ArticleDiscover oncology2025
Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Nomogram for survival prediction in metastatic differentiated thyroid cancer and survival comparison between systemic chemotherapy and local radiotherapy in high-risk patients.World journal of surgical oncology · 2025Article
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4 authors.
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Abstract
objectiveDespite the identification of various prognostic factors for anaplastic thyroid carcinoma (ATC) patients over the years, a precise prognostic tool for these patients is still lacking. This study aimed to develop and validate a prognostic model for predicting survival outcomes for ATC patients using random survival forests (RSF), a machine learning algorithm.
methodsA total of 1222 ATC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training set of 855 patients and a validation set of 367 patients. We developed an RSF model and a traditional Cox model using the training cohort and further compared their performance based on calibration and discrimination. integrated brier score (iBS) was used to estimate the calibration ability. The Brier score, C-index value, the receiver operating characteristic (ROC) curve with the area under the curve (AUC) and Decision Curves Analysis (DCA) were evaluated. Furthermore, we assessed the feature importance within the RSF model and validated its performance using the validation group.
resultsAn RSF model and a traditional Cox model were successfully developed in training set. The Brier score for the RSF model was 0.055, which is lower than the Cox model's score of 0.063, indicating better performance since a lower Brier score signifies superior model accuracy. The RSF model exceeded the Cox model in performance based on the C-index and AUC. Additionally, the DCA curve indicated that the RSF model provided substantial clinical benefit. And we further ranked the time-dependent features according to their permutation importance and observed that surgery, radiotherapy, and chemotherapy were the most influential predictors initially. Moreover, according to the RSF model predictions, the ATC patients were successfully stratified into 2 prognostic groups displaying significant difference in survival.
conclusionsThis prognostic study first revealed that RSF offers more precise overall survival predictions and superior prognostic stratification compared to the Cox regression model for ATC patients.
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