ArticleEuropean 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 Surgery2024
Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study.
Article in 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, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Machine learning for predicting muscle loss after radiotherapy using clinical and toxicity data in oral cavity cancer.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 · 2026Article
- Machine learning approaches for predicting differentiated thyroid cancer recurrence using thyroglobulin levels and whole-body scans: a retrospective cohort study.Scientific reports · 2026Article
- Machine learning prediction of thyroid cancer recurrence for early screening and clinical decision pathways: a retrospective cohort study.Discover oncology · 2026Article
- Instability of LLM text embeddings for unsupervised dimension reduction of tabular data.Frontiers in bioinformatics · 2026Article
- A comparative study on advanced predictive modeling of thyroid cancer recurrence using multi algorithmic machine learning frameworks.Scientific reports · 2025Article
- CatBoost with physics-based metaheuristics for thyroid cancer recurrence prediction.BioData mining · 2025Article
- Optimizing high dimensional data classification with a hybrid AI driven feature selection framework and machine learning schema.Scientific reports · 2025Article
- Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.BMC medical informatics and decision making · 2025Article
- Article
- Erosion of the temporal bone by vestibular schwannoma: morphometrics and predictive modeling.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 · 2025Article
- Thy-DAMP: deep artificial neural network model for prediction of thyroid cancer mortality.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 · 2025Article
- Integrating traditional omics and AI-driven approaches for discovery and validation of novel MicroRNA biomarkers and therapeutic targets in thyroid cancer.Frontiers in pharmacology · 2025Article
- Article
- Development and validation of machine learning models for predicting lung metastasis risk in differentiated thyroid cancer based on two databases.Gland surgery · 2024Article
- Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach.Internal and emergency medicine · 2024Article
- Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.BMC oral health · 2024Article
- Air Traffic Controller Workload Detection Based on EEG Signals.Sensors (Basel, Switzerland) · 2024Article
- Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.BMC cancer · 2024Article
- Analysis of risk factors for papillary thyroid carcinoma and the association with thyroid function indicators.Frontiers in endocrinology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe objective of this study was to train machine learning models for predicting the likelihood of recurrence in patients diagnosed with well-differentiated thyroid cancer. While thyroid cancer mortality remains low, the risk of recurrence is a significant concern. Identifying individual patient recurrence risk is crucial for guiding subsequent management and follow-ups.
methodsIn this prospective study, a cohort of 383 patients was observed for a minimum duration of 10 years within a 15-year timeframe. Thirteen clinicopathologic features were assessed to predict recurrence potential. Classic (K-nearest neighbors, support vector machines (SVM), tree-based models) and artificial neural networks (ANN) were trained on three distinct combinations of features: a data set with all features excluding American Thyroid Association (ATA) risk score (12 features), another with ATA risk alone, and a third with all features combined (13 features). 283 patients were allocated for the training process, and 100 patients were reserved for the validation of stage.
resultsThe patients' mean age was 40.87 ± 15.13 years, with a majority being female (81%). When using the full data set for training, the models showed the following sensitivity, specificity and AUC, respectively: SVM (99.33%, 97.14%, 99.71), K-nearest neighbors (83%, 97.14%, 98.44), Decision Tree (87%, 100%, 99.35), Random Forest (99.66%, 94.28%, 99.38), ANN (96.6%, 95.71%, 99.64). Eliminating ATA risk data increased models specificity but decreased sensitivity. Conversely, training exclusively on ATA risk data had the opposite effect.
conclusionsMachine learning models, including classical and neural networks, efficiently stratify the risk of recurrence in patients with well-differentiated thyroid cancer. This can aid in tailoring treatment intensity and determining appropriate follow-up intervals.
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