Microvascular flow imaging for detection of endometrial malignancy: comparison with color Doppler imaging

Introduction

With approximately 417,000 new cases being diagnosed globally each year, endometrial malignancy is the sixth most common cancer worldwide, and its incidence continues to rise (1,2). Abnormal uterine bleeding is the most commonly investigated presenting clinical symptom in both pre- and postmenopausal women (3). It has been suggested that among women with this symptom, endometrial biopsy is recommended for those over the age of 40 years (4-6). Therefore, appropriate evaluation of women with premenopausal or postmenopausal bleeding is crucial for the accurate diagnosis of endometrial malignancy and provides the best opportunity for cure.

Transvaginal ultrasound is the first-line imaging modality for evaluating the endometrium (7). In 2010, the International Endometrial Tumor Analysis (IETA) Consortium published a consensus statement on the methods for examining and measuring the endometrium, as well as the terminology for describing the sonographic features of endometrial lesions, including thickness, echogenicity, and vascular flow (8). Ultrasonographic studies in postmenopausal women with abnormal bleeding have primarily focused on assessing endometrial thickness as a method to exclude endometrial malignancy (1). Although a threshold of 5 mm in postmenopausal women provides an endometrial malignancy detection sensitivity of 96.2%, it has a low specificity of approximately 51.5% in postmenopausal women, which is even lower than that in premenopausal women (9). In such cases, an adjunctive test is recommended to refine the diagnosis. Angiogenesis is crucial for the development, growth, and progression of endometrial malignancy (10,11). Endometrial vascularity can be assessed via color Doppler imaging (CDI). However, in several studies, the specificity of CDI in discriminating benign from malignant endometrium was found to be unsatisfactory; Sladkevicius et al., for instance, reported a value of just 66% (12).

Microvascular flow imaging (MVFI) represents a significant advancement in vascular Doppler imaging, providing enhanced sensitivity for detecting smaller vessels and low-velocity blood flow without the use of intravenous contrast agents. Unlike conventional CDI, MVFI employs adaptive filtering techniques to reduce random motion artifacts while maintaining the detection of directional blood flow, resulting in higher-resolution and more accurate assessments of microvasculature (13). MVFI has been evaluated in many other fields (13,14), including arthritis (15), breast nodules (16), and deep vein thrombosis (17), yielding positive results. However, there is limited research on the use of MVFI for evaluating the endometrium. Therefore, the aim of this study was to evaluate the diagnostic efficacy of MVFI as compared that of CDI in detecting endometrial malignancy, with pathological outcomes serving as the reference standard. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-350/rc).

Methods

Patients

This prospective, single-center study was approved by the Institutional Review Board of the Third Affiliated Hospital of Sun Yat-sen University in September 2022 (Approval No. 2022-01-020-01), and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All patients provided written informed consent for participation.

The flowchart of patient recruitment is shown in Figure 1. Between June 2023 and October 2024, female patients with abnormal uterine bleeding were enrolled at the Third Affiliated Hospital of Sun Yat-sen University. The inclusion criteria were as follows: (I) women aged over 40 years with abnormal uterine bleeding as defined by the International Federation of Gynecology and Obstetrics (FIGO) PALM (polyp, adenomyosis, leiomyoma, and malignancy/hyperplasia)-COEIN (coagulopathy, ovulatory dysfunction, endometrial causes, iatrogenic, and not otherwise classified) classification (cycle length <21 or >35 days, intermenstrual bleeding, or postmenopausal bleeding of any amount) (18), (II) transvaginal ultrasound examination performed before surgery, and (III) a diagnosis confirmed by histological outcomes obtained from hysteroscopy or hysterectomy. The exclusion criteria were as follows: (I) unwillingness to sign the informed consent form; and (II) lack of complete imaging, which included difficulty in visualizing the endometrium, absence of CDI/MVFI sweep across the endometrium, and postvoid uterus not fully captured in a single field of view (FOV) (depth >11 cm).

Figure 1 Flowchart of patient selection. There was no significant difference in baseline data (except for the waistline and hipline) between included and excluded patients.

