首页|Xi'an Jiaotong University Reports Findings in Thyroid Cancer (Predicting overall survival in anaplastic thyroid cancer using machine learning approaches)
Xi'an Jiaotong University Reports Findings in Thyroid Cancer (Predicting overall survival in anaplastic thyroid cancer using machine learning approaches)
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New research on Oncology-Thyroid Can cer is the subject of a report. According to news reporting from Xi'an, People's Republic of China, by NewsRx journalists, research stated, "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." The news correspondents obtained a quote from the research from Xi'an Jiaotong U niversity, "This study aimed to develop and validate ML models using the SEER da tabase to predict 3-month, 6-month, and 12-month (overall survival) OS in ATC pa tients. Clinical and demographic data for patients with ATC from the SEER databa se (2004-2015) were utilized. Five ML algorithms-AdaBoost, support vector machin es, gradient boosting classifiers, random forests, and naive Bayes-were evaluate d. The data were split into training and testing sets (7:3 ratio), and the model s were tuned using fivefold cross-validation. Model performance was assessed usi ng the concordance index (C-index) and Brier score, with 95% confi dence intervals reported. The gradient boosting model achieved the greatest perf ormance for 3-month survival (C-index: 0.8197, 95% CI 0.7682-0.868 9; Brier score: 0.1802), and the AdaBoost model achieved the greatest performanc e in 6-month survival (C-index: 0.8473, 95% CI 0.7979-0.8933; Brie r score: 0.1775). The SVC model showed superior performance for 12-month surviva l (C-index: 0.8347, 95% CI 0.7866- 0.8816; Brier score: 0.1476). Us ing 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 survi val, with smaller tumors generally linked to better outcomes. ML algorithms can accurately predict short-term survival in ATC patients."
Xi'anPeople's Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOncologyThyroid CancerThyroid Neoplasms
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