Abstract
Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones.Materials and Methods: A total of 9 patients (age range 8–15 years) with CP and 12 healthy controls (age range 9–16 years) were prospectively enrolled (2018–2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance.Results: In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FFS 9.1%, FFGM 8.5%, and FFGL 10.2%) than control group (FFS 3.3%, FFGM 4.1%, FFGL 6.6%).Conclusion: The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due to CP in a non-invasive manner.
Highlights
Cerebral palsy (CP) is the most common cause of physical disability in childhood, caused by brain injury during the antenatal or early postnatal period [1]
Primary damage occurs in the central nervous system, clinical symptoms are mostly associated with the peripheral neuromuscular system, with skeletal muscles [2]
The fat fraction (FF) difference between dominant and non-dominant legs in the control group was not apparent for S and gastrocnemius medialis (GM) muscles, whereas the difference was pronounced for gastrocnemius lateralis (GL)
Summary
Cerebral palsy (CP) is the most common cause of physical disability in childhood, caused by brain injury during the antenatal or early postnatal period [1]. Muscle pathologies occur due to spasticity and contractures, and so far, those pathologies are assessed by either clinical scoring systems, e.g., modified Ashworth scale (MAS), or invasive procedures, e.g., biopsies [2]. In neuromuscular muscle diseases, quantitative MRI methods have already become standard for disease monitoring [5,6,7,8]. Among these quantification methods, fat fraction (FF) measurement is commonly employed to determine fatty infiltration in a muscle, providing insights into function and pathophysiology [6, 7, 9, 10]. We aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones
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