Related Experiment Video
Updated: Sep 11, 2026
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Ultrafast breast MRI to differentiate between molecular subtypes of breast cancer: a systematic review and
Hanna Terhaar1, Brittany Duck1, Priyanka Mitta1
1University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL 35294, United States.
Background:
Ultrafast dynamic contrast-enhanced MRI (UF-MRI) offers high temporal resolution and early kinetic information that may extend beyond lesion discrimination to molecular subtype differentiation in breast cancer. Conventional MRI has been more extensively studied, but whether UF-MRI provides comparable or superior diagnostic accuracy remains unclear.
Objective:
To systematically review and synthesize the evidence on the diagnostic accuracy of UF-MRI in differentiating breast cancer molecular subtypes with pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), and key kinetic parameters.
Evidence Acquisition:
A systematic search of PubMed, Scopus, and Embase through 2025 identified studies reporting UF-MRI performance stratified by subtype (Luminal, HER2-enriched, TNBC) in both screening and diagnostic extent-of-disease (EOD) breast MRI populations. Data extraction included study characteristics, diagnostic outcomes, and kinetic parameters (time to enhancement, TTE, and maximum slope, MaxSlope). Meta-analyses were performed using random-effects models to account for between-study heterogeneity with heterogeneity assessed by I². Meta-regression examined covariates including scanner vendor, sequence type, and reader.
Evidence Synthesis:
Nine studies including 1,371 lesions met inclusion criteria. Luminal cancers showed the highest pooled accuracy (sensitivity 0.90, 95% CI: 0.81-0.95; specificity 0.79, 95% CI: 0.65-0.89; DOR 25.80, 95% CI: 10.15-65.59; area under the curve [AUC] 0.87, 95% CI: 0.81-0.92), followed by HER2-enriched (sensitivity 0.85, 95% CI: 0.68-0.94; specificity 0.67, 95% CI: 0.10-0.97; DOR 15.03, 95% CI: 7.72-29.28; AUC 0.81, 95% CI: 0.69-0.89) and TNBC (sensitivity 0.82, 95% CI: 0.74-0.88; specificity 0.72, 95% CI: 0.69-0.75; DOR 10.09, 95% CI: 6.44-15.82, AUC 0.81, 95% CI: 0.72-0.85). Meta-regression identified scanner vendor and sequence type as contributors to heterogeneity. TTE was shortest for TNBC (7.86 s, 95% CI: 4.85-10.86) compared with HER2-enriched (9.67 s, 95% CI: 7.01-12.33) and Luminal (9.78 s, 95% CI: 8.00-11.56). MaxSlope showed extreme heterogeneity with poor reproducibility, precluding reliable interpretation of pooled estimates.
Conclusion:
UF-MRI demonstrates subtype-specific diagnostic performance for differentiating breast cancer molecular subtypes, with the strongest discriminatory accuracy in Luminal tumors and diagnostic accuracy patterns and kinetic parameters aligned with known differences in enhancement behavior across subtypes, high sensitivity but modest specificity in TNBC, and more variable performance in HER2-enriched cancers. Time to enhancement (TTE) appears to be a reliable and interpretable kinetic marker, whereas maximum slope (MaxSlope) shows extreme heterogeneity and limited reproducibility, restricting its clinical utility.
Clinical Impact:
This meta-analysis supports UF-MRI as a rapid imaging technique with potential utility for molecular subtype assessment in breast cancer. UF-MRI may complement existing breast MRI protocols by providing early kinetic information without substantially increasing scan time. Standardization of protocols is essential to reduce heterogeneity, improve reproducibility, and strengthen its role in precision breast imaging.
Key Finding:
UF-MRI demonstrated the highest overall diagnostic accuracy in Luminal tumors, reflected by the highest pooled sensitivity (0.90), specificity (0.79), and diagnostic odds ratio (25.8). Diagnostic performance was intermediate in HER2-enriched tumors (sensitivity 0.85, specificity 0.67, DOR 15.0) and lowest in TNBC (sensitivity 0.82, specificity 0.72, DOR 10.1). Time to enhancement (TTE) consistently reflected subtype-specific kinetic differences, whereas MaxSlope showed high variability and limited reliability.
Importance:
UF-MRI offers important subtype-specific diagnostic accuracy without costing additional scan time, making it a valuable addition to the evaluation of breast cancer on MRI.
