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The role of biochemical parameters and preoperative ultrasonographic markers in detecting malignancy in adnexal masses
1Department of Oncology, Azerbaijan Medical University, AZ1022 Baku, Azerbaijan
*Corresponding Author(s):eibrahimov1@amu.edu.az (Akbar Ibrahimov)
| History | Submitted: 22 May 2025 | Accepted: 11 July 2025 | Published: 15 October 2025 |
| Copyright: | ©2025 The Author(s). Published by MRE Press. |
Background: Accurate preoperative differentiation of adnexal masses is crucial for appropriate patient management. This study aimed to evaluate the diagnostic performance of preoperative ultrasonographic (USG) markers and biochemical parameters in distinguishing between malignant and benign adnexal masses and to compare the utility of the Risk of Malignancy Indexes. Methods: This retrospective cohort study analyzed data from 141 patients who underwent surgery for adnexal masses at the Department of Oncology, Azerbaijan Medical University. Data collected included demographics, clinical features, preoperative serum levels of biochemical markers (Cancer Antigen 125 (CA125), CA15-3, CA19-9, Alpha-Fetoprotein (AFP), and Carcinoembryonic Antigen (CEA)), and specific ultrasound (USG) findings. Postoperative histopathology served as the gold standard, identifying 91 benign and 50 malignant cases. Statistical analyses employed included the Independent Samples t-test, the Mann-Whitney U test, the Chi-square test, and multivariate backward stepwise logistic regression. Results: Malignancy was significantly associated with older patient age (mean age difference, p = 0.04), bilateral tumor presentation (44% in malignant vs. 9.9% in benign, p < 0.05), and elevated preoperative serum levels of CA125 and CA15-3 (p < 0.05 for both). Specific USG features significantly associated with malignancy included the presence of septations, irregular tumor surface, and the presence of ascites (p < 0.05 for all). Ascites demonstrated the highest predictive value, being present in 94% of malignant cases compared to 2.2% of benign cases (p < 0.05). RMI-4 exhibited slightly higher sensitivity (74%) and specificity (84.6%) for malignancy detection compared to RMI-3 (sensitivity 72%, specificity 83.5%). Conclusions: The integration of patient age, tumor laterality, specific USG characteristics, and selected serum biomarkers significantly enhances the preoperative prediction of malignancy in women presenting with adnexal masses. While advanced diagnostic modalities offer superior performance, RMI remains clinically valuable in resource-limited settings where implementation barriers may limit access to sophisticated diagnostic tools.
Cite this article
Akbar Ibrahimov. The role of biochemical parameters and preoperative ultrasonographic markers in detecting malignancy in adnexal masses.European Journal of Gynaecological Oncology,2025,46(10):39-45 DOI:10.22514/ejgo.2025.130
1. Introduction
Ovarian cancer represents the most lethal gynecologic malignancy, characterized by a significant increase in incidence over recent decades [1]. In the United States alone, the American Cancer Society projected approximately 19,680 new diagnoses and 12,740 deaths in 2024 [2]. The prognosis is heavily dependent on the stage at which the diagnosis is made. At the same time, the five-year overall survival (OS) rate for localized disease approaches 93%, and it dramatically drops to 31% for cases with distant metastases, resulting in an average overall survival (OS) of 30–40% [2]. A primary challenge contributing to these suboptimal outcomes is late-stage detection, with over 70% of patients presenting with advanced disease at diagnosis [3, 4]. The effective clinical management of a patient presenting with an adnexal mass hinges critically on accurately determining the likelihood of malignancy before intervention. While significant advancements have been made in imaging and biomarker development, reliably differentiating benign from malignant masses preoperatively remains a complex clinical challenge despite clear guidelines and risk stratification tools [5]. This differentiation is essential because the optimal management strategy varies substantially based on the nature of the mass. Benign lesions may be managed conservatively through surveillance or minimally invasive surgery aimed at ovarian preservation. In contrast, suspected malignancies necessitate referral to specialized gynecologic oncology centers for comprehensive staging and cytoreductive surgery [6]. Accurate preoperative risk stratification is therefore paramount to guide appropriate triage, ensuring timely and specialized care for patients with malignancy while avoiding unnecessary extensive surgery and associated morbidity for those with benign conditions [7, 8]. Various diagnostic models have been developed to estimate the risk of malignancy, integrating clinical information, serum biomarkers, and imaging findings. Among the most widely recognized are the Risk of Malignancy Indices (RMI) and models developed by the International Ovarian Tumor Analysis (IOTA) group, which utilize parameters such as patient age, menopausal status, serum CA125 levels, and specific ultrasonographic (USG) features like mass morphology, presence of solid areas, septations, papillary projections, and ascites [9, 10].
