基于MRI图像的松果体区肿瘤开颅手术入路的机器学习决策支持模型构建
Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.
作者信息
Chen Ziyan, Chen Yinhua, Su Yandong, Jiang Nian, Wanggou Siyi, Li Xuejun
机构信息
Department of Neurosurgery, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, Hunan, P. R. China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, P. R. China.
出版信息
BMC Med Imaging. 2025 May 27;25(1):194. doi: 10.1186/s12880-025-01712-2.
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.
背景
松果体区肿瘤（PRTs）虽罕见但为深部脑肿瘤，完整手术切除对有效治疗肿瘤至关重要。由于发病率低且位置深，手术入路的选择往往具有挑战性。本研究旨在将机器学习和深度学习算法与术前MRI图像相结合，构建PRT手术入路推荐模型，努力将临床经验建模以供实际参考和教学。
方法
本研究为回顾性研究，共纳入我院经影像学诊断为PRTs的173例患者。记录三种传统手术入路作为预测标签。选择临床和VASARI相关的放射学信息用于构建机器学习预测模型。还使用轴向、矢状面和冠状面方向的MRI图像建立并评估深度学习开颅手术入路预测模型。
结果
应用5种机器学习方法构建具有临床和VASARI特征的预测分类器，所有方法的受试者工作特征曲线（ROC）下面积（AUC）值均超过0.7。此外，基于不同方向的MRI图像应用了3种深度学习算法（ResNet-50、EfficientNetV2-m和ViT）。EfficientNetV2-m的AUC值最高，为0.89，显示出显著的高性能预测。并使用类激活映射揭示肿瘤本身及其周围关系是模型决策的关键区域。
结论
在我们的研究中，我们使用机器学习和深度学习构建手术入路推荐模型。深度学习可实现高性能预测，为PRT手术入路提供高效且个性化的决策支持工具。
临床试验编号
不适用。
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