Many studies report an overlap of MRI and clinical findings between patients with relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), which in part is reflective of inclusion of subjects with variable disease duration and short periods of follow-up. To overcome these limitations, we examined the differences between RRMS and SPMS and the relationship between MRI measures and clinical outcomes 30 years after first presentation with clinically isolated syndrome suggestive of multiple sclerosis. Sixty-three patients were studied 30 years after their initial presentation with a clinically isolated syndrome; only 14% received a disease modifying treatment at any time point. Twenty-seven patients developed RRMS, 15 SPMS and 21 experienced no further neurological events; these groups were comparable in terms of age and disease duration. Clinical assessment included the Expanded Disability Status Scale, 9-Hole Peg Test and Timed 25-Foot Walk and the Brief International Cognitive Assessment For Multiple Sclerosis. All subjects underwent a comprehensive MRI protocol at 3 T measuring brain white and grey matter (lesions, volumes and magnetization transfer ratio) and cervical cord involvement. Linear regression models were used to estimate age- and gender-adjusted group differences between clinical phenotypes after 30 years, and stepwise selection to determine associations between a large sets of MRI predictor variables and physical and cognitive outcome measures. At the 30-year follow-up, the greatest differences in MRI measures between SPMS and RRMS were the number of cortical lesions, which were higher in SPMS (the presence of cortical lesions had 100% sensitivity and 88% specificity), and grey matter volume, which was lower in SPMS. Across all subjects, cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of the Expanded Disability Status Scale; cortical lesions alone explained 43%. Grey matter volume, cortical lesions and gender explained 43% of the variance of Timed 25-Foot Walk. Reduced cortical magnetization transfer ratios emerged as the only significant explanatory variable for the symbol digit modality test and explained 52% of its variance. Cortical involvement, both in terms of lesions and atrophy, appears to be the main correlate of progressive disease and disability in a cohort of individuals with very long follow-up and homogeneous disease duration, indicating that this should be the target of therapeutic interventions.

Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC.

Phosphorous magnetic resonance spectroscopy (31P-MRS) allows a non-invasive analysis of phosphorus-containing compounds in vivo. The present study investigated the influence of brain region, hemisphere, age, sex and brain volume on 31P-MRS metabolites in healthy adults. Supratentorial brain 31P-MRS spectra of 125 prospectively recruited healthy volunteers (64 female, 61 male) aged 20 to 85 years (mean: 49.4 ± 16.9 years) were examined with a 3D-31P-MRS sequence at 3T, and the compounds phosphocreatine (PCr), inorganic phosphate (Pi) and adenosine triphosphate (ATP) were measured. From this data, the metabolite ratios PCr/ATP, Pi/ATP and PCr/Pi were calculated for different brain regions. In addition, volumes of gray matter, white matter and cerebrospinal fluid were determined. For all metabolite ratios significant regional differences and in several regions sex differences were found. In some brain regions and for some metabolites hemispheric differences were detected. In addition, changes with aging were found, which differed between women and men. The present results indicate that 31P-MRS metabolism varies throughout the brain, with age and between sexes, and therefore have important practical implications for the design and the interpretation of future 31P-MRS studies under physiological conditions and in patients with various cerebral diseases.

To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm. Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (Pd/Md) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%). We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy. This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA.

Radiation necrosis represents a potentially devastating complication after radiation therapy in brain tumors. The establishment of the diagnosis and especially the differentiation from progression and pseudoprogression with its therapeutic implications requires interdisciplinary consent and monitoring. Herein, we want to provide an overview of the diagnostic modalities, therapeutic possibilities and an outlook on future developments to tackle this challenging topic. The aim of this report is to provide an overview of the current morphological, functional, metabolic and evolving imaging tools described in the literature in order to (I) identify the best criteria to distinguish radionecrosis from tumor recurrence after the radio-oncological treatment of malignant gliomas and cerebral metastases, (II) analyze the therapeutic possibilities and (III) give an outlook on future developments to tackle this challenging topic. Additionally, we provide the experience of a tertiary tumor center with this important issue in neuro-oncology and provide an institutional pathway dealing with this problem.

Preoperative grade prediction is important in diagnostics of glioma. Even more important can be follow-up after chemotherapy and radiotherapy of high grade gliomas. In this review we provide an overview of MR-spectroscopy (MRS), technical aspects, and different clinical scenarios in the diagnostics and follow-up of gliomas in pediatric and adult populations. Furthermore, we provide a recap of the current research utility and possible future strategies regarding proton- and phosphorous-MRS in glioma research.