Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis
Purpose: To identify key clinical risk factors affecting therapeutic outcomes in relapsed primary central nervous system lymphoma (r-PCNSL) patients undergoing stereotactic radiosurgery salvage therapy (SRS-ST) and develop a decision tree-based predictive model. Patients and Methods: A retrospective...
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Elsevier
2025-10-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S193652332500213X |
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| author | Huili Zhao Shenao Zhang Lang Chen Xin Liu Aihong Cao Peng Du |
| author_facet | Huili Zhao Shenao Zhang Lang Chen Xin Liu Aihong Cao Peng Du |
| author_sort | Huili Zhao |
| collection | DOAJ |
| description | Purpose: To identify key clinical risk factors affecting therapeutic outcomes in relapsed primary central nervous system lymphoma (r-PCNSL) patients undergoing stereotactic radiosurgery salvage therapy (SRS-ST) and develop a decision tree-based predictive model. Patients and Methods: A retrospective analysis was performed on r-PCNSL patients undergoing SRS-ST at The Second Affiliated Hospital of Xuzhou Medical University between January 2012 and November 2021. The cohort was randomly divided into training and validation sets (7:3 ratio). The C5.0 algorithm was employed to develop a decision tree model for predicting treatment response. Model performance was evaluated using diagnostic metrics including accuracy (ACC), sensitivity, and specificity. Results: A cohort of 209 patients meeting inclusion/exclusion criteria were enrolled. Survival analysis revealed a mean progression-free survival (PFS) of 7.5 ± 2.6 months and overall survival (OS) of 13.8 ± 4.1 months. Using multivariate analysis, a decision tree model was developed incorporating three critical prognostic parameters: Karnofsky Performance Status (KPS); deep brain structure involvement; and International Extranodal Lymphoma Study Group (IELSG) score. The model demonstrated robust predictive accuracy, with sensitivities of 0.880-1.000 in the training set versus 0.667-0.880 in the validation set, and corresponding specificities of 0.926-1.000 and 0.854-0.984, respectively. Conclusions: Our analysis identified critical determinants of therapeutic response in r-PCNSL patients receiving SRS-ST, developing a clinically applicable decision tree model to guide hematologists and neuro-oncologists in personalizing treatment approaches. |
| format | Article |
| id | doaj-art-6d71e95ced7e419192cc2c7f8db39fa6 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-6d71e95ced7e419192cc2c7f8db39fa62025-08-20T03:58:36ZengElsevierTranslational Oncology1936-52332025-10-016010248210.1016/j.tranon.2025.102482Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysisHuili Zhao0Shenao Zhang1Lang Chen2Xin Liu3Aihong Cao4Peng Du5Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China; Department of Radiology, Xinyi People's Hospital, Xuzhou 221400, ChinaDepartment of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaDepartment of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, China; Corresponding authors.Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China; Corresponding authors.Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China; Corresponding authors.Purpose: To identify key clinical risk factors affecting therapeutic outcomes in relapsed primary central nervous system lymphoma (r-PCNSL) patients undergoing stereotactic radiosurgery salvage therapy (SRS-ST) and develop a decision tree-based predictive model. Patients and Methods: A retrospective analysis was performed on r-PCNSL patients undergoing SRS-ST at The Second Affiliated Hospital of Xuzhou Medical University between January 2012 and November 2021. The cohort was randomly divided into training and validation sets (7:3 ratio). The C5.0 algorithm was employed to develop a decision tree model for predicting treatment response. Model performance was evaluated using diagnostic metrics including accuracy (ACC), sensitivity, and specificity. Results: A cohort of 209 patients meeting inclusion/exclusion criteria were enrolled. Survival analysis revealed a mean progression-free survival (PFS) of 7.5 ± 2.6 months and overall survival (OS) of 13.8 ± 4.1 months. Using multivariate analysis, a decision tree model was developed incorporating three critical prognostic parameters: Karnofsky Performance Status (KPS); deep brain structure involvement; and International Extranodal Lymphoma Study Group (IELSG) score. The model demonstrated robust predictive accuracy, with sensitivities of 0.880-1.000 in the training set versus 0.667-0.880 in the validation set, and corresponding specificities of 0.926-1.000 and 0.854-0.984, respectively. Conclusions: Our analysis identified critical determinants of therapeutic response in r-PCNSL patients receiving SRS-ST, developing a clinically applicable decision tree model to guide hematologists and neuro-oncologists in personalizing treatment approaches.http://www.sciencedirect.com/science/article/pii/S193652332500213XDecision treeTherapeutic efficacyRelapsed primary central nervous system lymphomaStereotactic radiosurgerySalvage therapy |
| spellingShingle | Huili Zhao Shenao Zhang Lang Chen Xin Liu Aihong Cao Peng Du Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis Translational Oncology Decision tree Therapeutic efficacy Relapsed primary central nervous system lymphoma Stereotactic radiosurgery Salvage therapy |
| title | Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis |
| title_full | Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis |
| title_fullStr | Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis |
| title_full_unstemmed | Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis |
| title_short | Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis |
| title_sort | prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma a machine learning driven decision tree analysis |
| topic | Decision tree Therapeutic efficacy Relapsed primary central nervous system lymphoma Stereotactic radiosurgery Salvage therapy |
| url | http://www.sciencedirect.com/science/article/pii/S193652332500213X |
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