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Muscle injuries of the lower limbs are currently the most common sport-related injuries, the impact of which is particularly significant in elite athletes. MRI is the imaging modality of choice in assessing acute muscle injuries and radiologists play a key role in the current scenario of multidisciplinary health care teams involved in the care of elite athletes with muscle injuries. Despite the frequency and clinical relevance of muscle injuries, there is still a lack of uniformity in the description, diagnosis, and classification of lesions. The characteristics of the connective tissues (distribution and thickness) differ among muscles, being of high variability in the lower limb. This variability is of great clinical importance in determining the prognosis of muscle injuries. Recently, three classification systems, the Munich consensus statement, the British Athletics Muscle Injury classification, and the FC Barcelona-Aspetar-Duke classification, have been proposed to assess the severity of muscle injuries. A protocolized approach to the evaluation of MRI findings is essential to accurately assess the severity of acute lesions and to evaluate the progression of reparative changes. Certain MRI findings which are seen during recovery may suggest muscle overload or adaptative changes and appear to be clinically useful for sport physicians and physiotherapists.

BackgroundMuscle injuries are one of the main daily problems in sports medicine, football in particular. However, we do not have reliable means to predict the outcome, i.e. return to play (RTP) from severe injury. The aim of the present study was to evaluate the capability of the MLG-R classification system to grade hamstring muscle injuries by severity, offer a prognosis for the RTP, and identify injuries with higher risk of reinjury. And to assess the consistency of our proposed system by investigating its intra-and inter-observer reliability. MethodsAll male professional football players from FC Barcelona (FCB)-senior A and B and the two U-19 teams-with injuries occurred between February 2010 and February 2020 were reviewed. Only players with clinical presentation of hamstring muscle injury, with complete clinic information and magnetic resonance images (MRI), were included. Three different statistical and machine learning approaches (linear regression, random forest, and XGBoost) were used to assess the importance of each factor of the MLG-R classification system in determining the RTP as well as to offer a prediction of the expected RTP. We used the Cohen's kappa and the intraclass correlation coefficient (ICC) to assess the intra-and interobserver reliability. ResultsBetween 2010 and 2020, 76 hamstring injuries corresponding to 42 different players were identified, of which 50 (65.8%) were Grade 3 r , 54 (71.1%) affected the biceps femoris long head (BFlh), and 33 of the 76 (43.4%) were located at the proximal myotendinous junction (MTJ). The mean RTP for Grade 2, 3, and 3 r injuries were 14.3, 12.4, and 37 days, respectively. Injuries affecting the proximal MTJ had a mean RTP of 31.7 days while those affecting the distal part of the MTJ had a mean RTP of 23.9 days. The analysis of the grade 3r BFlh injuries located at the FT showed a median RTP time of 56 days while the injuries located at the central tendon had a shorter RTP of 24 days (p=0.038). The statistical analysis showed an excellent predictive power of the MLG-R classification system with a mean absolute error of 9.8 days and an R-squared of 0.48. The most important factors to determine the RTP were if the injury was at the free tendon (FT) of the BFlh or if it was a Grade 3 r injury. For all the items of the MLG-R classification the intra-and inter-observer reliability was excellent (k > 0.93) except for fibers blurring (κ = 0.68). ConclusionThe main determinant for long RTP after hamstring injury is the injury affecting the connective tissue structures of the hamstring.We developed a reliable hamstring muscle injury classification system based on MRI that showed excellent results in terms of reliability, prognosis capability and objectivity. It is easy to use in clinical 2 daily practice, and can be further adapted to future knowledge. The adoption of this system by the medical community would allow to uniform diagnosis leading to better injury management.

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