Whole-body magnetic resonance imaging evaluation of facioscapulohumeral muscular dystrophy.

INTRODUCTION: Facioscapulohumeral muscular dystrophy (FSHD) is a hereditary disorder that causes progressive muscle wasting. Increasing knowledge of the pathophysiology of FSHD has stimulated interest in developing biomarkers of disease severity. METHODS: Two groups of MRI scans were analyzed: whole-body scans from 13 subjects with FSHD; and upper and lower extremity scans from 34 subjects with FSHD who participated in the MYO-029 clinical trial. Muscles were scored for fat infiltration and edema-like changes. Fat infiltration scores were compared with muscle strength and function. RESULTS: The analysis revealed a distinctive pattern of both frequent muscle involvement and frequent sparing in FSHD. Averaged fat infiltration scores for muscle groups in the legs correlated with quantitative muscle strength and 10-meter walk times. CONCLUSIONS: Advances in MRI technology allow for acquisition of rapid, high-quality, whole-body imaging in diffuse muscle disease. This technique offers a promising disease biomarker in FSHD and other muscle diseases.

Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images.

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.