Background: Lynch syndrome (LS) is an autosomal dominant cancer predisposition syndrome caused by a germline pathogenic variant, or epigenetic silencing, of a mismatch repair (MMR) gene, leading to a wide cancer spectrum with gene-specific penetrance. Ascertainment, assessment and testing of LS individuals is complex. A Canadian national guideline is needed to ensure equitable access to patient care across the country.
View Article and Find Full Text PDFSchwannomatoses (SWN) are distinct cancer predisposition syndromes caused by germline pathogenic variants in the genes NF2, SMARCB1, or LZTR1. There is significant clinical overlap between these syndromes with the hallmark of increased risk for cranial, spinal and peripheral schwannomas. Neurofibromatosis type 2 was recently renamed as NF2-related SWN and is the most common SWN syndrome with increased risk for bilateral vestibular schwannomas, intradermal schwannomas, meningiomas and less commonly ependymoma.
View Article and Find Full Text PDFPurpose: Constitutional mismatch repair deficiency (CMMRD) is a genetic cancer predisposition syndrome among children and young adults. This study aimed to evaluate the frequency of CMMRD among patients with pediatric high-grade glioma (pHGG) in a single tertiary care center in Pakistan, a country with high consanguinity rates.
Patients And Methods: We reviewed the data of patients age <18 years with pHGG, anaplastic astrocytoma, and diffuse midline glioma (DMG) with CMMRD testing between 2016 and 2023.
Background: Gliomas are a major cause of cancer-related death among children, adolescents, and young adults (age 0-40 years). Primary mismatch repair deficiency (MMRD) is a pan-cancer mechanism with unique biology and therapeutic opportunities. We aimed to determine the extent and impact of primary MMRD in gliomas among children, adolescents, and young adults.
View Article and Find Full Text PDFMedical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.
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