While these models provide valuable tools, their performance can vary across different populations, and ongoing research seeks to refine their accuracy and identify optimal combinations of predictive parameters. Recent advances, particularly the IOTA ADNEX (The Assessment of Different NEoplasias in the adneXa) model, have demonstrated superior diagnostic performance but require substantial infrastructure and training investments. This creates a need for validated diagnostic approaches that can be implemented with existing resources in diverse healthcare settings globally. This study aims to evaluate the utility of specific preoperative USG markers and biochemical parameters in differentiating benign from malignant adnexal masses within a cohort of patients treated at Azerbaijan Medical University. Furthermore, we sought to compare the diagnostic performance of two commonly used indices, RMI-3 and RMI-4, in this specific clinical setting, thereby contributing valuable local data to inform preoperative risk assessment strategies.
2. Materials and methods
2.1 Study design and patient population
This study employed a retrospective cohort design to evaluate diagnostic markers for adnexal masses. The study was conducted at the Department of Oncology, Azerbaijan Medical University, reviewing patient records from January 2019 to December 2024. Ethical approval was obtained from the institutional review board (Decision No. #238, date 06 December 2024). Informed consent was waived due to the retrospective nature of the study and approval from the ethics committee.
We included consecutive patients aged 18 years or older who presented with an adnexal mass and subsequently underwent surgical intervention (laparotomy or laparoscopy) at our institution during the study period, resulting in a definitive histopathological diagnosis. Patients with incomplete medical records, those who received neoadjuvant therapy before surgery, or those with a previous history of ovarian malignancy were excluded. A total of 141 patients met the inclusion criteria. The sample size was determined by the number of eligible patients treated during the defined study period. Based on the final histopathological reports, patients were categorized into two groups: Group I (benign group, n = 91) and Group II (malignant group, n = 50).
2.2 Data collection
Data were retrospectively collected from electronic medical records and surgical pathology reports. Demographic information included patient age and menopausal status (defined as premenopausal or postmenopausal based on clinical history; postmenopausal status defined as cessation of menstruation for at least 12 months or age >50 years if status unclear). Clinical data encompassed parity, relevant medical history, pelvic and physical examination findings, and family history of malignancy. Preoperative biochemical parameters were retrieved from laboratory records. Venous blood samples were collected prior to surgery following a standard minimum 8-hour fasting period, primarily as part of routine preoperative assessment protocols. Serum levels of Cancer Antigen 125 (CA125), Cancer Antigen 15-3 (CA15-3), Carcinoembryonic Antigen (CEA), Alpha-Fetoprotein (AFP), and Cancer Antigen 19-9 (CA19-9) were measured. All assays were performed in the hospital’s central laboratory using electrochemiluminescence immunoassay (ECLIA) on a Roche Cobas e601 analyzer according to manufacturer protocols and established quality control procedures. While CA125 is the primary biomarker for epithelial ovarian cancer, the additional markers (CA15-3, CEA, AFP, CA19-9) were included as they are part of the standard institutional panel for characterizing complex adnexal masses to help rule out non-epithelial ovarian cancers or metastatic disease. However, their utility in this specific context was also evaluated.
2.3 Ultrasonographic evaluation
Preoperative transvaginal ultrasonography (TVUS), supplemented by transabdominal ultrasonography (TAUS) when necessary, was performed for all patients. Examinations were conducted by experienced gynecologists or radiologists within our department (>5 years of experience in gynecologic imaging). A standardized imaging protocol was followed, and transvaginal ultrasonography was conducted using a Voluson E8 ultrasound platform (GE HealthCare, Milwaukee, WI, USA) equipped with a 5–9 MHz vaginal probe. Volume datasets were acquired and analyzed per IOTA ADNEX criteria. Key USG parameters were systematically recorded based on IOTA terminology where applicable: laterality (unilateral/bilateral), maximum diameter of the mass (size range recorded), morphology (cystic, solid or complex), presence and number of locules (unilocular, multilocular), presence of solid components (including papillary projections), presence of septations, surface characteristics (smooth vs. irregular), echogenicity (anechoic, hypoechoic, mixed, hyperechoic), presence of acoustic shadowing, and presence of ascites (defined as free fluid in the pouch of Douglas or elsewhere in the pelvis/abdomen).
