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

Across the three eras, the same measurement principle recurs ( Table 1 ). Spectral Doppler offered high nominal resolution but unstable sampling. Color score offered low nominal resolution but greater practical robustness when embedded in multivariable systems. SMI, CEUS and AI now offer higher spatial or computational resolution, but they are likely to change practice only if they reduce variance rather than merely extract more signal. Table 1 Measurement Logic of Gynecologic Vascular Ultrasound Across Historical Eras Era Modality/Approach Primary Vascular Endpoint Clinical Promise Dominant Reproducibility Problem Most Defensible Current Role I Spectral Doppler RI, PI, PSV and derived waveform thresholds Objective numeric discrimination of benign versus malignant gynecologic disease Sampling-site dependence, vessel-selection bias, threshold non-transportability, and operator-coupled variance Adjunct confirmation of true flow rather than stand-alone malignancy triage II Morphology-first structured assessment Gray-scale morphology, locularity, solid components, papillary projections, ascites Standardized risk stratification when vascularity alone is insufficient Reader experience and descriptor interpretation, partly mitigated by lexicon standardization Core basis of IOTA and O-RADS risk assignment II Subjective color-score Four-level visual color score: no, minimal, moderate, strong flow Low-resolution but robust vascular descriptor embedded within multivariable systems Middle-category disagreement and dependence on color settings Pragmatic vascular input within O-RADS and IOTA-based systems III Microvascular imaging SMI or related vascular index from color-pixel fraction or microvessel visualization Higher sensitivity to low-velocity microvascular flow Gain, ROI placement, depth, frame rate, segmentation, and device dependence Within-patient serial change, spatial depiction, and selected cervical/endometrial applications III CEUS Enhancement pattern, time-intensity parameters, peak intensity, time to peak True intravascular perfusion assessment and clarification of indeterminate solid components Contrast timing, TIC acquisition, ROI placement, and protocol heterogeneity Adjunctive problem solving for indeterminate adnexal lesions and vascular mapping III Radiomics / AI Image-derived features, learned vascular representations, calibrated risk probabilities Automated integration of morphology and Doppler information Single-center overfitting, scanner/gain confounding, weak calibration, and limited multivendor validation Variance reduction, segmentation standardization, acquisition quality control, external validation Abbreviations : AI, artificial intelligence; CEUS, contrast-enhanced ultrasound; IOTA, International Ovarian Tumor Analysis; O-RADS, Ovarian-Adnexal Reporting and Data System; PI, pulsatility index; PSV, peak systolic velocity; RI, resistance index; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve. Measurement Logic of Gynecologic Vascular Ultrasound Across Historical Eras Abbreviations : AI, artificial intelligence; CEUS, contrast-enhanced ultrasound; IOTA, International Ovarian Tumor Analysis; O-RADS, Ovarian-Adnexal Reporting and Data System; PI, pulsatility index; PSV, peak systolic velocity; RI, resistance index; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve. Viewed as a bias-variance problem, this history also explains why low-bias continuous estimators can fail when their variance is uncontrolled, whereas coarser estimators may become clinically useful when their variance is contained ( Figure 2 ). Figure 2 Bias-variance interpretation of the three measurement eras. Spectral Doppler appeared objective and potentially low-bias but suffered from high operator- and setting-dependent variance. The visual color score is coarse but comparatively repeatable when embedded in structured systems. The re-quantification era of SMI, CEUS and AI must demonstrate controlled variance before these methods can serve as stand-alone estimators. The dartboards are illustrative and not derived from measured data. The question mark and question-mark suffixes indicate that Era III bias and variance remain unproven rather than measured. Three diagrams compare bias and variance across three measurement eras. Bias-variance interpretation of the three measurement eras. Spectral Doppler appeared objective and potentially low-bias but suffered from high operator- and setting-dependent variance. The visual color score is coarse but comparatively repeatable when embedded in structured systems. The re-quantification era of SMI, CEUS and AI must demonstrate controlled variance before these methods can serve as stand-alone estimators. The dartboards are illustrative and not derived from measured data. The question mark and question-mark suffixes indicate that Era III bias and variance remain unproven rather than measured. Reported reliability estimates are limited but informative: for IETA endometrial descriptors, weighted kappa for color score was 0.77 among experts and 0.69 among non-experts, versus 0.35 and 0.32 for a seven-category vascular pattern; for endometrial MVFI, interobserver weighted kappa was 0.721–0.725. 29 , 40 Breast SMI studies reported interobserver ICCs of 0.722–0.948, but these extra-gynecologic results provide technology-level context and cannot establish gynecologic performance. 34 , 35 The distinction between resolution and reproducibility also changes how new studies should be judged. A new Doppler endpoint should not be promoted because it has a statistically significant difference between benign and malignant groups. It should be promoted only after showing acceptable interobserver agreement, intraobserver agreement, acquisition stability, between-machine transportability, calibration and external validation.

