The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mainly a subjective process and difficult to quantify. A study published in the Orphanet Journal of Rare Diseases tested the ability of network science to provide an innovative framework for deriving useful information from muscle biopsies. To this end, the authors are developing a new method that analyzes muscle samples objectively, automatically, quickly, and accurately. Their database of 102 muscle biopsy images taken from 70 individuals, which included controls, those with neurogenic atrophy and those with muscular dystrophies, enabled them to define a new method of Computerized Image Analysis for Neuromuscular Diseases (NDICIA), which uses scientific network analysis to identify the characteristic signature of muscle biopsy images. NDICIA characterizes muscle tissues by representing each image as a network, with fibers serving as nodes and fiber contacts as links.
The authors reported that, after a "training" phase with verification of the pathological biopsies involved, NDICIA was able to quantify the degree of pathology in each sample. This method was validated by comparing the quantification of the severity of muscular dystrophies provided by NDICIA with the assessment made by a pathologist. The comparison revealed a strong correlation. The authors point out that this approach can be used to quantify new images without the need for prior 'training'. According to the authors, this study demonstrates that scientific network analysis captures useful information contained in muscle biopsies, aiding in the diagnosis of muscular dystrophies and neurogenic atrophies. The authors believe that this novel network analysis approach will be a useful and valuable tool for assessing the etiology of muscular dystrophies or neurogenic atrophies with the potential to quantify treatment outcomes in preclinical and clinical studies.