While formal inter-observer variability assessment was not performed due to the retrospective design, complex cases were often reviewed by multiple specialists as part of routine clinical practice.
2.4 Risk of malignancy index (RMI) calculation
The RMI was calculated for each patient using two variations, RMI-3 and RMI-4, based on the formulas described by Tingulstad and Yamamoto, respectively [11, 12].
• RMI-3: Calculated as U × M × CA125.
U (Ultrasound Score): Score 1 for each of the following features: multilocular cyst, solid areas, metastases, ascites, bilateral lesions. U = 1 if 0–1 feature present, U = 3 if ≥2 features present.
M (Menopausal Score): M = 1 for premenopausal, M = 3 for postmenopausal.
CA125: Serum CA125 level (IU/mL).
• RMI-4: Calculated as U × M × CA125 × S (Tumor Size Score).
U and M scores are the same as for RMI-3.
CA125: Serum CA125 level (IU/mL).
S (Size Score): S = 1 for tumor size <7 cm, S = 2 for tumor size ≥7 cm.
A cut-off value of 200 was used for RMI-3 and 450 for RMI-4 to classify masses as high risk or low risk for malignancy, consistent with established practice [12].
2.5 Statistical analysis
Statistical analysis was performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were calculated for all variables. Continuous variables were expressed as mean ± standard deviation (SD) or mean and interquartile range (IQR) based on their distribution, assessed using the Shapiro-Wilk test.
Categorical variables were presented as frequencies and percentages (%). Differences between the benign and malignant groups were evaluated using appropriate statistical tests. For normally distributed continuous variables (e.g., age after confirming normality), the Independent Samples t-test was used. For non-normally distributed continuous variables (e.g., biomarker levels, which often exhibit skewed distributions), the Mann-Whitney U test was employed (justifying its use due to non-normality). Differences in categorical variables (e.g., menopausal status, laterality, USG features) were analyzed using the Chi-square test or Fisher’s exact test where appropriate (expected cell counts <5). Univariate analysis (Chi-square/Fisher’s exact for categorical, Mann-Whitney U/t-test for continuous) was first performed to identify factors significantly associated with malignancy. Variables showing a significant association (p < 0.05) in the univariate analysis were then included in a multivariate backward stepwise logistic regression model to identify independent predictors of malignancy. Odds ratios (OR) and their 95% confidence intervals (CI) were calculated. This sequential approach enables the initial screening of potential predictors, followed by an assessment of their independent contributions. The diagnostic performance of RMI-3 and RMI-4 at the predefined cut-off of 200 and 450, respectively, was evaluated by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy. A p-value < 0.05 was considered statistically significant for all analyses.
3. Results
A total of 141 patients meeting the inclusion criteria were analyzed, comprising 91 (64.5%) cases with benign adnexal masses and 50 (35.5%) cases with malignant histopathology. The demographic and clinical characteristics of the study cohort are presented in the Table. Patients in the malignant group were significantly older than those in the benign group. Regarding menopausal status, 43.96% (40/91) of patients in the benign group and 72.00% (36/50) in the malignant group were postmenopausal (p < 0.05). Bilateral adnexal masses were significantly more frequent in the malignant group (44.0%, 22/50) compared to the benign group (9.9%, 9/91) (p < 0.001). The range of maximum diameters for the adnexal masses included in the study was 4–20 cm for the benign group (n = 91). For the malignant group (n = 50), the maximum diameters of the tumors included in the study ranged from 5.0 mm to 200.0 mm. Histopathological examination revealed a spectrum of diagnoses. Among the 50 malignant cases, the most common histological type was serous carcinoma (n = 37, 74%), followed by common types such as endometrioid (n = 5, 10%), mucinous (n = 5, 10%), and clear cell (n = 3, 6%). Metastatic tumors to the ovary were identified in three cases, originating from the gastrointestinal system (GIS). One case initially presenting as an adnexal mass was confirmed as leiomyosarcoma. Benign diagnoses included common benign types, such as serous cystadenomas, endometriomas, mature cystic teratomas, and mucinous cystadenomas.