Ultrasound examination

A HERA W10 diagnostic instrument (Samsung Medison Co., Ltd., Seoul, Korea) for obstetric and gynecologic ultrasound was used, along with a transvaginal probe for fan-shaped exploration with 3 to 10-MHz transducers. All images and patient information were stored on the device and in the picture archiving and communication system (PACS).

All ultrasound examinations were performed by a board-certified gynecological sonographer (Y.W.), with at least 1 year of experience in gynecological ultrasound examinations. Before the clinical trial began, the sonographer was trained in the IETA standards (8) by gynecological ultrasound experts. Ultrasound images were subsequently collected by the sonographer, who was not involved in the final image interpretation. The steps for image acquisition were as follows: First, after the longitudinal section of the uterus was localized, two-dimensional ultrasound images of the uterus were obtained to achieve optimal visualization of the target endometrium, and video clips of the longitudinal scan of the uterus were saved. On two-dimensional grayscale ultrasound examination, endometrial thickness, endometrial echogenicity, endometrial midline, bright edge sign, endometrial-myometrial junction, and intracavitary fluid were evaluated based on IETA standards (8). Second, CDI was conducted with an appropriate FOV and optimized parameters for endometrial evaluation. Finally, with the probe unmoved, MVFI was performed with the same FOV.

To achieve the highest image quality, all settings were fine-tuned for the endometrium. the mechanical index was 1.2 for CDI and 1.3 for MVFI. The gain, sensitivity, pulse repetition frequency, and depth for both CDI and MVFI were 30–60%, 7–32, 0.20–0.77 kHz, and 4–10 cm, respectively.

Clinical baseline data collection and reference standard

Before patients underwent transvaginal ultrasound examination, their medical history was obtained. In this study, age, height, weight, waistline, hipline, gravidity, parity, abortions, menopausal status, high blood pressure, diabetes mellitus, polycystic ovary syndrome, infertility, use of hormone replacement therapy (HRT), tamoxifen use, personal oncology history, and family oncology history were documented.

The histological diagnosis obtained from hysteroscopy and hysterectomy served as the reference standard for the study. The histological outcomes were classified into the following categories: (I) proliferative endometrium, (II) secretory endometrium, (III) atrophy, (IV) endometrial hyperplasia without atypia, (V) endometrial polyp, (VI) intracavitary leiomyoma, (VII) atypical hyperplasia, and (VIII) endometrial cancer.

Image analysis

Endometrial vascular flow was independently reviewed by two board-certified gynecological sonographers (M.Z. and J.C.), with 6 and 10 years of experience, respectively, using saved images and videos (8). The assessment, conducted with CDI and MVFI, was based on a 4-point scale detailed in Figure 2. In cases of disagreement, the final evaluation was determined by a gynecological tumor ultrasound expert (X.Z.). In addition, intraobserver agreements were assessed in reverse order (CDI to MVFI) of the prospective acquisition of CDI and MVFI examinations by the two sonographers, with a 1-month interval between assessments to minimize recall bias.

Figure 2 Vascular assessment of the endometrium. In each figure, the top is the schematic diagram, the middle is CDI, and the bottom is the MVFI. (A) A color score of 1 indicates no vascular flow, (B) a score of 2 indicates minimal vascular flow, (C) a score of 3 indicates moderate vascular flow, and (D) a score of 4 indicates abundant vascular flow. CDI, color Doppler imaging; MVFI, microvascular flow imaging.

Sample size calculation

The sample size was determined with the PASS software version 15.0 (NCSS, Kaysville, UT, USA; https://www.ncss.com/software/pass/). Based on findings from previous studies (2,9,19,20), the sensitivity and specificity of transvaginal ultrasound for the detection of endometrial malignancy fluctuated within the range of 90.0–96.2% and 51.5–99.3%, respectively. The estimated sensitivity was 90.0%, and the specificity was 80.0%. The prevalence of endometrial cancer in women with abnormal uterine bleeding fluctuated within the range of 5.6–13.0%, as reported by previous studies (21-24), and the estimated prevalence was 15.0% in our institution—a tertiary hospital. This calculation was based on a confidence level (1−α) of 0.95 and a confidence interval width of 0.2. According to the software calculation, a minimum of 234 participants was considered needed for the study.

