ReviewCureus2026
Quantitative Imaging of Microvascular Perfusion for Predicting Flap Survival in Reconstructive Surgery: A Systematic Review of Advanced Contrast-Based Techniques.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Flap failure due to microvascular perfusion compromise represents an important cause of morbidity in reconstructive surgery. Traditional monitoring methods are subjective and lack predictive accuracy. Advanced contrast-based quantitative imaging methods, particularly indocyanine green fluorescence angiography (ICG-FA) and contrast-enhanced ultrasound (CEUS), provide objective hemodynamic information. This systematic review summarizes the evidence for their predictive validity for flap survival. A systematic search of PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library was performed from inception up to February 1, 2026. Studies reporting quantitative perfusion parameters measured by ICG-FA or CEUS that were correlated with clinical outcomes (survival, necrosis) were included. Two reviewers independently screened titles/abstracts, conducted full-text reviews, extracted data, and evaluated risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, chosen for its specific design to assess diagnostic accuracy studies. Data were synthesized narratively and, where possible, using meta-analysis. Given the inclusion of both human and animal studies, a sensitivity analysis was planned, excluding animal studies to assess their impact on the primary pooled estimates. Of 2,583 identified records, 28 studies met inclusion criteria (19 ICG-FA, 9 CEUS), encompassing 1,583 flaps. The major quantitative parameters identified were time-to-peak (TTP), maximum fluorescence intensity (MFI), area under the curve (AUC), and inflow/outflow slope. Pooled analysis for ICG-FA demonstrated a significant association between prolonged TTP (mean difference (MD) = 4.2 seconds, 95% confidence interval (CI) 2.8-5.6, p < 0.001) and reduced AUC (MD = -112.3 arbitrary units [a.u.], 95% CI -154.1 to -70.5, p < 0.001) with subsequent flap compromise. For CEUS, decreased peak intensity and delayed wash-in were consistently correlated with necrosis. The sensitivity of quantitative ICG-FA for predicting necrosis ranged from 82-94%, and specificity ranged from 88-96%. Quantification protocols and threshold definitions were highly heterogeneous (I² > 75%). Quantitative parameters derived from ICG-FA and CEUS demonstrate significant correlations with flap viability and offer greater objectivity than qualitative assessment. Standardization of imaging procedures and diagnostic thresholds is urgently needed to transform these promising techniques into validated, reliable clinical decision-support systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.