The ultrasonographic characteristics differentiating benign and malignant masses are detailed in Table 1 and Fig. 1. Malignant masses were significantly more likely to exhibit complex morphology, including the presence of solid components (94% vs. 34.1%, p < 0.001) and septations (p < 0.001). Regarding the cystic masses in the malignant group, 6% (3/50) of malignant masses were predominantly cystic on initial USG description; however, a detailed review indicated these often contained subtle solid areas or thick septations contributing to their malignant classification rather than being purely simple cysts. Conversely, 34.1% (31/91) of benign masses had solid components (e.g., teratomas, etc.). Irregular outer surfaces and heterogeneous echogenicity were significantly more common in malignant masses (94% vs. 31.9%, p < 0.001). Anechogenicity was more frequent in benign masses (68.1% vs. 6%, p < 0.05), while malignant masses more often showed mixed echogenicity. Ascites was strongly associated with malignancy, detected in 54.0% (27/50) of malignant cases compared to only 2.2% (2/91) of benign cases (p < 0.001).
| Parameters | Benign n (%) | Malignant n (%) | Total n (%) |
| Unilateral | 82 (90.1%) | 28 (56.0%) | 110 (78.0%) |
| Bilateral | 9 (9.9%) | 22 (44.0%) | 31 (22.0%) |
| Cystic | 60 (65.9%) | 3 (6.0%) | 63 (44.7%) |
| Solid | 31 (34.1%) | 47 (94.0%) | 78 (55.3%) |
| Anechoic | 62 (68.1%) | 3 (6.0%) | 63 (46.1%) |
| Heterogeneous | 29 (31.9%) | 47 (94.0%) | 78 (53.9%) |
| Smooth surface | 62 (68.1%) | 3 (6.0%) | 63 (46.1%) |
| Irregular surface | 29 (31.9%) | 47 (94.0%) | 78 (53.9%) |
| Ascites, no | 89 (97.8%) | 23 (46.0%) | 112 (79.4%) |
| Ascites, yes | 2 (2.2%) | 27 (54.0%) | 29 (20.6%) |
| Total, n (%) | 91 (100%) | 50 (100%) | 141 (100%) |
Fig. 1.Ultrasonographic images of adnexal mass.
A comparison of preoperative serum biochemical markers showed significantly higher mean levels of CA125 (p < 0.001) and CA15-3 (p < 0.001) in the malignant group compared to the benign group (Table 2). No statistically significant differences were observed between the groups for mean serum levels of CA19-9 (p = 0.53), AFP (p = 0.08), or CEA (p = 0.66).
| Parameters | Benign Group Mean ± SD | Malignant Group Mean ± SD | p-value |
| CA125 | 44.7 ± 105.9 | 893.79 ± 1914.8 | <0.001 |
| CA19-9 | 15.5 ± 20.2 | 56.9 ± 137.1 | 0.530 |
| CA15-3 | 11.8 ± 6.1 | 71.8 ± 128.3 | <0.001 |
| AFP | 2.5 ± 1.7 | 2.2 ± 1.5 | 0.080 |
| CEA | 1.3 ± 0.7 | 6.8 ± 36.6 | 0.660 |
| Age | 52.3 ± 12.6 | 57.0 ± 13.7 | 0.040* |
*p-values calculated using Independent Samples t-test for age; Mann-Whitney U test for biomarker levels. CA: Cancer Antigen; SD: standard deviation; AFP: Alpha-Fetoprotein; CEA: Carcinoembryonic Antigen. |
Multivariate backward stepwise logistic regression analysis identified three independent USG predictors of malignancy: presence of ascites (OR = 23.22, p < 0.001), presence of septations (OR = 8.99, p = 0.01), and irregular tumor surface (OR = 5.91, p = 0.005) (Table 3).
| Parameters | Odds Ratio | p-value |
| Ascites | 23.22 | <0.001 |
| Irregularity | 5.91 | 0.005 |
| Septation | 8.99 | 0.010 |
The diagnostic performance of RMI-3 and RMI-4 using a cut-off value of 200 and 450 is presented in Tables 4 and 5.
| RMI-3 value | Total | ||
| <200 | ≥200 | ||
| Benign (%) | 76 (83.5%) | 15 (16.5%) | 91 (100%) |
| Malign (%) | 14 (28%) | 36 (72%) | 50 (100%) |
| Total | 90 (63.8%) | 51 (36.2%) | 141 (100%) |
RMI-3: Risk of Malignancy Indices-3. |
| RMI-4 value | Total | ||
| <450 | ≥450 | ||
| Benign (%) | 77 (84.6%) | 14 (15.4%) | 91 (100%) |
| Malign (%) | 13 (26%) | 37 (74%) | 50 (100%) |
| Total | 90 (63.8%) | 51 (36.2%) | 141 (100%) |
RMI-4: Risk of Malignancy Indices-4. |
RMI-4 demonstrated slightly higher sensitivity (74.0% vs. 72.0%) and specificity (84.6% vs. 83.5%) compared to RMI-3. While the differences in sensitivity and specificity between RMI-3 and RMI-4 were marginal in this cohort, a formal statistical comparison of their diagnostic accuracy metrics (e.g., using McNemar’s test or comparing AUCs (The Areas Under the Curve)) was not performed due to the overlapping nature of the indices and modest sample size, but RMI-4 showed a trend towards slightly better performance.
