ArticleJournal of oncology2022
Artificial Neural Network-Based Ultrasound Radiomics Can Predict Large-Volume Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of machine learning and deep learning algorithms for thyroid cancer metastasis: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- Pooled it
- Article
- A radiopathomics model for predicting large-number cervical lymph node metastasis in clinical N0 papillary thyroid carcinoma.European radiology · 2025Article
- 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
- Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis.Frontiers in endocrinology · 2023Article
- Article
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Authors and funding
7 authors.
Funding
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Abstract
Objective: To evaluate the ability of artificial neural network- (ANN-) based ultrasound radiomics to predict large-volume lymph node metastasis (LNM) preoperatively in clinical N0 disease (cN0) papillary thyroid carcinoma (PTC) patients. Methods: From January 2020 to April 2021, 306 cN0 PTC patients admitted to our hospital were retrospectively reviewed and divided into a training ( Results: The radiomic model showed better discrimination than other classifiers for large-volume LNM in the validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.856 and an area under the precision-recall curve (AUPR) of 0.381. The performance of the integrated model was better, with an AUROC of 0.910 and an AUPR of 0.463. According to the calibration curve and decision curve analysis, the radiomic and integrated models had good calibration and clinical usefulness. Moreover, the models had good predictive performance in the PTMC and CPTC subgroups. Conclusion: ANN-based ultrasound radiomics could be a potential tool to predict large-volume LNM preoperatively in cN0 PTC patients.
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Registered trials
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