ArticlePloS one2013
A model to discriminate malignant from benign thyroid nodules using artificial neural network.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Pooled it
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Machine intelligence in non-invasive endocrine cancer diagnostics.Nature reviews. Endocrinology · 2022Review
- Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis.International journal of endocrinology · 2022Article
- Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.Medicina (Kaunas, Lithuania) · 2021Article
- Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.European thyroid journal · 2020Review
- Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.Frontiers in oncology · 2020Review
- Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks.Scientific reports · 2019Article
- A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead.European geriatric medicine · 2018Article
- Nodular Thyroid Disease and Thyroid Cancer in the Era of Precision Medicine.European thyroid journal · 2017Review
- Diagnosis of Malignancy in Thyroid Tumors by Multi-Layer Perceptron Neural Networks With Different Batch Learning Algorithms.Global journal of health science · 2015Article
- The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests.BioMed research international · 2015Article
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Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US. MATERIALS AND
methods618 consecutive patients (528 women, 161 men) with 689 thyroid nodules (425 malignant and 264 benign nodules) were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules.
resultsSix sonographic features including shape (Taller than wide, p<0.001), margin (Not Well-circumscribed, p<0.001), echogenicity (Hypoechogenicity, p<0.001), internal composition (Solid, p<0.001), presence of calcifications (Microcalcification, p<0.001) and peripheral halo (Absent, p<0.001) were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AURO = 0.818), the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828.
conclusionANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.