Cine Magnetic Resonance Imaging for Assessing Chest Wall Invasion in Lung Cancer: A Systematic Review and Meta-Analysis.

J Thorac ImagingOct 24, 2026 (epub)

Danilo de Oliveira Santana Ramos, Sarah Verdan Moreira, Meiri Andréia Maria da Silva et al.

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In 30 seconds

This systematic review and meta-analysis evaluated cine MRI for detecting chest wall invasion in lung cancer, using surgical and histopathologic findings as the reference standard. It included five prospective studies with 190 patients suspected of having chest wall invasion. Pooled sensitivity was 99% (95% CI: 34%-100%) and specificity was 74% (95% CI: 64%-82%); negative and positive predictive values were 100% (95% CI: 52%-100%) and 50% (95% CI: 39%-61%), respectively. The findings suggest cine MRI may help rule out invasion, although the confidence intervals were wide.

Key findings

  • Pooled sensitivity was 99% (95% CI: 34%-100%) for detecting chest wall invasion.
  • Specificity was 74% (95% CI: 64%-82%), with a positive predictive value of 50% (95% CI: 39%-61%).
  • Negative predictive value was 100% (95% CI: 52%-100%).
  • The meta-analysis included five prospective studies comprising 190 patients with suspected chest wall invasion.

Why it matters

Preoperative assessment of chest wall invasion can inform evaluation of lung cancer. The high pooled sensitivity and negative predictive value suggest cine MRI could be useful when CT or static MRI is inconclusive, particularly for ruling out invasion. However, the modest specificity and positive predictive value mean a positive cine MRI result should not be treated as definitive evidence of invasion.

What to keep in mind

Only five prospective studies and 190 patients were included, and confidence intervals were wide, reflecting a limited evidence base. The authors call for larger prospective, multicenter studies with standardized protocols.

Source

Published in J Thorac Imaging. This summary was written by xxcode from the publication's abstract and metadata. It is not peer reviewed and is not a substitute for the original article. For clinical decisions, review the original publication.

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AI-generated summaries may contain errors or omissions. Verify clinically important information with the original publication.

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