ArticleFrontiers in oncology2021
A Radiomic Nomogram for the Ultrasound-Based Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Diagnostic Performance of Ultrasonography in the Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2022Pooled it
- Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.Frontiers in endocrinology · 2022Trial
- Diagnostic performance and generalizability of a clinical-ultrasound radiomics model for predicting extrathyroidal extension in thyroid carcinoma: a retrospective study.Annals of medicine · 2026Article
- Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma.Journal of imaging informatics in medicine · 2026Article
- A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics.BMC endocrine disorders · 2026Article
- MRI-based model to predict preoperative extrathyroidal extension in papillary thyroid carcinoma.European radiology · 2025Article
- Assessment of testicular parenchyma in COVID-19 patients: a case-control study using ultrasound histogram analysis.Revista da Associacao Medica Brasileira (1992) · 2025Article
- Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer.Cancers · 2024Article
- The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study.Radiology and oncology · 2024Article
- Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.Quantitative imaging in medicine and surgery · 2024Article
- Using multimodal ultrasound including full-time-series contrast-enhanced ultrasound cines for identifying the nature of thyroid nodules.Frontiers in oncology · 2024Article
- Construction and validation of a diagnostic model for high-risk papillary thyroid microcarcinoma.Frontiers in endocrinology · 2024Article
- Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis.Cancer management and research · 2024Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma.BMC medical imaging · 2023Article
- Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound.Diagnostics (Basel, Switzerland) · 2023Article
- The progress of radiomics in thyroid nodules.Frontiers in oncology · 2023Review
- Nomogram incorporating preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma.Frontiers in oncology · 2023Article
- Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer.Frontiers in oncology · 2023Article
- Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer.Frontiers in oncology · 2023Article
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12 authors.
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Abstract
purposeTo construct a sequence diagram based on radiological and clinical factors for the evaluation of extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC). MATERIALS AND
methodsBetween January 2016 and January 2020, 161 patients with PTC who underwent preoperative ultrasound examination in the Affiliated People's Hospital of Jiangsu University were enrolled in this retrospective study. According to the pathology results, the enrolled patients were divided into a non-ETE group and an ETE group. All patients were randomly divided into a training cohort (n = 97) and a validation cohort (n = 64). A total of 479 image features of lesion areas in ultrasonic images were extracted. The radiomic signature was developed using least absolute shrinkage and selection operator algorithms after feature selection using the minimum redundancy maximum relevance method. The radiomic nomogram model was established by multivariable logistic regression analysis based on the radiomic signature and clinical risk factors. The discrimination, calibration, and clinical usefulness of the nomogram model were evaluated in the training and validation cohorts.
resultsThe radiomic signature consisted of six radiomic features determined in ultrasound images. The radiomic nomogram included the parameters tumor location, radiological ETE diagnosis, and the radiomic signature. Area under the curve (AUC) values confirmed good discrimination of this nomogram in the training cohort [AUC, 0.837; 95% confidence interval (CI), 0.756-0.919] and the validation cohort (AUC, 0.824; 95% CI, 0.723-0.925). The decision curve analysis showed that the radiomic nomogram has good clinical application value.
conclusionThe newly developed radiomic nomogram model is a noninvasive and reliable tool with high accuracy to predict ETE in patients with PTC.
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