Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis
BackgroundAccurate preoperative identification of intracranial glioblastoma (GB), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) is crucial for determining the appropriate treatment strategy.PurposeWe aimed to develop and validate the utility of preoperative magnetic reso...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1541350/full |
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| author | Yuxiang Sun Yuxiang Sun Junpeng Xu Dongsheng Kong Yu Zhang Qijia Wu Liqin Wei Zihao Zhu Chunhui Li Shiyu Feng |
| author_facet | Yuxiang Sun Yuxiang Sun Junpeng Xu Dongsheng Kong Yu Zhang Qijia Wu Liqin Wei Zihao Zhu Chunhui Li Shiyu Feng |
| author_sort | Yuxiang Sun |
| collection | DOAJ |
| description | BackgroundAccurate preoperative identification of intracranial glioblastoma (GB), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) is crucial for determining the appropriate treatment strategy.PurposeWe aimed to develop and validate the utility of preoperative magnetic resonance imaging-based radiomics and machine learning models for the noninvasive identification them. STUDY TYPE: Retrospective. POPULATION: We included 202 patients, including 71 GB, 59 PCNSL, and 72 BM, randomly divided into a training cohort (n =141) and a validation cohort (n = 61).FIELD STRENGTH/SEQUENCE: Axial T2-weighted fast spin-echo sequence (T2WI) and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners. ASSESSMENT: We extracted radiomics features from the T2 sequence and CE-T1 sequence separately. Then, we applied the F-test and recursive feature elimination (RFE) to reduce the dimensionality for both individual sequences and the combined sequence CE-T1 combined with T2.The support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes classifier (NBC) were used in model development. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. Performance was evaluated using AUC, sensitivity, specificity, and accuracy metrics.ResultThe SVM model exhibited superior diagnostic performance with macro-average AUC values of 0.91 for CE-T1 alone, 0.86 for T2 alone, and 0.93 for combined CE-T1 and T2 sequences. And the combined sequence model demonstrated the best overall accuracy, sensitivity, and F1 score, with an accuracy of 0.77, outperforming both KNN and NBC models.ConclusionThe SVM-based MRI radiomics model effectively distinguishes between GB, PCNSL, and BM. Combining CE-T1 and T2 sequences significantly enhances classification performance, providing a robust, noninvasive diagnostic tool that could assist in treatment planning and improve patient outcomes. |
| format | Article |
| id | doaj-art-3af9e5e9c19e4eec9988ba74e513d941 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-3af9e5e9c19e4eec9988ba74e513d9412025-08-20T02:24:31ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15413501541350Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysisYuxiang Sun0Yuxiang Sun1Junpeng Xu2Dongsheng Kong3Yu Zhang4Qijia Wu5Liqin Wei6Zihao Zhu7Chunhui Li8Shiyu Feng9Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Xiongan, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Neurosurgery, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurosurgery, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, ChinaBackgroundAccurate preoperative identification of intracranial glioblastoma (GB), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) is crucial for determining the appropriate treatment strategy.PurposeWe aimed to develop and validate the utility of preoperative magnetic resonance imaging-based radiomics and machine learning models for the noninvasive identification them. STUDY TYPE: Retrospective. POPULATION: We included 202 patients, including 71 GB, 59 PCNSL, and 72 BM, randomly divided into a training cohort (n =141) and a validation cohort (n = 61).FIELD STRENGTH/SEQUENCE: Axial T2-weighted fast spin-echo sequence (T2WI) and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners. ASSESSMENT: We extracted radiomics features from the T2 sequence and CE-T1 sequence separately. Then, we applied the F-test and recursive feature elimination (RFE) to reduce the dimensionality for both individual sequences and the combined sequence CE-T1 combined with T2.The support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes classifier (NBC) were used in model development. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. Performance was evaluated using AUC, sensitivity, specificity, and accuracy metrics.ResultThe SVM model exhibited superior diagnostic performance with macro-average AUC values of 0.91 for CE-T1 alone, 0.86 for T2 alone, and 0.93 for combined CE-T1 and T2 sequences. And the combined sequence model demonstrated the best overall accuracy, sensitivity, and F1 score, with an accuracy of 0.77, outperforming both KNN and NBC models.ConclusionThe SVM-based MRI radiomics model effectively distinguishes between GB, PCNSL, and BM. Combining CE-T1 and T2 sequences significantly enhances classification performance, providing a robust, noninvasive diagnostic tool that could assist in treatment planning and improve patient outcomes.https://www.frontiersin.org/articles/10.3389/fonc.2025.1541350/fullcentral nervous system malignant tumorsmachine learningmagnetic resonance imaging; multi-classificationglioblastomaPCNSL = primary CNS lymphoma |
| spellingShingle | Yuxiang Sun Yuxiang Sun Junpeng Xu Dongsheng Kong Yu Zhang Qijia Wu Liqin Wei Zihao Zhu Chunhui Li Shiyu Feng Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis Frontiers in Oncology central nervous system malignant tumors machine learning magnetic resonance imaging; multi-classification glioblastoma PCNSL = primary CNS lymphoma |
| title | Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis |
| title_full | Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis |
| title_fullStr | Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis |
| title_full_unstemmed | Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis |
| title_short | Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis |
| title_sort | development and validation of a preoperative magnetic resonance imaging based and machine learning model for the noninvasive differentiation of intracranial glioblastoma primary central nervous system lymphoma and brain metastases a retrospective analysis |
| topic | central nervous system malignant tumors machine learning magnetic resonance imaging; multi-classification glioblastoma PCNSL = primary CNS lymphoma |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1541350/full |
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