Dataset Information

Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.

ABSTRACT: BACKGROUND:Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images. METHODS:Four hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model. RESULTS:Among 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710. CONCLUSION:Papillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.

SUBMITTER: Liu T

PROVIDER: S-EPMC6429647 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.

Liu Tongtong T Zhou Shichong S Yu Jinhua J Guo Yi Y Wang Yuanyuan Y Zhou Jin J Chang Cai C

Technology in cancer research & treatment 20190101

<h4>Background</h4>Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images.<h4>Methods</h4>Four hundred fi ...[more]

PMID: 30890092

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Project description:This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76-0.93) and 0.84 (0.71-0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74-0.90) and 0.77 (0.61-0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Project description:IntroductionGuidelines for prophylactic dissection in clinical central negative node (cN0) of papillary thyroid carcinoma vary among different countries due to the uncertainty on the benefit of dissection. The Chinese guidelines recommend prophylactic central compartment lymph node dissection (pCLND) under professional technology. Preoperative ultrasound (US) evaluation of central lymph node determines the surgical strategy used. Sensitivity differs significantly when US is conducted by different physicians even in diverse hospitals. In this study, the aim was to explore why the Chinese guidelines were different from the America Thyroid Association (ATA) guidelines through the real-world evidence on the preoperative diagnosis of cN0.MethodsPreoperative US and surgical pathology data for 1,015 patients with PTC attending 13 Grade-A tertiary hospitals in 2017 were collected. A retrospective analysis using US assessment of CLNM was the conducted to explore the benefits of this approach in China. US physicians in our hospital were trained on scanning the thyroid gland and its regional lymph nodes in normalization. Data of 1,776 patients were collected under the same condition from 2012 to 2017, whose ultrasonography was performed by diverse physicians and doctors. Further, data of 339 patients evaluated by the same sonographer and operated by the same surgical team was collected between 2015 and 2017. In this set of data, US combined CT versus US alone was compared. Patients were grouped into metastasis group and non-metastasis group based on postoperative pathological diagnosis of CLNM. Diagnostic efficacy of US was evaluated.ResultsA total of 925 patients who underwent preoperative ultrasonography in central lymph node, including 825 cases who underwent thyroidectomy and central lymph node dissection were included in this study. The sensitivity of ultrasonography in detecting CLNM was 23.18%, with occult metastasis rate of 40.8%. Data for 1,776 patients comprising paired ultrasonic report and pathological report were collected in our hospital, whose physicians underwent standardized training. The sensitivity was 37.58%. Furthermore, specialized evaluation showed high sensitivity in US/CT (84.58%) than US (58.21%) alone.ConclusionAlthough the sensitivity of US could be enhanced by standardized training and combination with CT, the prevalence of low sensitivity of US in real-world multicenter data and the high occult metastasis rate indicated that the Chinese guidelines were based on the current conditions.

Dataset Information

Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.

Publications

Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.

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