MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas

Journal of Imaging Informatics in Medicine
Fei Zheng1, Ping Yin1, Yang Li2, Yujian Wang1, W. Hao1, Qian Hao1, Xuzhu Chen3, Nan Hong1
1Department of Radiology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People’s Republic of China
2Imaging Department, Shanxi Province, Shanxi Provincial People’s Hospital, Shanxi Medical University, No. 359 Heping North Road, Jiancaoping District, Taiyuan, People’s Republic of China
3Department of Radiology, Fengtai District, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Beijing, People’s Republic of China

Tóm tắt

AbstractThis paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

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Journal of Imaging Informatics in Medicine

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