One case, histopathologically confirmed as benign (endometrioma), had an RMI-3 score >200 but an RMI-4 score <450, leading to its correct reclassification by RMI-4.
4. Discussion
The accurate preoperative differentiation of benign from malignant adnexal masses is a critical challenge in gynecologic oncology, directly influencing patient counseling, surgical planning, and referral pathways [5]. This study aimed to evaluate the contribution of clinical data, specific ultrasonographic (USG) features, and serum biomarkers to malignancy prediction in a cohort of patients presenting with adnexal masses at our institution and to compare the performance of RMI-3 and RMI-4. Our findings underscore the value of a multimodal approach, confirming that advanced patient age, bilateral tumor presentation, specific USG markers (notably ascites, septations, and irregular surfaces), and elevated levels of CA125 and CA15-3 are significantly associated with malignancy. The substantial predictive value of certain USG features identified in our logistic regression analysis aligns well with established knowledge. The presence of ascites yielded the highest odds ratio (OR = 23.22), consistent with numerous studies linking ascites to advanced-stage ovarian cancer and peritoneal involvement [5, 9]. Similarly, the significance of septations (OR = 8.99) and irregular tumor surfaces (OR = 5.91) reflects tumor complexity and potentially invasive growth patterns, corroborating the morphological criteria emphasized by the IOTA group and incorporated into models like the ADNEX model [9, 10]. The higher frequency of bilateral masses in malignant cases 44% vs. 9.9%) also serves as a readily identifiable clinical indicator suggestive of malignancy, consistent with previous reports [13, 14]. Regarding biochemical markers, our results confirmed the established role of CA125 as a key predictor, with significantly elevated levels in the malignant group. Interestingly, CA15-3 also showed a significant association with malignancy in our cohort. While CA15-3 is primarily associated with breast cancer, its elevation has been reported in other malignancies, including some ovarian cancers. However, its routine use for evaluating adnexal masses is not standard practice [15]. The lack of significant differences for CA19-9, AFP, and CEA reinforces their limited utility in the primary differentiation of epithelial ovarian cancer. However, they remain relevant in specific contexts (e.g., suspected germ cell tumors for AFP, mucinous tumors for CA19-9 and CEA, or metastatic disease). It is crucial to acknowledge that CA125 is not elevated in all ovarian malignancies, particularly in early-stage disease and certain histological subtypes, such as mucinous, clear cell, or germ cell tumors. Recent meta-analyses confirm CA125’s relatively high sensitivity (around 82%) but highlight its lower specificity due to elevation in benign conditions [4, 16]. This underscores the need for complementary diagnostic markers.
We compared the diagnostic performance of RMI-3 and RMI-4, two widely used algorithms. In our cohort, both indices performed comparably, with RMI-4 showing marginally better sensitivity (74% vs. 72%) and specificity (84.6% vs. 83.5%) at a cut-off of 450. This level of performance is broadly consistent with ranges reported in previous meta-analyses, which typically show sensitivities around 70–85% and specificities around 75–90% for RMI variations [16]. The Singh et al. [16] (2025) meta-analysis reported a pooled AUC of 0.8508 for RMI, comparable to HE4 (Human Epididymis Protein 4) (AUC 0.8586) and ROMA (the Risk of Ovarian Malignancy Algorithm) (AUC 0.8619), all outperforming CA125 alone (AUC 0.8128) [16]. The slight improvement with RMI-4, which incorporates tumor size, suggests that size may add incremental value, although the difference was not statistically significant in our analysis. The case where RMI-4 correctly reclassified a benign mass initially flagged by RMI-3 illustrates the potential impact of incorporating size; this specific case involved a large simple cyst in a postmenopausal woman with slightly elevated CA125 where the large size likely contributed to the RMI-4 score remaining below the threshold, mitigating the influence of other factors that pushed the RMI-3 score above 200. This highlights the nuances of index interpretation and the importance of considering all clinical and imaging data.