Statistical analysis

All statistical analyses were conducted with MedCalc v. 16.4 (MedCalc Software Ostend, Belgium) and SPSS v. 23 (IBM Corp., Armonk, NY, USA) software, with two-tailed P values less than 0.05 being considered statistically significant. Normally distributed data are expressed as the mean ± standard deviation, while nonnormally distributed data are expressed as medians with their lower and upper quartiles. Vascularity scores were compared between pathological outcomes and ultrasound techniques via the Wilcoxon rank-sum test. Chi-squared test was used for categorical data and the Mann-Whitney test for continuous data. Univariable logistic analysis was used to identify independent ultrasonic risk factors for predicting endometrial malignancy, and multivariable logistic analysis was used to construct a predictive model through the forward logistic regression method.

The performance was evaluated according to the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and negative likelihood ratio (−LR). The Delong test was used to compare the AUCs, and the McNemar test was used to compare the sensitivity and specificity.

Intra- and interobserver agreement for the vascular flow score was assessed via the weighted kappa statistic. The strength of agreement was evaluated as follows: 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, good agreement; and 0.81–1.00, excellent agreement.

Results

Characteristics of study population

During the study period, 407 patients were initially enrolled, and 283 women were ultimately included in the final analysis (Figure 1). Table 1 presents the characteristics of the study participants. Of the 283 patients, 79 (27.9%) were postmenopausal. Patients with high blood pressure and diabetes mellitus accounted for 11.3% (32/282) and 4.9% (14/283) of the patients, respectively. No patients had polycystic ovary syndrome or infertility. The number of patients taking tamoxifen and undergoing HRT was 5 (1.8%) and 29 (10.2%), respectively. Meanwhile, 16 (5.7%) patients had personal history of oncology, and 29 (10.2%) had a family history of oncology.

As shown in Table 1, the histological evaluation of the 283 endometrial specimens identified 251 nonmalignancies and 32 malignancies, yielding an overall malignancy rate of 11.3% (32/283). Among the nonmalignant endometria, 20.5% (n=58), 7.8% (n=22), 4.2% (n=12), 16.6% (n=47), 34.6% (n=98), and 4.9% (n=14) were proliferative endometria, secretory endometria, atrophy, endometrial hyperplasia without atypia, endometrial polyp, and intracavitary leiomyoma, respectively. Atypical hyperplasia and endometrial cancer accounted for 2.1% (n=6) and 9.2% (n=26) of the total cases, respectively.

Ultrasound features of endometrial specimens based on IETA standards

The sonographic features of nonmalignant and malignant endometrium are listed in Table 2. Statistically significant differences (P<0.05) were found between nonmalignant and malignant endometria in term of endometrial thickness, endometrial echogenicity, endometrial–myometrial junction, intracavitary fluid, CDI, and MVFI. There were no statistically significant differences between nonmalignant and malignant endometria in terms of endometrial midline or bright edge sign (P>0.05).

Comparison of endometrial flow scores in endometrial nonmalignancies and malignancies

In the comparison of the subjective vascular scores in endometrial nonmalignancies and malignancies, the median CDI score was 1 [1, 2] for nonmalignant endometria and 2 [1, 3] for malignant endometria (P<0.001; Figure 3A). The median MVFI score was 2 [1, 3] for nonmalignant endometria and 3 [2, 4] for malignant endometria (P<0.001; Figure 3B).

Figure 3 Comparison of vascular scores. (A) Comparison of vascular scores between nonmalignant and malignant endometria in CDI. The median score of malignancies was higher than that of nonmalignancies (2 vs. 1; P<0.0001). (B) Comparison of vascular scores between nonmalignant and malignant endometria in MVFI. The median score of malignancies was higher than that of nonmalignancies (3 vs. 2; P<0.0001). (C) Comparison of vascular scores between CDI and MVFI in all endometrial specimens. The median score of MVFI was higher than that of CDI (2 vs. 1; P<0.0001). ****, P<0.0001. CDI, color Doppler imaging; MVFI, microvascular flow imaging.

Comparison of endometrial vascular flow scores between CDI and MVFI

The subjective vascular scores of CDI and MVFI are compared in Figure 3C. The median CDI score was 1 [1, 2], and the median MVFI score was 2 [1, 3] (P<0.001). The percentages for scores 1, 2, 3, and 4 in CDI were 69.7%, 22.9%, 4.8%, and 2.6%, respectively, while in MVFI, they were 31.0%, 37.6%, 22.5%, and 8.9%, respectively. The endometrial flow score in MVFI was significantly higher than that in CDI.

