistic regression was performed using the two radiomics scores together with clinical data, and a nomogram was subsequently developed. The performance of the nomogram was assessed with respect to discrimination, calibration, and clinical usefulness in the training and external validation set. Results: Both the SWE and BMUS Rad-scores were significantly higher in patients with cervical LN metastasis. Multivariate analysis indicated that the SWE Rad-scores, multifocality, and ultrasound (US)-reported LN status were independent risk factors associated with LN status. The radiomics nomogram, which incorporated the three variables, showed good calibration and discrimination in the training set (area under the receiver operator characteristic curve [AUC] 0.851 [CI 0.791-0.912]) and the validation set (AUC 0.832 [CI 0.749-0.916]). The significantly improved net reclassification improvement and index-integrated discrimination improvement demonstrated that SWE radiomics signature was a very useful marker to predict the LN metastasis in PTC. Decision curve analysis indicated that the SWE radiomics nomogram was clinically useful. Furthermore, the nomogram also showed favorable discriminatory efficacy in the US-reported LN-negative (cN0) subgroup (AUC 0.812 [CI 0.745-0.860]). Conclusions: The presented radiomics nomogram, which is based on the SWE radiomics signature, shows a favorable predictive value for LN staging in patients with PTC.

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Section: Ln Metastasis Is One Of the Most Important Clinical Features Associated With Local Recurrence And Distant Metastasis (38)contrasting

confidence: 61%

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

Jiang

Tang

et al. 2020

Thyroid

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“…In the current study, age ≥55 years was a significant independent risk predictor of CLNM, which was consistent with the literature ( 38 ). The diameter was recognized as an independent risk factor for CLNM in patients with PTC ( 10 , 11 , 36 ), and our study got the same result. It may be attributed to the more extensive the tumors, the more aggressive and proliferative.…”

Section: Discussionsupporting

confidence: 80%

A Comparative Analysis of Six Machine Learning Models Based on Ultrasound to Distinguish the Possibility of Central Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma

et al. 2021

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Current approaches to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) have failed to identify patients who would benefit from preventive treatment. Machine learning has offered the opportunity to improve accuracy by comparing the different algorithms. We assessed which machine learning algorithm can best improve CLNM prediction. This retrospective study used routine ultrasound data of 1,364 PTC patients. Six machine learning algorithms were compared to predict the possibility of CLNM. Predictive accuracy was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). The patients were randomly split into the training (70%), validation (15%), and test (15%) data sets. Random forest (RF) led to the best diagnostic model in the test cohort (AUC 0.731 ± 0.036, 95% confidence interval: 0.664–0.791). The diagnostic performance of the RF algorithm was most dependent on the following five top-rank features: extrathyroidal extension (27.597), age (17.275), T stage (15.058), shape (13.474), and multifocality (12.929). In conclusion, this study demonstrated promise for integrating machine learning methods into clinical decision-making processes, though these would need to be tested prospectively.

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“…Zhan et al found that "taller than wide" shape and tumor size were independent risk factors for LNM (22). Besides, the presence of calcification, closer distance between tumor and capsule were also indicators for LNM (23). Although the previously-mentioned US characteristics are encouraging, diagnostic accuracy is greatly influenced by the expertise of the operator.…”

Section: Discussionmentioning

confidence: 99%

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

Tong¹,

Ji²,

Huang³

et al. 2021

Academic Radiology

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Rationale and Objectives: Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decisionmaking 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. Methods: 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. Results: 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. Conclusion: 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.

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Clinical characteristics and ultrasonographic features for predicting central lymph node metastasis in clinically node‐negative papillary thyroid carcinoma without capsule invasion

Cited by 40 publications

References 32 publications

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

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

A Comparative Analysis of Six Machine Learning Models Based on Ultrasound to Distinguish the Possibility of Central Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma

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

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