An individual radiomics nomogram for differential diagnosis between multiple sclerosis and neuromyelitis optica spectrum disorder
Yaou Liu1, Di Dong2, Liwen Zhang2, Yunyun Duan1, Jie Tian2, and Kuncheng Li3

1Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 2CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China


Clinically distinguishing the multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) is critical, since the prognosis and treatment of these disorders differ. We extracted nine radiomics features from 485 radiomics features combining with clinical measurements to build the model for differentiating MS and NMOSD. The area under receiver operating characteristic curve (AUC) of the model was 0.8808 and 0.7115 in the primary and validation cohort. The model demonstrated good calibration. The current study revealed the different radiomics features between MS and NMOSD, and developed and validated an individual model to differentiate the two diseases.


Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are two main inflammatory demyelinating diseases of the central nervous system1, 2. Despite the existence of diagnostic criteria 3, 4, the differential diagnosis of the two diseases is difficult. Therefore it is crucial to identify new efficient biomarkers to quantify the pathological alterations and accurately differentiate the two diseases. Radiomics is the process of the conversion of medical images into high-dimensional, mineable data via high-throughput extraction of quantitative features, followed by subsequent data analysis for decision support5-7.It can capture important lesion information and has great potential to provide valuable information for revealing the pathophysiology, helping differential diagnosis, and guiding personalized therapy. This study aims to investigate the radiomics features of spinal cord lesion in MS and NMOSD, and develop and validate a nomogram that incorporated both the radiomics signature and clinical variables for individual differential diagnosis of the two diseases.


189 patients (95 MS and 94 NMOSD) with spinal cord lesions were recruited from Xuanwu Hospital, Capital Medical University including 135 patients in the primary cohort and 54 patients in the validation cohort. The main demographic and clinical characteristics of the patients were shown in Figure1. All spinal cord MRI scans were performed on a 3.0 Tesla MR system (Siemens Magnetom Trio Tim system, Germany). Marking the hyperintense cord lesions as ROIs on sagittal T2-weighted images was performed by an experienced neuroradiologist (Y.D) using MRIcro software (http://www.mccauslandcenter.sc.edu/mricro/mricro/mricro.html). In our study, we applied the emerging method of radiomics to discriminate the MS and NMOSD (Figure 2). The process mainly included following steps:(1) Feature extraction: we described regions of interest (ROIs) of the spinal cord lesions by extracting four radiomics features group8: a: shape and size features, b: grey intensity features, c: textural features and d: wavelet features. (2) Feature selection: we used least absolute shrinkage and selection operator (LASSO) method to select features and built a logistic regression model. We built the prediction model for differentiate MS and NMOSD by the radiomics features combining with several clinical variables, and the receiver operating characteristic (ROC) curve was plotted to show the performance of the model. An individual radiomics nomogram was developed using multivariable logistic regression on the basis of discriminative predictors in the primary cohort. In order to assess the performance of the nomogram, we plotted the calibration curve and calculated the Harrell’s C-index to quantify the discrimination of radiomics nomogram.


After feature selection, 485 radiomics features were reduced to 9 potential features and 7 clinical and routine MRI characteristics were reduced to 3 potential variables, which were implemented to develop the LASSO logistic regression model, including WLLH_GLCM_cluster_tendency (P=0.027), WLHL_GLCM_difference_entropy(P=0.20), WHLL_GLCM_cluster_shade (P=0.098), GLRLM_SRE (P=3.5×10-5), WLHL_GLRLM_LRE (P=0.077), WLHL_GLRLM_LRLGLE (P=0.077), WHLL_GLRLM_LRHGLE (P=1.2×10-4), WHLH_GLRLM_LRE(P=5.6×10-5), WHLH_GLRLM_LRLGLE (P=5.6×10-5), gender (P=0.011), length (P=4.6×10-5) and EDSS(P =0.05). The area under ROC curve (AUC) was 0.8808 and 0.7115 in the primary and validation cohorts, and 0.898 and 0.6684 in the female primary and validation cohort respectively (Figure 3). The individualized prediction model for discrimating MS and NMO was developed by the multivariate logistic regression analysis and visualized by the nomogram (Figure 4). The C-index for the nomogram was 0.8902 (95% CI, 0.851 to 0.932) (Figure 5).


