This study documents a real-world progressive and significant loss of LCM efficacy over time in a pediatric population. Further prospective studies on larger populations are required to confirm the remarkable loss of LCM efficacy over time.
We aimed to investigate the significance of optical coherence tomography (SD-OCT) in managing pediatric optic pathway gliomas (OPGs) in children younger than 5 years of age. A retrospective monocentric study was conducted. SD-OCT scans were obtained using the handheld iVue system to assess peripapillary retinal nerve fibre layer (pRNFL) thickness at three time points: baseline (OCT1), end of treatment (OCT2), and at last follow-up (OCT3). We compared the median value of pRNFL (and interquartile range—IQR) at different follow-up times and in different sub-groups (stable disease—SD, partial response—PR, and progression disease—PD). Thirteen children younger than 5 years of age were included. The Median follow-up time was 3.9 years (IQR 1.2). Six patients showed a pRNFL change of more than 10% during follow-up. Seven patients showed PD during follow-up. Median pRNFL at baseline was 81.5 µm (IQR 31.5); median pRNFL at the end of treatment was 73 µm (IQR 33); median pRNFL at last follow-up was 72 µm (IQR 38.5). The mean pRNFL at baseline was significantly lower than the mean normative values. Only subjects with PD showed pRNFL change close to statistical significance. This study confirms the role of SD-OCT in managing OPGs for therapeutic decisions and strategy planning of visual rehabilitation.
Chiari I malformation is characterized by the herniation of cerebellar tonsils below the foramen magnum. It is often accompanied by syringomyelia and neurosurgical management is still controversial. In fact, it is frequent that some symptomatic patients initially undergo bony decompression of the posterior fossa and need in a short time more invasive surgery with higher morbility (e.g., decompression of posterior fossa with dural plastic, with or without tonsillar coarctation) because of unsatisfactory results at MRI controls. This study proposes a machine learning approach (based on SVM classifier), applied to different morphometric indices estimated from sagittal MRI and some information on the patient (i.e., age and symptoms at diagnosis), to recognize patients with higher risk of syringomyelia and clinical deterioration. Our database includes 58 pediatric patients who underwent surgery treatment. A negative outcome at 1 year from the intervention was observed in 38% of them (accuracy of 62%). Our algorithm allows us to increase the accuracy to about 71%, showing it to be a valid support to neurosurgeons in refining the clinical picture.
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