Classification systems for assessing acute muscle injuries: a retrospective comparison of inter-reader agreements

Oliver A Binkert1, Christian W A Pfirrmann2, Sonja Fierstra2

  • 1Faculty of Medicine, University of Zurich, Zurich, Switzerland.

Skeletal Radiology
|August 13, 2025
PubMed
Abstract

Insights

The Munich Consensus Injury Classification (MCIC) and British Athletics Muscle Injury Classification (BAMIC) show moderate reliability for MRI grading of acute muscle injuries. The Chan et al. Injury Classification (CIC) demonstrates fair reliability, with challenges in localizing injuries within muscles.

Area of Science:

  • Musculoskeletal Radiology
  • Medical Imaging Analysis
  • Sports Medicine Diagnostics

Background:

  • Accurate MRI grading of acute muscle injuries is crucial for effective treatment and rehabilitation.
  • Several classification systems exist, but their inter-reader reliability for MRI findings needs comparative analysis.

Purpose of the Study:

  • To compare the inter-reader reliability of three common MRI classification systems for acute muscle injuries: BAMIC, MCIC, and CIC.
  • To identify which classification system offers the best reproducibility in grading muscle injuries.

Main Methods:

  • Four musculoskeletal radiologists independently graded 111 acute muscle injuries using BAMIC, MCIC, and CIC.
  • Inter-reader reliability was quantified using Fleiss' Kappa (κ) and 95% confidence intervals (CI).

Main Results:

  • MCIC (κ=0.566) and BAMIC (κ=0.506) demonstrated moderate inter-reader reliability, while CIC (κ=0.306) showed fair reliability.
  • Reproducibility was higher for injury severity (κ=0.594-0.696) than for injury location within the muscle (κ=0.349-0.576).
  • MCIC showed the highest reproducibility for non-hamstring thigh injuries (κ=0.749), and CIC showed the lowest for lower leg injuries (κ=0.199).

Conclusions:

  • MCIC and BAMIC offer moderate reliability for MRI grading of acute muscle injuries.
  • CIC exhibits fair reliability, with significant challenges in anatomically localizing injuries within the muscle.
  • Improving the reproducibility of anatomical localization is key for enhancing muscle injury classification systems.