A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

A 61-year-old patient was diagnosed with a left-sided falx meningioma. Histopathological analysis following extirpation showed a meningothelial meningioma ZNS WHO grade 1 with sparse mitoses. Over the course of 12 years, the patient received irradiation (54.0 Gy), peptide radio-receptor therapy (¹⁷⁷Lu-DOMITATE) and targeted therapy (mTOR inhibitor). Follow-up imaging revealed an increased size of the residual tumor. Due to increased liver function parameters, imaging of the liver was performed, showing widespread space-occupying lesions with atypical appearance. Biopsy revealed metastasis of the meningioma, now with 2.7 mitoses/mm², necrosis and homozygous CDKN2A/B deletion, corresponding to an anaplastic CNS meningioma WHO grade 3. A second small meningioma on the left petroclival side has been consistent in size over 12 years. Metastatic meningiomas pose a pertinent clinical challenge due to poor prognosis. The lung, bone, liver and cervical lymph nodes are the most common sites of extracranial metastasis. According to the World Health Organization criteria, the most important predictive factor for recurrence and metastasis is the tumor grade.

Background and Objectives: Immune checkpoint inhibitors (ICIs) have enriched tumor therapy, improving overall survival. Immunotherapy adverse events (irAEs) occur in up to 50% of patients and also affect the peripheral nervous system. The exact pathomechanism is unclear; however, an autoimmune process is implicated. Thus, the clinical evaluation of irAEs in the peripheral nervous system is still demanding. We retrospectively analyzed nerve ultrasound (NU) data of polyneuropathies (PNPs) secondary to checkpoint inhibitors. Materials and Methods: NU data of patients with PNP symptoms secondary to ICI therapy were retrospectively analyzed using the Ultrasound Pattern Sum Score (UPSS) as a quantitative marker. Our findings were compared with a propensity score match analysis (1:1 ratio) to NU findings in patients with chronic inflammatory demyelinating polyneuropathy (CIDP) and chemotherapy-associated PNP patients. Results: In total, 10 patients were included (4 female, mean age 66 ± 10.5, IQR 60–77), where NU was performed in 80%. The UPSS obtained ranged from 0 to 5 (mean 2 ± 1.6, IQR 1–2.5). The morphological changes seen in the NUs resembled sonographic changes seen in chemotherapy-associated PNP (n = 10, mean UPSS 1 ± 1, IQR 0–2) with little to no nerve swelling. In contrast, CIDP patients had a significantly higher UPSS (n = 10, mean UPSS 11 ± 4, IQR 8–13, p < 0.0001). Conclusions: Although an autoimmune process is hypothesized to cause peripheral neurological irAEs, NU showed no increased swelling as seen in CIDP. The nerve swelling observed was mild and comparable to ultrasound findings seen in chemotherapy-associated PNP.