The current era has reopened the original ambition. Microvascular flow imaging, CEUS and AI all promise to recover objectivity at higher resolution. The correct question for each is not merely whether it detects more vessels or features. The correct question is whether the additional information remains stable across operators, equipment, acquisition presets and populations. In endometrial imaging, standardized vocabulary preceded new microvascular technology. The IETA consensus established terms for endometrial and intrauterine lesions, including color score and vascular pattern. 28 IETA-based studies later evaluated interobserver agreement and showed that even standardized visual features require reliability testing. 29 Recent IETA work demonstrated that endometrial cancer, polyps and other intracavitary lesions can be described by recurring ultrasound patterns, but those patterns must be tied to reproducible terminology. 30 Older Doppler work on focal endometrial disease supports a pattern-based rather than threshold-based approach. Power Doppler mapping helped distinguish endometrial polyps from submucosal fibroids by recognizing single-vessel and rim-like vascular patterns. 31 Color Doppler sonohysterography similarly suggested that vascular architecture can help differentiate polyps from submucosal fibroids. 32 These studies are important because they show that the meaningful vascular unit may be architecture, not a single continuous cutoff. Superb microvascular imaging and related modes are appealing because they suppress clutter and display low-velocity flow that conventional Doppler may miss. Early SMI validation in thyroid and breast imaging provides extra-gynecologic, technology-level context only and cannot establish gynecologic performance. 33 Quantitative vascular index measurements have shown that even an automated-looking SMI scalar can be sensitive to reproducibility constraints. 34 A recent breast-lesion study further emphasized that SMI vascular index may have limited diagnostic value even when reproducibility is measured carefully. 35 Gynecologic SMI evidence is now emerging. Quantitative SMI has been investigated for cervical lesions and suggested potential diagnostic value. 36 Serial SMI vascularity measurements have also been used to monitor chemoradiotherapy response in locally advanced cervical cancer. 37 SMI visualization in cervical cancer has also been associated with histologic microvessel densities, supporting biological plausibility. 38 Comparative work suggests that quantified SMI and quantified CEUS may both assess cervical cancer vascularization. 39 Microvascular flow imaging has also been studied for endometrial carcinoma detection and compared with conventional color Doppler imaging. 40 The implication is narrower than enthusiastic technology language often suggests. SMI is most defensible where the task is within-patient change or spatial depiction. Cross-sectional VI thresholds for malignancy triage remain vulnerable to gain, depth, frame rate, region-of-interest placement and scanner effects. CEUS changes the vascular signal because microbubbles are intravascular and enhancement can separate true perfusion from debris or artifact. O-RADS combined with CEUS improved specificity and positive predictive value for malignant adnexal tumor diagnosis in one study. 41 A prospective multicenter CEUS risk-stratification model for adnexal masses with solid components reported stronger diagnostic value than conventional assessment alone. 42 A meta-analysis found that O-RADS combined with CEUS improved diagnostic accuracy for ovarian adnexal masses. 43 A newer integrated model combining O-RADS US v2022, CEUS and CA125 illustrates how CEUS is being folded into multivariable rather than single-threshold risk assessment. 44 CEUS therefore fits the central thesis in a different way. Its most reproducible value may often be categorical and morphology-anchored: enhancement is present or absent, enhancement occurs earlier or later than the myometrium, and solid tissue enhances or does not enhance. Time-intensity curve parameters can be informative, but their acquisition sensitivity means that they require the same standardization discipline that spectral Doppler lacked. Representative appearances should be interpreted by modality: spectral Doppler as waveform-derived indices, O-RADS as a 1–4 visual color score, SMI as low-velocity microvascular depiction, and CEUS as enhancement timing and pattern; values should not be compared directly across modalities. Cervical cancer illustrates why the same vascular measurement can be weak for one task and useful for another. Transvaginal color Doppler was investigated as a predictor of response to concurrent chemoradiotherapy. 