In this study, we demonstrated the different radiomics features of spinal cord lesions in MS and NMOSD, developed and validated an individual radimoics nomogram combining with clinical variables to differentiate the two diseases. The main radiomics features differentiate the two diseases (MS vs NMOSD) is the heterogeneity of the lesion signal, which is consistent with pathological studies showing more severe demyelination, axonal loss in NMOSD than MS, and NMOSD lesion can present necrotic and cystic changes with extensive tissue destruction1, 9, 10. These features can be captured and quantified by radiomics and help understand the pathophysiology of the disease and performed better than clinical measures and other lesion appearance such as lesion length to discriminating the two diseases. The radiomics features of cord lesions dominated the nomogram in terms of relative contribution to total points and differential diagnosis between the two diseases, highlighting the clinical importance of radiomics features.


A validated nomogram that incorporates the radiomics signature combining with spinal cord lesions, cord lesion length, gender, and EDSS can well differential MS and NMOSD based on routine MRI data. Further study from different parameters from various scanners in multicenter setting is required to validate our current findings and confirm its generalizability.


This work was supported by the ECTRIMS-MAGNMIS Fellowship from ECTRIMS (Y.L), the National Science Foundation of China (Nos. 81101038, 81227901, 81771924, 81501616, 61231004, 81401377, 81471221 and 81230028), the National Basic Research Program of China (2013CB966900), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701), the Beijing Natural Science fund (No.7133244), the Beijing Nova Programme (xx2013045), Beijing Municipal Administration of Hospital Clinical Medicine Development of Special Funding Support (code:ZYLX201609) , the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), and Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period(2012BAI10B04)


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Figure 1. Characteristics of patients in the primary and validation cohorts

Data are presented as mean ± standard or median depending on the normality (Lilliefors test). EDSS = Expanded Disability Status Scale; F, female; HC = healthy control; M, male; MMSE = Mini-Mental State Examination; NA, not available; NMOSD = neuromyelitis optica spectrum disorder aP-value obtained using two-tail Pearson chi-square test. bP-values obtained using two-tail Wilcoxon rank sum tests. cP-values obtained using two-sample two-tail t tests. Radiomics score revealed that the reliability of prediction for each patient.

Figure 2. The procedure of radiomics method. (a) Original images of patients. (b) Contour of lesion delineated by experienced radiologists. (c) Extracting features from the segmented regions of interest (ROIs), such as shape, intensity, texture and wavelet features. (d) Prediction analysis by least absolute shrinkage and selection operator (LASSO) regression model.

Figure 3 (a) ROC curves of primary and validation cohorts. There were 135 patients in primary cohort. We used 54 patients as validation cohort to test the model. Radiomics features combining with clinical variables had the potential ability to predict the MS and NMOSD; (b) ROC curves analysis for the female in primary and validation cohorts

Figure 4. We depicted the radiomics nomogram by the radiomics signatures and several clinical and routine MRI variables. We can obtain a value of prediction for each patient.

Figure 5. (a) Calibration curve of the radiomics nomogram for primary cohort with 135 patients. The C-index was 0.8902 (95% CI, 0.851 to 0.932). The corrected C-index was 0.870 via bootstrapping validation; (b) calibration curve of the radiomics nomogram for validation cohort with 54 patients. The C-index was 0.804 (95% CI, 0.690 to 0.917). The corrected C-index was 0.782 via bootstrapping validation. We plotted the calibration carve in the primary cohort to test discrimination of model prediction ability for MS and NMOSD. X-axis represents the nomogram predicted probability. Y-axis represents the actual probability. The diagonal dotted line with blue described an ideal prediction by an optimal model. And the red diagonal dotted line described the prediction of nomogram predicted value. The black solid line revealed the performance of calibration curve with multiple sets of the bootstrap

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)