Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images
Abstract Background Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. This study aims to combine machine learnin...
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BMC
2025-05-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01712-2 |
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| author | Ziyan Chen Yinhua Chen Yandong Su Nian Jiang Siyi Wanggou Xuejun Li |
| author_facet | Ziyan Chen Yinhua Chen Yandong Su Nian Jiang Siyi Wanggou Xuejun Li |
| author_sort | Ziyan Chen |
| collection | DOAJ |
| description | Abstract Background Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. This study aims to combine machine learning and deep learning algorithms with pre-operative MRI images to build a model for PRTs surgical approaches recommendation, striving to model clinical experience for practical reference and education. Methods This study was a retrospective study which enrolled a total of 173 patients diagnosed with PRTs radiologically from our hospital. Three traditional surgical approaches of were recorded for prediction label. Clinical and VASARI related radiological information were selected for machine learning prediction model construction. And MRI images from axial, sagittal and coronal views of orientation were also used for deep learning craniotomy approach prediction model establishment and evaluation. Results 5 machine learning methods were applied to construct the predictive classifiers with the clinical and VASARI features and all methods could achieve area under the ROC (Receiver operating characteristic) curve (AUC) values over than 0.7. And also, 3 deep learning algorithms (ResNet-50, EfficientNetV2-m and ViT) were applied based on MRI images from different orientations. EfficientNetV2-m achieved the highest AUC value of 0.89, demonstrating a significant high performance of prediction. And class activation mapping was used to reveal that the tumor itself and its surrounding relations are crucial areas for model decision-making. Conclusion In our study, we used machine learning and deep learning to construct surgical approach recommendation models. Deep learning could achieve high performance of prediction and provide efficient and personalized decision support tools for PRTs surgical approach. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-80466c5bfb904285b045ef288ac33632 |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-80466c5bfb904285b045ef288ac336322025-08-20T02:38:32ZengBMCBMC Medical Imaging1471-23422025-05-0125111210.1186/s12880-025-01712-2Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI imagesZiyan Chen0Yinhua Chen1Yandong Su2Nian Jiang3Siyi Wanggou4Xuejun Li5Department of Neurosurgery, Xiangya Hospital, Central South UniversityDepartment of Neurosurgery, Xiangya Hospital, Central South UniversityDepartment of Neurosurgery, Changhai Hospital, Naval Medical UniversityDepartment of Neurosurgery, Xiangya Hospital, Central South UniversityDepartment of Neurosurgery, Xiangya Hospital, Central South UniversityDepartment of Neurosurgery, Xiangya Hospital, Central South UniversityAbstract Background Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. This study aims to combine machine learning and deep learning algorithms with pre-operative MRI images to build a model for PRTs surgical approaches recommendation, striving to model clinical experience for practical reference and education. Methods This study was a retrospective study which enrolled a total of 173 patients diagnosed with PRTs radiologically from our hospital. Three traditional surgical approaches of were recorded for prediction label. Clinical and VASARI related radiological information were selected for machine learning prediction model construction. And MRI images from axial, sagittal and coronal views of orientation were also used for deep learning craniotomy approach prediction model establishment and evaluation. Results 5 machine learning methods were applied to construct the predictive classifiers with the clinical and VASARI features and all methods could achieve area under the ROC (Receiver operating characteristic) curve (AUC) values over than 0.7. And also, 3 deep learning algorithms (ResNet-50, EfficientNetV2-m and ViT) were applied based on MRI images from different orientations. EfficientNetV2-m achieved the highest AUC value of 0.89, demonstrating a significant high performance of prediction. And class activation mapping was used to reveal that the tumor itself and its surrounding relations are crucial areas for model decision-making. Conclusion In our study, we used machine learning and deep learning to construct surgical approach recommendation models. Deep learning could achieve high performance of prediction and provide efficient and personalized decision support tools for PRTs surgical approach. Clinical trial number Not applicable.https://doi.org/10.1186/s12880-025-01712-2Pineal region tumorsCraniotomy approachesMagnetic resonance imagingMachine learningDeep learning |
| spellingShingle | Ziyan Chen Yinhua Chen Yandong Su Nian Jiang Siyi Wanggou Xuejun Li Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images BMC Medical Imaging Pineal region tumors Craniotomy approaches Magnetic resonance imaging Machine learning Deep learning |
| title | Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images |
| title_full | Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images |
| title_fullStr | Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images |
| title_full_unstemmed | Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images |
| title_short | Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images |
| title_sort | machine learning decision support model construction for craniotomy approach of pineal region tumors based on mri images |
| topic | Pineal region tumors Craniotomy approaches Magnetic resonance imaging Machine learning Deep learning |
| url | https://doi.org/10.1186/s12880-025-01712-2 |
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