Niederschweiberer, Moritz

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Background Psychiatric comorbidities are increasingly recognised in demyelinating diseases, yet their frequency and timing in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are not well defined. We aimed to characterise the spectrum and temporal pattern of psychiatric illness in MOGAD and compare to multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) with aquaporin-4 antibody (AQP4-IgG). Methods We conducted a cross-sectional study of patients with MOGAD (n=388), age-matched and sex-matched MS (n=257) and AQP4-IgG-positive NMOSD (n=58) evaluated at Mayo Clinic (1991–2025). Psychiatric diagnoses, timing relative to the first attack and patient-reported outcomes were extracted from electronic health records (EHRs). Complementary analyses were conducted using the NeuroBlu real-world EHR database. Results Among 388 patients with MOGAD, 54.8% had ≥1 psychiatric disorder, most commonly anxiety (47.7%), depression (36.6%) and attention-deficit/hyperactivity disorder (ADHD, 9%). After the first attack, 33.3% of patients with MOGAD developed a new psychiatric disorder, often within the first year. MOGAD showed a lower frequency of depression versus MS (36.6% vs 47.5%, p=0.02) but similar rates of anxiety and ADHD. Temporal patterns differed: psychiatric disorders often appeared years before onset in MS but clustered shortly after onset in MOGAD. NeuroBlu analyses supported high rates of psychiatric disorders across the three conditions. Conclusion Psychiatric comorbidities are common in MOGAD, particularly depression and anxiety, typically emerging close after onset, whereas in MS they often preceded onset by years. These findings underscore the need for routine psychiatric screening in MOGAD and highlight unresolved questions regarding whether, and to what extent, MOG-IgG-mediated inflammatory demyelination contributes to psychiatric vulnerability.