Figures 4,5 show the typical ultrasonic images of a nonmalignant endometrium. Figure 4 depicts the ultrasonic images of endometrial hyperplasia. The vascularity in the endometrium was scored higher on MVFI (score =2) than on CDI (score =1). Figure 5 provides ultrasonic images of endometrial polyps. The vascularity in MVFI was more abundant (score =4) than that in CDI (score =3).

Figure 4 A 52-year-old woman with abnormal uterine bleeding whose pathological outcome was endometrial hyperplasia. (A) Grayscale ultrasound image of the endometrium. (B) The thickness of endometrium was 13.6 mm. (C) The final subjective vascular score for CDI was 1. (D) The final subjective vascular score for MVFI was 2. CDI, color Doppler imaging; MVFI, microvascular flow imaging.

Figure 5 A 61-year-old woman with postmenopausal bleeding whose pathological outcome was endometrial polyp. (A) Grayscale ultrasound image of the endometrium. (B) The thickness of endometrium was 20.1 mm. (C) The final subjective vascular score for CDI was 3. (D) The final subjective vascular score for MVFI was 4. CDI, color Doppler imaging; MVFI, microvascular flow imaging.

Abundant vascularity was observed in endometrial malignancy, as depicted in Figures 6,7. Specifically, Figure 6 shows the high vascularity in focal atypical hyperplasia. The subjective score was higher for MVFI (score =4) than for CDI (score =2). A similar outcome can be observed in Figure 7, which shows endometrioid adenocarcinoma. CDI showed moderate vascularity (score =3), while MVFI showed abundant vascularity (score =4). MVFI exhibited more abundant vascularity in both nonmalignant and malignant endometria as compared with CDI.

Figure 6 A 43-year-old woman with abnormal uterine bleeding whose pathological outcome was focal atypical hyperplasia. (A) Grayscale ultrasound image of endometrium. (B) The thickness of the endometrium was 15.2 mm. (C) The final subjective vascular score for CDI was 2. (D) The final subjective vascular score for MVFI was 4. CDI, color Doppler imaging; MVFI, microvascular flow imaging.

Figure 7 A 45-year-old woman with abnormal uterine bleeding whose pathological outcome was endometrioid adenocarcinoma. (A) Grayscale ultrasound image of endometrium. (B) The thickness of endometrium was 18.5 mm. (C) The final subjective vascular score for CDI was 3. (D) The final subjective vascular score for MVFI was 4. CDI, color Doppler imaging; MVFI, microvascular flow imaging..

Diagnostic performance for endometrial malignancy detection

Table 3 presents the diagnostic performance of various vascular flow detection methods in predicting endometrial malignancy. Under a threshold of MVFI color score >2 and CDI color score >1 for malignancy, the AUC of MVFI (0.769; 95% CI: 0.717–0.817) was significantly higher than that of CDI (0.681; 95% CI: 0.623–0.735) (P=0.011). In addition, the sensitivity of MVFI (71.9%; 95% CI: 51.3–86.3%) was higher than that of CDI (59.4%; 95% CI: 40.6–76.3%) (P=0.031). Meanwhile, the specificity of MVFI (72.9%; 95% CI: 67.0–78.3%) was the same as that of CDI (72.9%; 95% CI: 67.0–78.3%) (P=0.850).

Further analysis indicated a misdiagnosis rate of 27.1% (68/251) for both CDI and MVFI. For CDI, the proportions of misdiagnosis listed from highest to lowest were as follows: endometrial polyp (30/68, 44.1%), proliferative endometrium (14/68, 20.6%), endometrial hyperplasia without atypia (12/68, 17.6%), secretory endometrium (6/68, 8.8%), and intracavitary leiomyoma (6/68, 8.8%). For MVFI, the proportions of misdiagnosis listed from highest to lowest were as follows: endometrial polyp (30/68, 44.1%), endometrial hyperplasia without atypia (17/68, 25.0%), proliferative endometrium (8/68, 11.8%), secretory endometrium (7/68, 10.3%), and intracavitary leiomyoma (6/68, 8.8%).

