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Zheng F, Yin P, Yang L, Wang Y, Hao W, Hao Q, Chen X, Hong N. MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:653-665. [PMID: 38343248 PMCID: PMC11031538 DOI: 10.1007/s10278-023-00957-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 04/20/2024]
Abstract
This 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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Affiliation(s)
- Fei Zheng
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Li Yang
- Imaging Department, Shanxi Province, Shanxi Provincial People's Hospital, Shanxi Medical University, No. 359 Heping North Road, Jiancaoping District, Taiyuan, People's Republic of China
| | - Yujian Wang
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Wenhan Hao
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Qi Hao
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Xuzhu Chen
- Department of Radiology, Fengtai District, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Beijing, People's Republic of China.
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China.
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Zhao K, Gao A, Gao E, Qi J, Chen T, Zhao G, Zhao G, Wang P, Wang W, Bai J, Zhang Y, Zhang H, Yang G, Ma X, Cheng J. Multiple diffusion metrics in differentiating solid glioma from brain inflammation. Front Neurosci 2024; 17:1320296. [PMID: 38352939 PMCID: PMC10861663 DOI: 10.3389/fnins.2023.1320296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024] Open
Abstract
Background and purpose The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models. Materials and methods Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated. Results 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758). Conclusion Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.
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Affiliation(s)
- Kai Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ankang Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinbo Qi
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ting Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Gaoyang Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Bai
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huiting Zhang
- MR Research Collaboration, Siemens Healthineers Ltd., Wuhan, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xiaoyue Ma
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Beretti T, Desnous B. Vertigo and dizziness in children: When to consider a neurological cause. Arch Pediatr 2023; 30:505-509. [PMID: 37537083 DOI: 10.1016/j.arcped.2023.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 07/04/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023]
Abstract
Vertigo is common in childhood and adolescence. Although children and adults share common causes of vertigo, epidemiology changes with aging. For instance, ischemic stroke is less frequent in childhood, whereas audiovestibular disorders, such as vestibular neuritis and the migraine equivalent, are the leading causes of vertigo. However, even if severe causes of vertigo are rare, clinicians must not miss them. In this review, we discuss the neurological causes of central vertigo in children. The diagnostic approaches reviewed here are focused on the search for signs of severity, such as an abrupt onset, infectious context, or intracranial hypertension, which may subsequently require brain imaging.
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Affiliation(s)
- Thibault Beretti
- Department of Paediatric Neurology, La Timone Children Hospital, Aix-Marseille University, France
| | - Béatrice Desnous
- Department of Paediatric Neurology, La Timone Children Hospital, Aix-Marseille University, France.
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Piao S, Luo X, Bao Y, Hu B, Liu X, Zhu Y, Yang L, Geng D, Li Y. An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma. Front Neurol 2022; 13:998279. [PMID: 36408523 PMCID: PMC9669344 DOI: 10.3389/fneur.2022.998279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions. METHODS In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists. RESULTS The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets. CONCLUSION The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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Han Y, Yang Y, Shi ZS, Zhang AD, Yan LF, Hu YC, Feng LL, Ma J, Wang W, Cui GB. Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI. Eur J Radiol 2020; 134:109467. [PMID: 33307462 DOI: 10.1016/j.ejrad.2020.109467] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/22/2020] [Accepted: 12/01/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE In populations without contrast enhancement, the imaging features of atypical brain parenchyma inflammations can mimic those of grade II gliomas. The aim of this study was to assess the value of the conventional MR-based radiomics signature in differentiating brain inflammation from grade II glioma. METHODS Fifty-seven patients (39 patients with grade II glioma and 18 patients with inflammation) were divided into primary (n = 44) and validation cohorts (n = 13). Radiomics features were extracted from T1-weighted images (T1WI) and T2-weighted images (T2WI). Two-sample t-test and least absolute shrinkage and selection operator (LASSO) regression were adopted to select features and build radiomics signature models for discriminating inflammation from glioma. The predictive performance of the models was evaluated via area under the receiver operating characteristic curve (AUC) and compared with the radiologists' assessments. RESULTS Based on the primary cohort, we developed T1WI, T2WI and combination (T1WI + T2WI) models for differentiating inflammation from glioma with 4, 8, and 5 radiomics features, respectively. Among these models, T2WI and combination models achieved better diagnostic efficacy, with AUC of 0.980, 0.988 in primary cohort and that of 0.950, 0.925 in validation cohort, respectively. The AUCs of radiologist 1's and 2's assessments were 0.661 and 0.722, respectively. CONCLUSION The signature based on radiomics features helps to differentiate inflammation from grade II glioma and improved performance compared with experienced radiologists, which could potentially be useful in clinical practice.
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Affiliation(s)
- Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China
| | - Zhe-Sheng Shi
- College of Basic Medicine, Fourth Military Medical University, Xi'an, Shaanxi, 710032, PR China
| | - An-Ding Zhang
- College of Basic Medicine, Fourth Military Medical University, Xi'an, Shaanxi, 710032, PR China
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China
| | - Lan-Lan Feng
- Department of Pathology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, PR China
| | - Jiao Ma
- Department of Pathology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, PR China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China.
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China.
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Panagopoulos D, Themistocleous M, Apostolopoulou K, Sfakianos G. Herpes Simplex Encephalitis Initially Erroneously Diagnosed as Glioma of the Cerebellum: Case Report and Literature Review. World Neurosurg 2019; 129:421-427. [DOI: 10.1016/j.wneu.2019.06.158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 06/17/2019] [Accepted: 06/19/2019] [Indexed: 10/26/2022]
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