Background: Neuromyelitis optica (NMO) shows various brain magnetic resonance imaging (MRI) abnormalities with recurrent central nervous system (CNS) attacks, although predominantly affecting the spinal cord and optic nerve. However, NMO with extensive involvement of the brain has infrequently been studied. We investigated the clinical, radiographic features and immunomodulating changes of NMO patients with extensive brain lesions (EBLs) in China. Methods: NMO patients (including 16 NMO patients with EBLs and 53 NMO patients without EBLs) hospitalized during January 2006 and February 2010 were recruited and analyzed retrospectively. Data of clinical characteristics, magnetic resonance imaging (MRI) features, laboratory abnormalities, treatment details and outcomes were analyzed. All the patients received the follow-up visits for two years. Results: EBLs in NMO were classified into four categories according to their respective MRI characteristics: 1) Tumefactive-like lesions (n=4, 25%); 2) Acute disseminated encephalomyelitis (ADEM)-like lesions (n=6, 37.5%); 3) Multiple sclerosis (MS)-like lesions (n=5, 31.25%); 4) Posterior reversible encephalopathy syndrome (PRES)-like lesions (n=1, 6.25%). NMO patients with EBLs had higher rates of encephalopathy symptoms (37.5% vs. 5.6%, p = 0.004), homonymous hemianopia (18.8% vs. 0%, p = 0.011) and AQP4 seropositivity (100% vs. 69.8%, p = 0.008) than NMO patients without EBLs (NEBLs). Immunomodulating changes (including the levels of C3, C4, ESR and CRP) were significantly higher in patients with EBLs than those without EBLs. The relapse times in EBLs during the follow-up period were more frequent than those happened in NEBLs (1.88 ± 0.30 vs. 1.23 ± 0.14, p = 0.04). The EDSS scores in EBLs patients were also much higher than those in NEBLs throughout all the whole visits of follow-up. Conclusions: The presence of EBLs in NMO may indicate a higher diseases activity and portend a worse prognosis. CRP is a useful marker in monitoring diseases activity. Systemic inflammation may be crucial to the formation of EBLs in NMO.

Acute brainstem syndrome (ABS) may herald multiple sclerosis (MS), neuromyelitis optica (NMO), or occur as an isolated syndrome. The aquaporin 4 (AQP4)-specific serum autoantibody, NMO-IgG, is a biomarker for NMO. However, the role of anti-AQP4 antibody in the conversion of ABS to NMO is unclear. Thirty-one patients with first-event ABS were divided into two groups according to the presence of anti-AQP4 antibodies, their clinical features and outcomes were retrospectively analyzed. Fourteen of 31 patients (45.16 %) were seropositive for NMO-IgG. The 71.43 % of anti-AQP4 (+) ABS patients converted to NMO, while only 11.76 % of anti-AQP4 (-) ABS patients progressed to NMO. Anti-AQP4 (+) ABS patients demonstrated a higher IgG index (0.68 ± 0.43 vs 0.42 ± 0.13, p < 0.01) and Kurtzke Expanded Disability Status Scale (4.64 ± 0.93 vs 2.56 ± 0.81, p < 0.01) than anti-AQP4 (-) ABS patients. Area postrema clinical brainstem symptoms occurred more frequently in anti-AQP4 (+) ABS patients than those in anti-AQP4 (-) ABS patients (71.43 % vs 17.65 %, p = 0.004). In examination of magnetic resonance imaging (MRI), the 78.57 % of anti-AQP4 (+) ABS patients had medulla-predominant involvements in the sagittal view and dorsal-predominant involvements in the axial view. ABS represents an inaugural or limited form of NMO in a high proportion of anti-AQP4 (+) patients.

Objective: To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T 2WI (n = 788) and diffusion-weighted imaging (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: Nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusion: The multiparametric MRI-based radiomics signature can potentially serve as a non-invasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to non-invasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.