Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning
Abstract Introduction Patients with lung adenocarcinoma (LUAD) who develop brain metastases (BM) face significantly poor prognoses. A well-crafted prognostic model could greatly assist clinicians in patient counseling and in devising tailored therapeutic strategies. Methods The study cohort comprise...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01854-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861802980605952 |
|---|---|
| author | Min Liang Zhiwen Zhang Langming Wu Mafeng Chen Shifan Tan Jian Huang |
| author_facet | Min Liang Zhiwen Zhang Langming Wu Mafeng Chen Shifan Tan Jian Huang |
| author_sort | Min Liang |
| collection | DOAJ |
| description | Abstract Introduction Patients with lung adenocarcinoma (LUAD) who develop brain metastases (BM) face significantly poor prognoses. A well-crafted prognostic model could greatly assist clinicians in patient counseling and in devising tailored therapeutic strategies. Methods The study cohort comprised LUAD patients with BM identified from the surveillance, epidemiology, and end results database between 2000 and 2018. We pinpointed independent prognostic features for overall survival (OS) using Lasso regression analyses. Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). Results We extracted a total of 9121 eligible patients from the database, identifying eleven clinical parameters that significantly influenced OS prognostication. The XGBoost model exhibited superior discriminative power, achieving AUC values of 0.829 and 0.827 for 1- and 2-year survival, respectively, in the training cohort, and 0.816 and 0.809 in the validation cohort. In comparison to other models, the XGBoost model excelled in both training and validation phases, as demonstrated by substantial differences in AUC, DCA, calibration, and Brier score. This model has been made accessible via a web-based platform. Conclusions This study has developed an XGBoost-based machine learning model with an accompanying web-based application, providing a novel resource for clinicians to support personalized decision-making and enhance treatment outcomes for LUAD patients with BM. |
| format | Article |
| id | doaj-art-5233de7fe70c4f408d6d32255fcedbbd |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-5233de7fe70c4f408d6d32255fcedbbd2025-02-09T12:43:39ZengSpringerDiscover Oncology2730-60112025-02-0116111610.1007/s12672-025-01854-3Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learningMin Liang0Zhiwen Zhang1Langming Wu2Mafeng Chen3Shifan Tan4Jian Huang5Department of Respiratory and Critical Care Medicine, Maoming People’s HospitalEmergency Department, Maoming People’s HospitalDepartment of Science and Education, Maoming People’s HospitalDepartment of Otolaryngology, Maoming People’s HospitalDepartment of Respiratory and Critical Care Medicine, Maoming People’s HospitalDepartment of Thoracic Surgery, Maoming People’s HospitalAbstract Introduction Patients with lung adenocarcinoma (LUAD) who develop brain metastases (BM) face significantly poor prognoses. A well-crafted prognostic model could greatly assist clinicians in patient counseling and in devising tailored therapeutic strategies. Methods The study cohort comprised LUAD patients with BM identified from the surveillance, epidemiology, and end results database between 2000 and 2018. We pinpointed independent prognostic features for overall survival (OS) using Lasso regression analyses. Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). Results We extracted a total of 9121 eligible patients from the database, identifying eleven clinical parameters that significantly influenced OS prognostication. The XGBoost model exhibited superior discriminative power, achieving AUC values of 0.829 and 0.827 for 1- and 2-year survival, respectively, in the training cohort, and 0.816 and 0.809 in the validation cohort. In comparison to other models, the XGBoost model excelled in both training and validation phases, as demonstrated by substantial differences in AUC, DCA, calibration, and Brier score. This model has been made accessible via a web-based platform. Conclusions This study has developed an XGBoost-based machine learning model with an accompanying web-based application, providing a novel resource for clinicians to support personalized decision-making and enhance treatment outcomes for LUAD patients with BM.https://doi.org/10.1007/s12672-025-01854-3Machine learningPrognosisSurvivalLung adenocarcinomaBrain metastases |
| spellingShingle | Min Liang Zhiwen Zhang Langming Wu Mafeng Chen Shifan Tan Jian Huang Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning Discover Oncology Machine learning Prognosis Survival Lung adenocarcinoma Brain metastases |
| title | Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning |
| title_full | Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning |
| title_fullStr | Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning |
| title_full_unstemmed | Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning |
| title_short | Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning |
| title_sort | evolving prognostic paradigms in lung adenocarcinoma with brain metastases a web based predictive model enhanced by machine learning |
| topic | Machine learning Prognosis Survival Lung adenocarcinoma Brain metastases |
| url | https://doi.org/10.1007/s12672-025-01854-3 |
| work_keys_str_mv | AT minliang evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning AT zhiwenzhang evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning AT langmingwu evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning AT mafengchen evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning AT shifantan evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning AT jianhuang evolvingprognosticparadigmsinlungadenocarcinomawithbrainmetastasesawebbasedpredictivemodelenhancedbymachinelearning |