While our study focused on RMI, we acknowledge that the IOTA ADNEX model has demonstrated superior diagnostic performance, with recent studies showing AUC of 0.958 compared to 0.886 for IOTA Simple Rules. However, ADNEX implementation requires specialized 20-hour training programs and online calculation tools that may not be available in resource-limited healthcare settings. Our decision to focus on RMI reflects the practical realities of healthcare delivery in low-income countries where cost-effectiveness and accessibility are paramount. The global burden of ovarian cancer severely affects women in resource-constrained environments, where treatment costs often exceed typical healthcare expenditure capacity. RMI maintains critical advantages for resource-limited clinical settings: it requires only readily available parameters and can be calculated manually without specialized software. This study addresses the gap between optimal diagnostic performance and practical implementation feasibility in healthcare systems with limited resources.
This study has several limitations inherent to its design. We acknowledge that advanced diagnostic modalities, particularly the IOTA ADNEX model, have demonstrated superior diagnostic performance compared to RMI approaches in well-resourced clinical environments. Our choice to focus on RMI was guided by practical implementation considerations in resource-limited settings rather than optimal diagnostic performance alone. The single-center design may limit generalizability, although our results are representative of many healthcare systems with similar resource constraints. The sample size (n = 141, with 50 malignant cases) is modest, which may limit statistical power, particularly for subgroup analyses or detecting smaller effect sizes. We did not specifically analyze borderline ovarian tumors as a separate category, which represents a distinct management challenge. Furthermore, while RMI remains a valuable tool, newer models, such as the IOTA ADNEX model, which predicts the probability of specific malignancy subtypes (benign, borderline, stage I invasive, stage II–IV invasive, metastatic), are increasingly recognized for providing more nuanced risk assessments.
Recent large validation studies and meta-analyses suggest that ADNEX generally offers superior or comparable performance to RMI, particularly in specific patient groups, such as postmenopausal women [9, 10, 17]. Additionally, the IOTA Simple Rules, when applicable, have shown high diagnostic accuracy, sometimes exceeding RMI [18, 19].
Future research should focus on developing simplified versions of advanced diagnostic algorithms that maintain predictive accuracy while reducing implementation barriers. Cost-effectiveness analyses comparing different diagnostic approaches in diverse healthcare contexts would provide valuable insights for clinical decision-making. Moreover, future research should also ideally involve prospective, multicenter designs with larger cohorts to validate our findings and enable more robust comparisons between different diagnostic models, including ADNEX, Simple Rules, and potentially AI-driven algorithms that integrate complex imaging features [20, 21]. Investigating the role of newer biomarkers, such as HE4 (forming the ROMA index with CA125) or novel miRNA panels, alongside detailed USG assessment according to standardized IOTA criteria, could further enhance preoperative risk stratification [4, 16, 22, 23].
5. Conclusions
In conclusion, this study conducted at Azerbaijan Medical University reaffirms the significant value of a multimodal approach for the preoperative differentiation of benign and malignant adnexal masses. Our findings demonstrate that integrating patient age, tumor laterality (bilateral presentation), specific ultrasonographic features indicative of complexity (particularly the presence of ascites, septations, and irregular tumor surfaces), and elevated serum levels of CA125 and CA15-3 significantly improves the prediction of malignancy. While both RMI-3 and RMI-4 provided reasonable diagnostic accuracy in our cohort, RMI-4 showed marginally better performance, suggesting a potential incremental benefit from incorporating tumor size. However, reliance solely on risk indices is insufficient, and careful consideration of all clinical and imaging findings, potentially incorporating newer models like IOTA ADNEX where feasible, is essential for optimal patient triage and management. Further prospective, multicenter studies are warranted to validate these findings and continue refining preoperative risk assessment strategies for women presenting with adnexal masses. While acknowledging the superior performance of advanced diagnostic modalities, our findings demonstrate that RMI-based approaches maintain clinical utility where implementation barriers limit access to more sophisticated tools.
Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Author contributions
AI—Conceptualization; Methodology; Formal analysis and investigation; Writing-original draft preparation; Writing-review and editing; Supervision.
Ethics approval and consent to participate
Since the study was retrospective and observational, the Clinical Research Ethics Committee of Azerbaijan Medical University approved the study and waived the need for written informed consent (Decision number #238, date 06 December 2024).
Acknowledgment
We would like to acknowledge Fidan Novruzova, and Aygun Hasanova, for their outstanding scientific contribution to this manuscript.
Funding
This research received no external funding.
Conflict of interest
The author declares no conflict of interest.
