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Micro-ultrasound sensitivity 0.84 and specificity 0.41 for prostate cancer in five prospective studies

Micro-ultrasound sensitivity 0.84 and specificity 0.41 for prostate cancer in five prospective…
Photo by National Cancer Institute / Unsplash
Key Takeaway
Consider micro-ultrasound as a rule-out adjunct for prostate cancer, noting its high sensitivity but low specificity and modest post-test probability shifts.

This is a systematic review and diagnostic test accuracy meta-analysis of five prospective studies evaluating standalone 29-MHz micro-ultrasound as an index test for classifying clinically significant prostate cancer, using histopathology as the reference standard. The meta-analysis found a pooled sensitivity of 0.84 (95% CI 0.65-0.94) and a pooled specificity of 0.41 (95% CI 0.25-0.59). The positive likelihood ratio was 1.45 (95% CI 1.17-1.80), the negative likelihood ratio was 0.37 (95% CI 0.23-0.61), and the diagnostic odds ratio was 3.95 (95% CI 2.48-6.30). Post-test probability shifts were approximately 33% after a positive test and approximately 11% after a negative test.

The authors acknowledge substantial heterogeneity across studies, which they attribute to differences in thresholds and clinical spectrum. They note that spectrum-related factors were associated with lower specificity and that no covariate robustly altered sensitivity in exploratory analyses, given the small number of studies. The review highlights the need for standardized PRI-MUS thresholds, reader training, and larger multicenter studies.

Practice relevance supports micro-ultrasound as a complementary or triage adjunct, particularly when multiparametric MRI is unavailable, contraindicated, or delayed. The evidence is limited by heterogeneity and the small number of studies, and the findings should be interpreted cautiously.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedJun 2026
View Original Abstract ↓
BACKGROUND: Micro-ultrasound (micro-US; 29-MHz) offers real-time, high-resolution prostate imaging, but its stand-alone diagnostic accuracy remains uncertain. We synthesized prospective evidence to evaluate micro-US for classifying clinically significant prostate cancer (csPCa) using histopathology as the reference standard. METHODS: We searched PubMed, Embase, Scopus, and Web of Science (inception-20 May 2025) for prospective studies assessing micro-US as an index test on a diagnostic pathway. Data were pooled using random-effects models on logit-transformed sensitivity and specificity, with an HSROC representation and model diagnostics. Subgroup and meta-regression analyses explored heterogeneity, including threshold (PRI-MUS) and spectrum effects. Clinical utility was appraised using Fagan nomograms and a likelihood-ratio scatter. Small-study effects were evaluated with Deeks' test. RESULTS: Five prospective studies met criteria. Pooled sensitivity was 0.84 (95% CI 0.65-0.94) and pooled specificity was 0.41 (95% CI 0.25-0.59), indicating moderate discrimination on HSROC. Secondary metrics were concordant (PLR 1.45, 95% CI 1.17-1.80; NLR 0.37, 95% CI 0.23-0.61; DOR 3.95, 95% CI 2.48-6.30). On a 25% pre-test probability, the Fagan nomogram showed modest shifts (~ 33% after a positive test; ~11% after a negative), supporting a triage/rule-out role. Heterogeneity was substantial and strongly influenced by threshold and clinical spectrum differences; subgroup and meta-regression suggested that spectrum-related factors were associated with lower specificity, whereas no covariate robustly altered sensitivity (exploratory given small k). Model checks were acceptable, and Deeks' test showed no evidence of small-study effects (p ≈ 0.70). CONCLUSION: As a stand-alone index test for csPCa classification, micro-US demonstrates high sensitivity but low specificity, yielding modest impact on post-test probability. These findings support micro-US as a complementary/triage (rule-out) adjunct, particularly when mpMRI is unavailable, contraindicated, or delayed, while highlighting the need for standardized PRI-MUS thresholds, reader training, and larger multicenter studies to refine specificity and clarify integration with MRI-based pathways.
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