Imaging-Based Classification of Fifth Metatarsal Fractures and Its Impact on Clinical Decision-Making: A Systematic Review

TL;DR

This review examines how imaging-based classifications help diagnose and manage fifth metatarsal fractures, improving treatment decisions and outcomes.

Contribution

The study systematically evaluates the reliability and clinical impact of imaging-based classification systems for fifth metatarsal fractures.

Findings

Structured imaging classifications improve interobserver agreement and treatment standardization.

Imaging-integrated approaches using CT or MRI enhance accuracy for subtle or stress-related fractures.

Existing systems correlate with functional outcomes and aid in risk stratification.

Abstract

Fifth metatarsal fractures are common forefoot injuries with variable healing potential and treatment outcomes depending on fracture location and type. Imaging-based classification systems provide radiographic frameworks to guide diagnosis, prognostication, and management, yet their reliability can vary. This systematic review evaluated existing imaging-based classifications for fifth metatarsal fractures, focusing on anatomical and radiographic criteria, reproducibility, and clinical impact. A comprehensive search of PubMed, Embase, Scopus, and the Cochrane Library was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, including studies reporting reliability or clinical relevance of imaging-based classifications. Findings indicate that structured imaging classifications improve interobserver agreement, facilitate standardized treatment decisions, and correlate with functional outcomes. While traditional systems provide foundational guidance, imaging-integrated approaches, especially using CT or MRI, enhance accuracy in subtle or stress-related fractures. In conclusion, imaging-based classifications are essential for accurate diagnosis, risk stratification, and management of fifth metatarsal fractures, and future research should focus on standardized, multimodality frameworks to optimize clinical outcomes.

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