Charcot-Marie-Tooth Disease type 4C: Novel mutations, clinical presentations, and diagnostic challenges.

INTRODUCTION: This study analyzes and describes atypical presentations of Charcot-Marie-Tooth disease type 4C (CMT4C). METHODS: We present clinical and physiologic features of 5 patients with CMT4C caused by biallelic private mutations of SH3TC2. RESULTS: All patients manifested scoliosis, and nerve conduction study indicated results in the demyelinating range. All patients exhibited signs of motor impairment within the first years of life. We describe 2 or more different genetic diseases in the same patient, atypical presentations of CMT, and 3 new mutations in CMT4C patients. DISCUSSION: A new era of unbiased genetic testing has led to this small case series of individuals with CMT4C and highlights the recognition of different genetic diseases in CMT4C patients for accurate diagnosis, genetic risk identification, and therapeutic intervention. The phenotype of CMT4C, in addition, appears to be enriched by a number of features unusual for the broad CMT category. Muscle Nerve 57: 749-755, 2018.

Holistic segmentation of the lung in cine MRI.

Duchenne muscular dystrophy (DMD) is a childhood-onset neuromuscular disease that results in the degeneration of muscle, starting in the extremities, before progressing to more vital areas, such as the lungs. Respiratory failure and pneumonia due to respiratory muscle weakness lead to hospitalization and early mortality. However, tracking the disease in this region can be difficult, as current methods are based on breathing tests and are incapable of distinguishing between muscle involvements. Cine MRI scans give insight into respiratory muscle movements, but the images suffer due to low spatial resolution and poor signal-to-noise ratio. Thus, a robust lung segmentation method is required for accurate analysis of the lung and respiratory muscle movement. We deployed a deep learning approach that utilizes sequence-specific prior information to assist the segmentation of lung in cine MRI. More specifically, we adopt a holistically nested network to conduct image-to-image holistic training and prediction. One frame of the cine MRI is used in the training and applied to the remainder of the sequence ([Formula: see text] frames). We applied this method to cine MRIs of the lung in the axial, sagittal, and coronal planes. Characteristic lung motion patterns during the breathing cycle were then derived from the segmentations and used for diagnosis. Our data set consisted of 31 young boys, age [Formula: see text] years, 15 of whom suffered from DMD. The remaining 16 subjects were age-matched healthy volunteers. For validation, slices from inspiratory and expiratory cycles were manually segmented and compared with results obtained from our method. The Dice similarity coefficient for the deep learning-based method was [Formula: see text] for the sagittal view, [Formula: see text] for the axial view, and [Formula: see text] for the coronal view. The holistic neural network approach was compared with an approach using Demon's registration and showed superior performance. These results suggest that the deep learning-based method reliably and accurately segments the lung across the breathing cycle.