45 Two-dimensional and three-dimensional ultrasound have been used to assess early response to neoadjuvant chemotherapy in locally advanced cervical cancer. 46 Three-dimensional power Doppler imaging has also been used to quantify vascularization in early-stage cervical cancer. 47 Earlier work on a vascularity index for cervical carcinoma connected in vivo Doppler quantification to angiogenesis assessment. 48 This body of work supports a pragmatic distinction. A one-time malignancy threshold in a heterogeneous adnexal population is variance-intolerant. Serial monitoring in the same patient is more variance-tolerant because each patient partially serves as her own control. For this reason, continuous SMI VI or CEUS time-intensity analysis may be more clinically credible in treatment monitoring than in initial cross-sectional triage. AI is the newest form of re-quantification. A review of AI applied to ultrasound in gynecologic oncology found growing interest but also emphasized the need for better validation. 49 A systematic review of AI in benign gynecologic ultrasound found that most studies focused on classification or automatic measurement tasks and that external validation was often absent. 50 A systematic review of AI ultrasound for gynecological tumors reported promising diagnostic performance but substantial heterogeneity across studies. 51 Broader ultrasound AI reviews note that ultrasound is particularly operator-dependent, which makes acquisition variability a central translational barrier. 52 The measurement lens disciplines the interpretation of these studies. A model trained on internally consistent images can learn scanner, gain, color-map or annotation conventions rather than transportable vascular biology. A high internal area under the receiver operating characteristic curve (AUC) is therefore not enough. The relevant endpoints are prospective external discrimination, calibration, decision-curve utility, scanner robustness and performance under acquisition variation. Reporting standards are beginning to catch up. The CLAIM checklist gives authors and reviewers a structured framework for medical-imaging AI reporting. 53 The 2024 CLAIM update reflects the rapid evolution of imaging AI and strengthens expectations around data, model development and evaluation. 54 Radiomics has its own standardization problem, and the Image Biomarker Standardisation Initiative was developed to harmonize quantitative feature definitions. 55 Ultrasound radiomics research has shown that acquisition parameters, scanners and segmentation location can substantially affect feature reproducibility. 56

Few imaging signals are as intuitively compelling as blood flow within an adnexal, endometrial or cervical lesion. Tumor angiogenesis was classically framed as a necessary biological support for solid tumor growth and a therapeutic target. 1 The later oncology literature consolidated this concept by framing angiogenesis as a broad feature of malignant progression. 2 Gynecologic Doppler ultrasound offered an appealing bedside translation of that biology: if vascularity reflects angiogenesis, and angiogenesis reflects malignant behavior, then a sufficiently careful vascular measurement should improve triage. Here, the quantification paradox denotes the contrast between increasingly precise vascular measurements and the clinical durability of a coarse color score embedded in standardized multivariable systems. The earliest color Doppler work in gynecology shared that ambition. Doppler ultrasound was proposed as a repeatable noninvasive method for assessing tumor vascularity in gynecologic disorders. 3 Early transvaginal color Doppler scoring schemes then sought to classify ovarian malignancy by combining morphologic findings with vascular indices and velocity measurements. 4 Resistance and pulsatility indices were attractive because they appeared to convert a vascular impression into a continuous, machine-generated scalar. 5 The paradox is that this apparently objective endpoint did not become the dominant clinical endpoint. Direct comparisons showed that morphology often outperformed spectral waveform analysis for diagnosing ovarian malignancy. 6 Transvaginal color Doppler studies reported increased flow and lower impedance in many malignant tumors, but they also documented overlap with benign and inflammatory lesions. 7 Ovarian tumor screening work reached the same practical message: Doppler flow contained signal, but the signal was not specific enough to act as an independent decision rule. 8 What changed practice was not a more precise vascular number. Morphology indexing demonstrated that structured gray-scale descriptors could carry major diagnostic value. 