As shown in Table 3, logistic regression analysis was used to develop ultrasonic diagnostic models for endometrial malignancy. Model 1 (endometrial thickness + intracavitary fluid + CDI) demonstrated an AUC of 0.809 (95% CI: 0.717–0.818) and a sensitivity of 68.8% (95% CI: 50.0–83.9%) for predicting endometrial malignancy. Model 2 (endometrial thickness + intracavitary fluid + MVFI) showed significantly superior performance, with an AUC of 0.855 (95% CI: 0.808–0.894) and a sensitivity of 87.5% (95% CI: 71.0–96.5%). Compared to Model 1, Model 2 had a significantly higher AUC (P=0.046) and sensitivity (P=0.031), but an identical specificity (72.9%; P=1.000).

Intra- and interobserver agreement

The intraobserver agreement for Reviewer 1 and Reviewer 2 was excellent, with values of 0.972 and 0.884 for CDI, respectively, and 0.959 and 0.899 for MVFI, respectively. The interobserver agreement between the two radiologists for CDI and MVFI was good, with values of 0.764 and 0.721 in the original order, respectively, and 0.726 and 0.725 in the reverse order, respectively (see Table 4).

Discussion

To the best of our knowledge, this is the first study to report the results of MVFI assessment of endometrial malignancy. In this study, we demonstrated that MVFI was clinically significantly more sensitive than was CDI for detecting endometrial blood flow in women with abnormal uterine bleeding. Specifically, the AUC and sensitivity for detecting endometrial malignancy of MVFI alone (AUC 0.769; sensitivity 71.9%) and Model 2 (AUC 0.855; sensitivity 87.5%) were significantly higher than those of CDI alone (AUC 0.681; sensitivity 59.4%) and Model 1(AUC 0.809; sensitivity 68.8%), while the specificity was identical (72.9% for all). Additionally, the intraobserver agreement was excellent, and the interobserver agreement was good. This study is novel not only because we assessed the practicality of the MVFI method—a novel microvascular technique that employs an advanced proprietary clutter filtration mechanism—but also because we compared its diagnostic efficacy in evaluating endometrial vascularity to that of CDI.

MVFI was more effective at detecting capillaries with small diameters and slow blood flow rates than was CDI. As demonstrated by our results, the median subjective vascularity score for MVFI (score =2) was significantly higher than that for CDI (score=1) across all endometrial pathologies, which is consistent with previous studies. For instance, Kang et al. (25) investigated the feasibility of MVFI compared to color/power Doppler imaging (CDI/PDI) for detecting intratumoral vascularity in suspected residual or recurrent hepatocellular carcinomas (HCCs) after transarterial chemoembolization (TACE), reporting that MVFI demonstrated a significantly higher subjective vascularity score than CDI. Similarly, Han et al. (26) evaluated the vascular architecture of focal liver lesions using the MVFI technique and found that MVFI provided richer blood flow signals, enabling observers to detect blood signals that CDI could not observe. In theory, tissue echoes and blood scatter echoes exhibit similar characteristics when blood flow velocities are low (especially in small vessels) or when significant tissue motion occurs, resulting in reduced sensitivity of clutter filters in CDI to vascularity. However, tissue signals in ultrasound imaging possess higher spatial coherence as compared to blood signals. Based on the principle, MVFI and similar techniques enhance flow detection sensitivity by selectively suppressing flash artifact signals from surrounding tissue, enabling the assessment of small or slow-flow signals within endometrium.

Angiogenesis is a recognized hallmark of endometrial carcinogenesis (27). In our study, vascularity scores were significantly higher in malignant than nonmalignant endometria on both CDI (median: 2 vs. 1; P<0.001) and MVFI (median: 3 vs. 2; P<0.001). These findings align with a study on the MRI marker diffusion-derived vessel density (DDVD), which similarly revealed elevated microvascular density in high-risk endometrial cancer (28); moreover, significantly higher DDVD values were observed in both Ki-67 high-proliferation and aggressive histological groups. Collectively, MVFI and DDVD vascular imaging techniques provide clinically valuable detection of abnormal angiogenesis in endometrial malignancy.

