ArticlePloS one2020
Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 46 citations in OpenAlex.
- Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.Scientific reports · 2023Trial
- Value of TERT promoter mutations for early outcomes in papillary thyroid cancer.Endocrine-related cancer · 2026Article
- EnhancingQuantitative imaging in medicine and surgery · 2026Article
- Hybrid deep feature and machine learning framework for classification of thyroid nodules in ultrasound images.Frontiers in oncology · 2026Article
- Dose-Response Relationship Between BRAF V600E Abundance and Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.Cancers · 2025Article
- Molecular Diagnostics and Personalized Therapeutics in Differentiated Thyroid Carcinoma: A Clinically Oriented Review.Diagnostics (Basel, Switzerland) · 2025Review
- Breaking barriers: noninvasive AI model for BRAFInternational journal of computer assisted radiology and surgery · 2025Article
- Article
- Article
- Development of Personalized Strategies for Precisely Battling Malignant Melanoma.International journal of molecular sciences · 2024Review
- Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.Frontiers in endocrinology · 2024Article
- Differential expression of zinc finger CCHC-type superfamily proteins in thyroid carcinoma and their associations with tumor immunity.Thyroid research · 2023Article
- 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
- Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas.Animals : an open access journal from MDPI · 2023Article
- The Presence of Typical "BRAFV600E-Like" Atypia in Papillary Thyroid Carcinoma is Highly Specific for the Presence of the BRAFV600E Mutation.Endocrine pathology · 2023Article
- TERT Promoter and BRAF V600E Mutations in Papillary Thyroid Cancer: A Single-Institution Experience in Korea.Cancers · 2022Article
- Review
- Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules.The Journal of international medical research · 2022Article
- Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFFrontiers in endocrinology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer.
methods469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves.
resultsIn this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004).
conclusionDeep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.
Indexed as
Identifiers
What OpenQuestion holds
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.