9 The risk of malignancy index then embedded ultrasound findings inside a broader clinical score with CA125 and menopausal status, illustrating the value of multivariable containment rather than single-marker certainty. 10 This review uses that historical contrast to define the quantification paradox: the field wanted continuous vascular quantification, yet the clinically durable solution was a coarse semiquantitative vascular score embedded in externally validated systems. For clinicians involved in women’s health, the practical question is not whether vascularity can be measured more finely, but whether vascular information can be used reproducibly to guide triage, monitoring, and follow-up across real clinical settings. This three-era movement from the precise vascular number, through the coarse visual score, and then back toward microvascular and AI-based re-quantification is summarized in Figure 1 . Figure 1 The quantification paradox in gynecologic Doppler ultrasound. The field moved from continuous vascular quantification with spectral indices and related numeric endpoints toward the coarser but clinically adopted IOTA/O-RADS color score, and is now swinging back toward re-quantification through SMI, CEUS and AI. The figure emphasizes that clinical adoption depends not only on measurement precision but also on whether the vascular conclusion is reproducible and transportable. The diagram is a conceptual schematic and not a quantitative comparison. The check mark denotes established clinical adoption, whereas the question mark denotes unresolved reproducibility and transportability. Diagram of three eras in gynecologic Doppler ultrasound quantification. The quantification paradox in gynecologic Doppler ultrasound. The field moved from continuous vascular quantification with spectral indices and related numeric endpoints toward the coarser but clinically adopted IOTA/O-RADS color score, and is now swinging back toward re-quantification through SMI, CEUS and AI. The figure emphasizes that clinical adoption depends not only on measurement precision but also on whether the vascular conclusion is reproducible and transportable. The diagram is a conceptual schematic and not a quantitative comparison. The check mark denotes established clinical adoption, whereas the question mark denotes unresolved reproducibility and transportability.

Adnexal malignancy triage is a one-time decision in a heterogeneous population, so it strongly penalizes fragile cross-sectional thresholds. In this setting, the color score within O-RADS or IOTA-based systems should remain a supporting feature rather than the main determinant of management. Endometrial pathology is partly a pattern-recognition problem. A single feeding vessel, rim-like vascularity, multifocal irregular vascularity and color score may be more clinically meaningful than a single VI cutoff. The best role for microvascular imaging here is likely to improve visualization of vascular architecture while preserving IETA-style terminology. Cervical cancer response assessment is a longitudinal problem. Serial vascular change may be more reliable than absolute pretreatment vascularity because within-patient comparisons cancel some between-patient and between-scanner variance. For this reason, SMI VI, qSMI and CEUS time-intensity measures should first be evaluated as monitoring biomarkers before being marketed as universal diagnostic thresholds. Fibroids, adenomyosis and benign vascular mapping impose a different evidentiary burden. The task is often anatomic or procedural rather than binary malignancy triage. Microvascular and contrast techniques may add value by mapping perfusion before or after embolization, focused ultrasound therapy or other uterus-sparing treatments, even when no universal malignancy cutoff exists. This task-specific placement along the continuum from variance-intolerant one-time triage to variance-tolerant longitudinal monitoring is summarized in Figure 3 , with the corresponding readouts and cautions for each task detailed in Table 2 . Figure 3 Matching vascular readouts to the variance tolerance of the clinical task. Cross-sectional adnexal malignancy triage is variance-intolerant and therefore favors O-RADS/IOTA morphology with a visual color score. Endometrial focal lesions and benign procedural mapping favor architectural or spatial vascular readouts. Cervical cancer treatment response is more variance-tolerant because serial within-patient measurements allow each patient to serve partly as her own control. The spectrum is a conceptual decision aid, not a quantitative ranking. The diagram shows a range from variance-intolerant to variance-tolerant clinical tasks, covering four areas: 1. Adnexal malignancy triage uses O-RADS/IOTA morphology and color score for a one-time benign vs malignant decision in a diverse population. Caution: RI, PI, VI should not be sole deciders. 