The Quantification Paradox in Gynecologic Color Doppler Ultrasound: From Spectral Indices to Microvascular Imaging and Artificial Intelligence.

Color Doppler ultrasound has long promised to convert tumor vascularity into an objective and reproducible measure for differentiating benign from malignant gynecologic disease. The historical record is more complicated. Quantitative spectral indices such as the resistance index, pulsatility index and peak systolic velocity were repeatedly proposed as objective discriminators, but their cutoffs did not become stable clinical standards. What entered major adnexal-mass systems was instead a coarse visual color score, used within structured multivariable frameworks such as the International Ovarian Tumor Analysis models and the Ovarian-Adnexal Reporting and Data System. New technologies-superb microvascular imaging, contrast-enhanced ultrasound, radiomics and deep learning-now reopen the old ambition of vascular quantification. This narrative review reorganizes gynecologic Doppler literature around measurement rather than disease category. It proposes that a major limiting problem may have been reproducibility, not signal content. High-resolution vascular features are more likely to become clinically useful when operator, machine, acquisition and population variance are controlled. The practical agenda for Doppler innovation should therefore prioritize standardized acquisition, reproducibility reporting, calibration, external validation and task-specific deployment over another isolated high-AUC or high-resolution vascular biomarker. Literature was identified through PubMed/MEDLINE searches (inception to 31 May 2026) combining gynecologic ultrasound with Doppler, O-RADS/IOTA, SMI, CEUS, radiomics, artificial intelligence and reproducibility; citation chaining was also used, with gynecologic evidence prioritized.