{"title":"Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.","authors":"Arnavaz Hajizadeh Barfejani, Mohammadreza Rostami, Mohammad Rahimi, Hossein Sabori Far, Shahab Gholizadeh, Morteza Behjat, Aidin Tarokhian","doi":"10.1007/s00405-024-08986-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop and validate ML models using the SEER database to predict 3-month, 6-month, and 12-month (overall survival) OS in ATC patients.</p><p><strong>Methods: </strong>Clinical and demographic data for patients with ATC from the SEER database (2004-2015) were utilized. Five ML algorithms-AdaBoost, support vector machines, gradient boosting classifiers, random forests, and naive Bayes-were evaluated. The data were split into training and testing sets (7:3 ratio), and the models were tuned using fivefold cross-validation. Model performance was assessed using the concordance index (C-index) and Brier score, with 95% confidence intervals reported.</p><p><strong>Results: </strong>The gradient boosting model achieved the greatest performance for 3-month survival (C-index: 0.8197, 95% CI 0.7682-0.8689; Brier score: 0.1802), and the AdaBoost model achieved the greatest performance in 6-month survival (C-index: 0.8473, 95% CI 0.7979-0.8933; Brier score: 0.1775). The SVC model showed superior performance for 12-month survival (C-index: 0.8347, 95% CI 0.7866-0.8816; Brier score: 0.1476). Using SHAP with a gradient boosting model, the top five features affecting 6-month OS were identified: surgery, the presence of stage IVC, radiation, chemotherapy, and tumor size. Treatment improved survival, while higher stages reduced survival, with smaller tumors generally linked to better outcomes.</p><p><strong>Conclusion: </strong>ML algorithms can accurately predict short-term survival in ATC patients. These models can potentially guide clinical decision-making and individualized treatment strategies.</p>","PeriodicalId":11952,"journal":{"name":"European Archives of Oto-Rhino-Laryngology","volume":" ","pages":"1653-1657"},"PeriodicalIF":1.9000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Archives of Oto-Rhino-Laryngology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00405-024-08986-2","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/21 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop and validate ML models using the SEER database to predict 3-month, 6-month, and 12-month (overall survival) OS in ATC patients.
Methods: Clinical and demographic data for patients with ATC from the SEER database (2004-2015) were utilized. Five ML algorithms-AdaBoost, support vector machines, gradient boosting classifiers, random forests, and naive Bayes-were evaluated. The data were split into training and testing sets (7:3 ratio), and the models were tuned using fivefold cross-validation. Model performance was assessed using the concordance index (C-index) and Brier score, with 95% confidence intervals reported.
Results: The gradient boosting model achieved the greatest performance for 3-month survival (C-index: 0.8197, 95% CI 0.7682-0.8689; Brier score: 0.1802), and the AdaBoost model achieved the greatest performance in 6-month survival (C-index: 0.8473, 95% CI 0.7979-0.8933; Brier score: 0.1775). The SVC model showed superior performance for 12-month survival (C-index: 0.8347, 95% CI 0.7866-0.8816; Brier score: 0.1476). Using SHAP with a gradient boosting model, the top five features affecting 6-month OS were identified: surgery, the presence of stage IVC, radiation, chemotherapy, and tumor size. Treatment improved survival, while higher stages reduced survival, with smaller tumors generally linked to better outcomes.
Conclusion: ML algorithms can accurately predict short-term survival in ATC patients. These models can potentially guide clinical decision-making and individualized treatment strategies.
期刊介绍:
Official Journal of
European Union of Medical Specialists – ORL Section and Board
Official Journal of Confederation of European Oto-Rhino-Laryngology Head and Neck Surgery
"European Archives of Oto-Rhino-Laryngology" publishes original clinical reports and clinically relevant experimental studies, as well as short communications presenting new results of special interest. With peer review by a respected international editorial board and prompt English-language publication, the journal provides rapid dissemination of information by authors from around the world. This particular feature makes it the journal of choice for readers who want to be informed about the continuing state of the art concerning basic sciences and the diagnosis and management of diseases of the head and neck on an international level.
European Archives of Oto-Rhino-Laryngology was founded in 1864 as "Archiv für Ohrenheilkunde" by A. von Tröltsch, A. Politzer and H. Schwartze.