2. Endometrial focal lesions use IETA descriptors and vascular imaging for pattern recognition (polyp/fibroid/malignancy). Caution: Avoid universal VI cutoffs. 3. Fibroids, adenomyosis, benign mapping use Color Doppler, microvascular, CEUS and 3D perfusion mapping for spatial/procedural decisions, not binary triage. Caution: Distinguish mapping utility from malignancy thresholds. 4. Cervical cancer treatment response uses Serial SMI VI/CEUS time-intensity with the patient as their own control for longitudinal decisions. Caution: Baseline-only predictions are fragile. A diagram showing variance-intolerant to variance-tolerant clinical tasks with readouts and cautions. Table 2 Matching Vascular Ultrasound Endpoints to Clinical Tasks Clinical Task Decision Type Preferred Vascular Readout Why This Fit is Measurement-Theoretically Defensible Cautions/Non-Ideal Endpoints Adnexal malignancy triage One-time benign/malignant risk assignment in heterogeneous populations O-RADS or IOTA morphology with visual color score Coarse vascularity is buffered by morphology and validated risk systems, reducing dependence on a fragile single cutoff Do not let RI, PI, PSV, or VI thresholds drive management alone Indeterminate O-RADS 4 or solid-appearing adnexal components Problem solving after initial ultrasound stratification CEUS enhancement pattern and O-RADS-plus-CEUS models Presence, absence, and timing of enhancement are more reproducible than color artifacts or spectral thresholds Continuous TIC thresholds still require local protocol standardization Endometrial focal lesions Pattern recognition among polyp, submucosal fibroid, and suspicious endometrial disease IETA descriptors, feeding-vessel pattern, color score, and targeted microvascular imaging Vascular architecture is often more informative than a single global vascular-index number Avoid universal VI cutoffs unless acquisition and ROI rules are fixed Cervical cancer treatment response Longitudinal within-patient response assessment Serial SMI VI, serial CEUS parameters, or 2D/3D vascular indices Each patient can serve as her own control, reducing between-subject and between-machine variance Baseline-only prediction is more fragile than serial change Fibroids, adenomyosis, and benign procedural mapping Spatial or procedural evaluation rather than binary malignancy classification Color Doppler, microvascular modes, CEUS, or 3D vascular mapping Spatial depiction can be clinically useful even without a transportable malignancy threshold Diagnostic threshold claims should be separated from mapping utility AI-assisted ultrasound workflow Quality control, acquisition guidance, segmentation, and calibrated decision support Automated segmentation, standardized ROI extraction, calibrated probability output, OOD detection AI targets the variance sources that limit human and machine reproducibility Internal AUC alone is insufficient without calibration and external validation Abbreviations : 2D/3D, two-dimensional/three-dimensional; AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; CEUS, contrast-enhanced ultrasound; IETA, International Endometrial Tumor Analysis; IOTA, International Ovarian Tumor Analysis; OOD, out-of-distribution; O-RADS, Ovarian-Adnexal Reporting and Data System; PI, pulsatility index; PSV, peak systolic velocity; RI, resistance index; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve; VI, vascular index. Matching vascular readouts to the variance tolerance of the clinical task. Cross-sectional adnexal malignancy triage is variance-intolerant and therefore favors O-RADS/IOTA morphology with a visual color score. Endometrial focal lesions and benign procedural mapping favor architectural or spatial vascular readouts. Cervical cancer treatment response is more variance-tolerant because serial within-patient measurements allow each patient to serve partly as her own control. The spectrum is a conceptual decision aid, not a quantitative ranking. Matching Vascular Ultrasound Endpoints to Clinical Tasks Abbreviations : 2D/3D, two-dimensional/three-dimensional; AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; CEUS, contrast-enhanced ultrasound; IETA, International Endometrial Tumor Analysis; IOTA, International Ovarian Tumor Analysis; OOD, out-of-distribution; O-RADS, Ovarian-Adnexal Reporting and Data System; PI, pulsatility index; PSV, peak systolic velocity; RI, resistance index; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve; VI, vascular index.

