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Stake J, Spiekers C, Akkurt BH, Heindel W, Brix T, Mannil M, Musigmann M. Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study. Diagnostics (Basel) 2024; 14:1070. [PMID: 38893597 PMCID: PMC11171889 DOI: 10.3390/diagnostics14111070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.84 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed.
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Affiliation(s)
- Jacob Stake
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
| | - Christine Spiekers
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
| | - Burak Han Akkurt
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
| | - Walter Heindel
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
| | - Tobias Brix
- Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
| | - Manoj Mannil
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
| | - Manfred Musigmann
- University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; (J.S.)
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Shen S, Wei R, Gao Y, Yang X, Zhang G, Yan B, Xiao Z, Li J. Cortical atrophy in early-stage patients with anti-NMDA receptor encephalitis: a machine-learning MRI study with various feature extraction. Cereb Cortex 2024; 34:bhad499. [PMID: 38185983 DOI: 10.1093/cercor/bhad499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
Conventional brain magnetic resonance imaging (MRI) of anti-N-methyl-D-aspartate-receptor encephalitis (NMDARE) is non-specific, thus showing little differential diagnostic value, especially for MRI-negative patients. To characterize patterns of structural alterations and facilitate the diagnosis of MRI-negative NMDARE patients, we build two support vector machine models (NMDARE versus healthy controls [HC] model and NMDARE versus viral encephalitis [VE] model) based on radiomics features extracted from brain MRI. A total of 109 MRI-negative NMDARE patients in the acute phase, 108 HCs and 84 acute MRI-negative VE cases were included for training. Another 29 NMDARE patients, 28 HCs and 26 VE cases were included for validation. Eighty features discriminated NMDARE patients from HCs, with area under the receiver operating characteristic curve (AUC) of 0.963 in validation set. NMDARE patients presented with significantly lower thickness, area, and volume and higher mean curvature than HCs. Potential atrophy predominately presented in the frontal lobe (cumulative weight = 4.3725, contribution rate of 29.86%), and temporal lobe (cumulative weight = 2.573, contribution rate of 17.57%). The NMDARE versus VE model achieved certain diagnostic power, with AUC of 0.879 in validation set. Our research shows potential atrophy across the entire cerebral cortex in acute NMDARE patients, and MRI machine learning model has a potential to facilitate the diagnosis MRI-negative NMDARE.
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Affiliation(s)
- Sisi Shen
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
| | - Ran Wei
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Yu Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Xinyuan Yang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Guoning Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Bo Yan
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Zhuoling Xiao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Jinmei Li
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
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Wei HL, Wei C, Feng Y, Yan W, Yu YS, Chen YC, Yin X, Li J, Zhang H. Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images. iScience 2023; 26:108107. [PMID: 37867961 PMCID: PMC10585394 DOI: 10.1016/j.isci.2023.108107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/19/2023] [Accepted: 09/27/2023] [Indexed: 10/24/2023] Open
Abstract
Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Cunsheng Wei
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Yibo Feng
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Jiangsu Province, Nanjing 210006, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Jiangsu Province, Nanjing 210006, China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
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Choi BK, Choi YJ, Sung M, Ha W, Chu MK, Kim WJ, Heo K, Kim KM, Park YR. Development and validation of an artificial intelligence model for the early classification of the aetiology of meningitis and encephalitis: a retrospective observational study. EClinicalMedicine 2023; 61:102051. [PMID: 37415843 PMCID: PMC10319989 DOI: 10.1016/j.eclinm.2023.102051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 07/08/2023] Open
Abstract
Background Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process. Methods In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model. Findings Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264. Interpretation This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction. Funding MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea.
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Affiliation(s)
- Bo Kyu Choi
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Jo Choi
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - MinDong Sung
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - WooSeok Ha
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Kyung Chu
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won-Joo Kim
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyoung Heo
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyung Min Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
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Xiang Y, Dong X, Zeng C, Liu J, Liu H, Hu X, Feng J, Du S, Wang J, Han Y, Luo Q, Chen S, Li Y. Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China. Front Immunol 2022; 13:913703. [PMID: 35720336 PMCID: PMC9199424 DOI: 10.3389/fimmu.2022.913703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022] Open
Abstract
Objective To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early in patients with adult anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in Southwest China. Methods From January 2012, a two-center study of anti-NMDAR encephalitis was initiated to collect clinical and MRI data from acute patients in Southwest China. Two experienced neurologists independently assessed the patients’ prognosis at 24 moths based on the modified Rankin Scale (mRS) (good outcome defined as mRS 0–2; bad outcome defined as mRS 3-6). Risk factors influencing the prognosis of patients with acute anti-NMDAR encephalitis were investigated using clinical data. Five DL and radiomics models trained with four single or combined four MRI sequences (T1-weighted imaging, T2-weighted imaging, fluid-attenuated inversion recovery imaging and diffusion weighted imaging) and a clinical model were developed to predict the prognosis of anti-NMDAR encephalitis. A fusion model combing a clinical model and two machine learning-based models was built. The performances of the fusion model, clinical model, DL-based models and radiomics-based models were compared using the area under the receiver operating characteristic curve (AUC) and accuracy and then assessed by paired t-tests (P < 0.05 was considered significant). Results The fusion model achieved the significantly greatest predictive performance in the internal test dataset with an AUC of 0.963 [95% CI: (0.874-0.999)], and also significantly exhibited an equally good performance in the external validation dataset, with an AUC of 0.927 [95% CI: (0.688-0.975)]. The radiomics_combined model (AUC: 0.889; accuracy: 0.857) provided significantly superior predictive performance than the DL_combined (AUC: 0.845; accuracy: 0.857) and clinical models (AUC: 0.840; accuracy: 0.905), whereas the clinical model showed significantly higher accuracy. Compared with all single-sequence models, the DL_combined model and the radiomics_combined model had significantly greater AUCs and accuracies. Conclusions The fusion model combining clinical variables and machine learning-based models may have early predictive value for poor outcomes associated with anti-NMDAR encephalitis.
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Affiliation(s)
- Yayun Xiang
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xiaoxuan Dong
- College of Computer and Information Science, Chongqing, China
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Junhang Liu
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hanjing Liu
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xiaofei Hu
- Department of Neurology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jinzhou Feng
- Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Silin Du
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jingjie Wang
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Yongliang Han
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Qi Luo
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
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6
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Drenthen GS, Jansen JFA. Editorial for "Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI". J Magn Reson Imaging 2021; 55:1093-1094. [PMID: 34668607 DOI: 10.1002/jmri.27951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 09/27/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
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