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Background: Caused by duplications of the gene encoding peripheral myelin protein 22 (PMP22), Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common hereditary neuropathy. Despite this shared genetic origin, there is considerable variability in clinical severity. It is hypothesized that genetic modifiers contribute to this heterogeneity, the identification of which may reveal novel therapeutic targets. In this study, we present a comprehensive analysis of clinical examination results from 1564 CMT1A patients sourced from a prospective natural history study conducted by the RDCRN-INC (Inherited Neuropathy Consortium). Our primary objective is to delineate extreme phenotype profiles (mild and severe) within this patient cohort, thereby enhancing our ability to detect genetic modifiers with large effects., Methods: We have conducted large-scale statistical analyses of the RDCRN-INC database to characterize CMT1A severity across multiple metrics., Results: We defined patients below the 10th (mild) and above the 90th (severe) percentiles of age-normalized disease severity based on the CMT Examination Score V2 and foot dorsiflexion strength (MRC scale). Based on extreme phenotype categories, we defined a statistically justified recruitment strategy, which we propose to use in future modifier studies., Interpretation: Leveraging whole genome sequencing with base pair resolution, a future genetic modifier evaluation will include single nucleotide association, gene burden tests, and structural variant analysis. The present work not only provides insight into the severity and course of CMT1A, but also elucidates the statistical foundation and practical considerations for a cost-efficient and straightforward patient enrollment strategy that we intend to conduct on additional patients recruited globally., (© 2024 Peripheral Nerve Society.)

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (r S ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (r S  = -0.21, p = 0.001), Decipher (r S  = -0.193, p = 0.003), and three-tier risk (r S  = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times., (© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)