First, acquisition standardization should be treated as a primary endpoint. Studies of SMI VI, CEUS time-intensity parameters or Doppler radiomics should prespecify gain, dynamic range, depth, pulse-repetition frequency, wall-filter settings, frame rate, transducer frequency and region-of-interest rules. Second, reproducibility reporting should become mandatory. Interobserver and intraobserver agreement should be reported for visual scores, segmentation masks, vascular index values and AI outputs. Between-machine agreement should be reported whenever a method is intended for multicenter clinical adoption. A consolidated reporting checklist spanning acquisition protocol, region-of-interest and segmentation, reader and operator effects, model development, calibration and external validation is provided in Table 3 . Table 3 Suggested Reporting Checklist for Future Doppler, SMI, CEUS, Radiomics, and AI Studies Reporting Domain Required Item for Vascular Ultrasound Studies Why It Matters for the Quantification Paradox Applies Especially to Recommended Statistic/Output Acquisition protocol Report machine, transducer, gain, PRF, wall filter, dynamic range, depth, frame rate, and preset Prevents high-resolution endpoints from becoming non-transportable local measurements SMI, CEUS, radiomics, AI Protocol table and site-level acquisition summary ROI and segmentation Define lesion boundary, vascular ROI, exclusion of necrosis/debris, and who performed segmentation ROI placement is a direct source of VI, TIC, and radiomics variance SMI VI, CEUS TICs, radiomics Interobserver ICC, Dice coefficient, mask audit Reader and operator effects State reader number, experience, blinding, training, and consensus procedure Separates true biomarker signal from expert-reader or operator-specific performance Color score, O-RADS, IETA, AI labels Weighted kappa, ICC, stratified reader analysis Model development Specify split strategy, leakage prevention, feature selection, hyperparameters, and internal validation Prevents apparent low bias from being purchased by overfitting and unmeasured variance Radiomics and deep learning CLAIM checklist, transparent modeling table Calibration Report calibration intercept, slope, calibration plot, and recalibration when needed Clinical adoption requires transportable probabilities, not only discrimination Risk models and AI classifiers Calibration curve, Brier score, ECE, calibration slope External validation Test on prospective, multicenter, multivendor, or temporally separated cohorts when possible Directly tests whether a vascular threshold or AI model transports beyond local acquisition conditions All new quantitative endpoints AUC with CI, sensitivity/specificity, decision-curve analysis, subgroup analysis Abbreviations : AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; CEUS, contrast-enhanced ultrasound; CI, confidence interval; CLAIM, Checklist for Artificial Intelligence in Medical Imaging; Dice, Dice similarity coefficient; ECE, expected calibration error; ICC, intraclass correlation coefficient; IETA, International Endometrial Tumor Analysis; O-RADS, Ovarian-Adnexal Reporting and Data System; PRF, pulse repetition frequency; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve; VI, vascular index. Suggested Reporting Checklist for Future Doppler, SMI, CEUS, Radiomics, and AI Studies Abbreviations : AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; CEUS, contrast-enhanced ultrasound; CI, confidence interval; CLAIM, Checklist for Artificial Intelligence in Medical Imaging; Dice, Dice similarity coefficient; ECE, expected calibration error; ICC, intraclass correlation coefficient; IETA, International Endometrial Tumor Analysis; O-RADS, Ovarian-Adnexal Reporting and Data System; PRF, pulse repetition frequency; ROI, region of interest; SMI, superb microvascular imaging; TIC, time-intensity curve; VI, vascular index. Third, AI should be pointed at variance reduction. The most valuable applications may be acquisition guidance, automated lesion segmentation, standardized vascular-index computation, image-quality control, calibration monitoring and out-of-distribution detection. These tasks directly attack the historical failure mode of Doppler quantification. Fourth, prediction-model discipline should be imported into Doppler AI. TRIPOD established transparent reporting expectations for clinical prediction models. 57 Calibration deserves explicit attention because poorly calibrated models can mislead clinical management even when discrimination appears strong. 58 PROBAST gives reviewers a practical tool for judging risk of bias and applicability in prediction-model studies. 59 TRIPOD+AI now extends reporting guidance to models using regression or machine-learning methods. 60 Finally, new vascular technologies should be tested first in variance-tolerant tasks. Serial monitoring, standardized spatial mapping and quality-control assistance are more plausible early targets than universal cross-sectional malignancy thresholds. Only after stability is demonstrated should high-resolution vascular quantification be used to drive management decisions. Access also differs by setting: conventional Doppler is widely available, whereas vendor-specific microvascular modes, CEUS and AI may require newer equipment, contrast agents, training or governance infrastructure. Future studies should therefore report these requirements and compare incremental value against an accessible conventional-ultrasound baseline.

The history of gynecologic color Doppler ultrasound is best understood as a thirty-year experiment in measurement. The field repeatedly attempted to convert tumor vascularity into an objective high-resolution estimator, but the estimator repeatedly failed when variance was left uncontrolled. What survived was a coarse vascular score protected by standardized terminology, multivariable context and external validation. The newest tools—SMI, CEUS, radiomics and AI—should not be judged by how much more vascular detail they display. They should be judged by whether the same vascular conclusion can be obtained by another operator, on another scanner, in another center and in another patient population. The next clinically useful Doppler biomarker will be the one that makes vascular information not only visible, but reproducible and transportable. Established evidence supports the reproducibility of standardized color scoring and the instability of some vascular thresholds, whereas the quantification paradox remains a conceptual interpretation of this pattern. Whether reproducibility is the dominant causal constraint, and whether SMI, CEUS or AI will overcome it, remain hypotheses for prospective multicenter testing.