Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma
Abstract Objective Despite 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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-024-01703-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585648472588288 |
|---|---|
| author | Yihan Sun Da Lin Xiangyang Deng Yinlong Zhang |
| author_facet | Yihan Sun Da Lin Xiangyang Deng Yinlong Zhang |
| author_sort | Yihan Sun |
| collection | DOAJ |
| description | Abstract Objective Despite 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. Methods A 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. Results An 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. Conclusions This 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. |
| format | Article |
| id | doaj-art-890b30259aa84857a2c20f1499f9dcdb |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-890b30259aa84857a2c20f1499f9dcdb2025-01-26T12:39:49ZengSpringerDiscover Oncology2730-60112025-01-0116111010.1007/s12672-024-01703-9Machine learning-based prognostic model for patients with anaplastic thyroid carcinomaYihan Sun0Da Lin1Xiangyang Deng2Yinlong Zhang3Department of Thyroid Breast Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityDepartment of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityDepartment of Thyroid Breast Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityAbstract Objective Despite 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. Methods A 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. Results An 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. Conclusions This 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.https://doi.org/10.1007/s12672-024-01703-9Anaplastic thyroid carcinomaTraditional Cox modelRandom survival forestsPrognosisSurvival prediction |
| spellingShingle | Yihan Sun Da Lin Xiangyang Deng Yinlong Zhang Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma Discover Oncology Anaplastic thyroid carcinoma Traditional Cox model Random survival forests Prognosis Survival prediction |
| title | Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma |
| title_full | Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma |
| title_fullStr | Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma |
| title_full_unstemmed | Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma |
| title_short | Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma |
| title_sort | machine learning based prognostic model for patients with anaplastic thyroid carcinoma |
| topic | Anaplastic thyroid carcinoma Traditional Cox model Random survival forests Prognosis Survival prediction |
| url | https://doi.org/10.1007/s12672-024-01703-9 |
| work_keys_str_mv | AT yihansun machinelearningbasedprognosticmodelforpatientswithanaplasticthyroidcarcinoma AT dalin machinelearningbasedprognosticmodelforpatientswithanaplasticthyroidcarcinoma AT xiangyangdeng machinelearningbasedprognosticmodelforpatientswithanaplasticthyroidcarcinoma AT yinlongzhang machinelearningbasedprognosticmodelforpatientswithanaplasticthyroidcarcinoma |