In our study, MVFI demonstrated better diagnostic performance in terms of AUC and sensitivity (0.770 and 71.9%, respectively), which were significantly higher than those of CDI (0.681 and 59.4%, respectively), while maintaining comparable specificity (72.9% for both). Transvaginal sonographic assessment of endometrial thickness is a noninvasive technique that can exclude endometrial malignancy in postmenopausal women with uterine bleeding. In their meta-analysis, Long et al. reported that cutoffs of ≥3, ≥4, and ≥5 mm were all highly sensitive in detecting endometrial malignancy (9). However, endometrial thickening is nonspecific in postmenopausal women with abnormal bleeding, and in premenopausal women with abnormal uterine bleeding, endometrial thickness is even less specific, as it fluctuates cyclically (29). Therefore, an adjunctive test would be advisable to refine diagnosis in such cases. Given its superior diagnostic performance compared to CDI, MVFI might be considered an adjunctive test for precisely diagnosing endometrial malignancy.

Our results further indicated that the loss in specificity was primarily due to the difficulty in distinguishing certain nonmalignant cases (endometrial polyps and hyperplasia without atypia) from malignant cases (endometrial carcinoma and atypical hyperplasia) with the CDI and MVFI blood flow scoring. Case of a nonmalignant endometrium, such as endometrial polyps and endometrial hyperplasia without atypia, can exhibit high color scores. Consistent with our results, Van Den Bosch et al. (30) evaluated the IETA terms in women with abnormal uterine bleeding. The results showed that the color score was typically 1–2 for endometrial hyperplasia without atypia (78%), 2–3 for endometrial polyps (69%), 3–4 for atypical hyperplasia (53%), and 3–4 for endometrial cancer (65%). In such cases, sole reliance on blood flow abundance could lead to diagnostic errors. Van Den Bosch et al. (30) demonstrated that echogenicity, endometrial–myometrial junction, and vascular pattern are capable of discriminating endometrial malignancy from nonmalignancy. Integrating additional sonographic features, including endometrial thickness and intracavitary fluid, can improve diagnostic efficacy, as highlighted in our results.

Given that the reproducibility of a technique is crucial for its reliability in clinical practice, we also evaluated both inter- and intraobserver agreement in our study. In our series, the intraobserver agreement for MVFI and CDI evaluations was excellent, while the interobserver agreement was good. Consistent with the study by Bartolotta et al. (31), interreader agreement for MVFI and CDI had the lowest relative k-scores of 0.85 and 0.79, respectively, corresponding to excellent and good agreement. Notably, in our study, all reviewers were experienced radiologists, whereas in Bartolotta et al.’s study, one reader was a final-year resident, demonstrating that both junior and senior radiologists exhibit good reproducibility in blood flow score assessment.

Several limitations of our study should be acknowledged. Firstly, the focus was on perimenopausal women with abnormal uterine bleeding, including both pre- and postmenopausal females. Therefore, the calculated cutoff values may not be applicable to both populations. Second, the single-center nature and the limited sample size of our study suggest that its conclusions should be validated in larger, multicenter studies in the future.

Conclusions

Overall, among pre- and postmenopausal women with abnormal uterine bleeding, MVFI outperforms CDI in assessing vascular abundance in the endometrium and demonstrates good inter- and intrareader agreement. Therefore, MVFI holds promise as an adjunctive test for accurately diagnosing endometrial malignancy in women with abnormal uterine bleeding.

Acknowledgments

The authors thank the statistical support from Jingjing Chen (the Third Affiliated Hospital of Sun Yat-sen University) and writing advice from Prof. Pheier Saw (Sun Yat-sen Memorial Hospital, Sun Yat-sen University).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-350/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-350/dss

Funding: This work was supported by the National Natural Science Foundation of China (No. 82202191); Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515220008); Municipal and University (Hospital) Joint Funding Project of Guangzhou Municipal Science and Technology Bureau (No. SL2022A03J00358); Guangzhou Basic and Applied Basic Research Foundation (No. 2024A04J4786); Natural Science Foundation of Guangdong Province (No. 2022A1515012027).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-350/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This prospective, single-center study was approved by the Institutional Review Board of the Third Affiliated Hospital of Sun Yat-sen University in September 2022 (Approval No. 2022-01-020-01), and all patients provided written informed consent for participation. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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