Neurofibromatosis type 1 (NF1) is a proteiform genetic condition caused by pathogenic variants in NF1 and characterized by a heterogeneous phenotypic presentation. Relevant genotype–phenotype correlations have recently emerged, but only few pertinent studies are available. We retrospectively reviewed clinical, instrumental, and genetic data from a cohort of 583 individuals meeting at least 1 diagnostic National Institutes of Health (NIH) criterion for NF1. Of these, 365 subjects fulfilled ≥2 NIH criteria, including 235 pediatric patients. Genetic testing was performed through cDNA-based sequencing, Next Generation Sequencing (NGS), and Multiplex Ligation-dependent Probe Amplification (MLPA). Uni- and multivariate statistical analysis was used to investigate genotype–phenotype correlations. Among patients fulfilling ≥ 2 NIH criteria, causative single nucleotide variants (SNVs) and copy number variations (CNVs) were detected in 267/365 (73.2%) and 20/365 (5.5%) cases. Missense variants negatively correlated with neurofibromas (p = 0.005). Skeletal abnormalities were associated with whole gene deletions (p = 0.05) and frameshift variants (p = 0.006). The c.3721C>T; p.(R1241*) variant positively correlated with structural brain alterations (p = 0.031), whereas Lisch nodules (p = 0.05) and endocrinological disorders (p = 0.043) were associated with the c.6855C>A; p.(Y2285*) variant. We identified novel NF1 genotype–phenotype correlations and provided an overview of known associations, supporting their potential relevance in the implementation of patient management.

BRAF inhibitors, in recent years, have played a central role in the disease control of unresectable BRAF-mutated pediatric low-grade gliomas (LGGs). The aim of the study was to investigate the acute and long-term effects of vemurafenib on the lipid metabolism in children treated for an LGG. In our cohort, children treated with vemurafenib (n = 6) exhibited alterations in lipid metabolism a few weeks after starting, as was demonstrated after 1 month (n = 4) by the high plasma levels of the total cholesterol (TC = 221.5 ± 42.1 mg/dL), triglycerides (TG = 107.8 ± 44.4 mg/dL), and low-density lipoprotein (LDL = 139.5 ± 51.5 mg/dL). Despite dietary recommendations, the dyslipidemia persisted over time. The mean lipid levels of the TC (222.3 ± 34.7 mg/dL), TG (134.8 ± 83.6 mg/dL), and LDL (139.8 ± 46.9 mg/dL) were confirmed abnormal at the last follow-up (45 ± 27 months, n = 6). Vemurafenib could be associated with an increased risk of dyslipidemia. An accurate screening strategy in new clinical trials, and a multidisciplinary team, are required for the optimal management of unexpected adverse events, including dyslipidemia.

Background/Objectives: PET imaging with [¹⁸F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. Methods: In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [¹⁸F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [¹⁸F]F-DOPA uptake. A total of 73 subjects with treatment-naïve low- and high-grade gliomas, who underwent brain MRI within two weeks of [¹⁸F]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. Results: For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357); however, significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Conclusions: Our framework aids in analyzing [¹⁸F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

PET imaging with [18F]F-DOPA shows great promise for assessing paediatric gliomas. Manual tumour delineation and parameter extraction are time-consuming an