Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases

Abstract

Background Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. Methods We recruited 116 CNS demyelinating patients including 78 MS, and 38 NMO. Three neuroradiologists performed visual differential diagnosis based on brain MRI for comparison purpose. A multi-level scheme was designed to harness the selection of discriminative and stable radiomics features extracted from brain while mater lesions in T1-MPRAGE, T2 sequences and clinical factors. Based on the imaging phenotype composed of the selected radiomic and clinical features, Multi-parametric Multivariate Random Forest (MM-RF) model was constructed and verified with both 10-fold cross-validation and independent testing. Result interpretation was provided to build trust in diagnostic decisions. Results Eighty-six patients were randomly selected to form the training set while the rest 30 patients for independent testing. On the training set, our MM-RF model achieved accuracy 0.849 and AUC 0.826 in 10-fold cross-validation, which were significantly higher than clinical visual analysis (0.709 and 0.683, p < 0.05). In the independent testing, the MM-RF model achieved AUC 0.902, accuracy 0.871, sensitivity 0.873, specificity 0.869, respectively. Furthermore, age, sex and EDSS were found mildly correlated with the radiomic features (p of all < 0.05). Conclusions Multi-parametric radiomic features have potential as practical quantitative imaging biomarkers for differentiating MS from NMO.

Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study

“…Based on the assumption that medical imaging contains exploitable information reflecting underlying pathophysiology, the radiomics approach utilizes the extraction and subsequent analysis of various standardized radiomic features from radiological images. In recent years, radiomics has shown promising potential beyond its original application in oncology imaging [ 20 , 21 , 33 , 34 ]. However, application to AE has been less extensively studied.…”

“…For example, recent studies have shown radiomics’ potential in differentiating demyelinating diseases of the central nervous system [ 20 ], identifying Parkinson’s disease subtypes [ 21 ] and discriminating grade II glioma from brain inflammation [ 22 ].…”

Machine learning classification of multiple sclerosis in children using optical coherence tomography

“…This sensitivity and specificity surpass VEP, where the sensitivity for predicting MS diagnosis in adults ranges between 20% and 50% in children with clinically isolated syndromes, and 25% and 50% in children with clinically definite MS. 20 , 21 Importantly, the sensitivity and specificity of OCT in diagnosing MS comes close to that of the current diagnostic gold standard, MRI. Studies looking at binary classification between MS and controls 22 , 23 or MS and NMOSD 24 using artificial intelligence have reported high sensitivity at 96% and 87%, respectively. Our results suggest that auto-segmented OCT features may be as useful and readily clinically applicable in diagnosing MS in children without any a priori criteria, although MRI may have greater sensitivity and specificity when screening for other pathologies.…”

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

“…Huang et al [37] collected magnetic resonance images of 116 patients with demyelinating diseases of the central nervous system (including 38 cases of NMO and 78 cases of MS). Based on radiological and clinical features, a The accuracy, sensitivity, specificity, and F1 scores of network A were 0:863 ± 0:055, 0:750 ± 0:136, 0:896 ± 0:042, and 0:712 ± 0:121, respectively; the accuracy, sensitivity, specificity, and F1 scores of network B were 0:855 ± 0:018, 0:821 ± 0:071, 0:865 ± 0:021, and 0:719 ± 0:040, and the AUC values of the two networks were 0.922.…”