Improved Clinical Value of Combining IOTA LR2 with CEUS for Preoperative Assessment of Adnexal Masses

Improved Clinical Value of Combining IOTA LR2 with CEUS for Preoperative Assessment of Adnexal Masses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improved Clinical Value of Combining IOTA LR2 with CEUS for Preoperative Assessment of Adnexal Masses Cai Tian, Ying Hao, Jingqiao Liu, Yiwei Han, Zijia Shi, Tianyi Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7698811/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective: To evaluate the diagnostic performance of the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2) alone and in combination with contrast-enhanced ultrasound (CEUS) for preoperative differentiation of adnexal masses. Methods: A total of 198 surgically resected adnexal masses from 171 patients were assessed by LR2 and LR2 combined with CEUS, with histopathology as the reference standard. Diagnostic accuracy was compared using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results: LR2 achieved an AUC of 0.90 (95% CI: 0.85–0.94), with 93.9% sensitivity and 86.4% specificity. The addition of CEUS improved specificity to 90.9% and accuracy to 91.4% (AUC = 0.92, 95% CI: 0.88–0.97). Subgroup analyses confirmed the superiority of LR2 + CEUS over LR2 alone in both pre- and postmenopausal women. Conclusion: While LR2 offers high sensitivity, its specificity is limited. Combining LR2 with CEUS significantly reduces misdiagnosis, providing greater clinical utility for preoperative assessment of adnexal tumors. IOTA LR2 CEUS adnexal mass ultrasound diagnosis 1. Introduction Ovarian cancer is one of the most common gynecologic malignancies and the fifth leading cause of cancer-related death among women. It ranks third in incidence among female reproductive system tumors—after uterine corpus and cervical cancers—but first in mortality, posing a serious threat to women’s health [ 1 , 2 ]. In 2022, approximately 324,000 new cases were diagnosed worldwide, resulting in over 206,000 deaths [ 3 ]. Due to the asymptomatic nature of early-stage ovarian cancer and inadequate screening, over 70% of patients present with advanced-stage disease at initial diagnosis, resulting in a 5-year survival rate of approximately 28% [ 4 , 5 ], which imposes a significant burden on global public health [ 6 , 7 ]. Therefore, early and accurate differentiation of benign from malignant adnexal masses has become a critical step in clinical decision-making and is key to improving patient prognosis and survival. Since the mid-1980s, ultrasound has been the first-line imaging modality for evaluating adnexal lesions due to its noninvasiveness, cost-effectiveness, and lack of ionizing radiation [ 8 ]. However, conventional ultrasound assessment of adnexal masses relies heavily on the examiner’s subjective judgment, and diagnostic accuracy and consistency are dependent on operator skill and experience [ 9 ]. To improve objectivity and diagnostic performance, various ultrasound-based guidelines, scoring systems, and predictive models have been developed in recent years [ 10 , 11 ]. Among these, the International Ovarian Tumor Analysis (IOTA) group has conducted the largest multicenter, prospective study to date, establishing a series of standardized ultrasound diagnostic algorithms—including the Simple Rules (SR), Simple Rules Risk (SRR), and IOTA Logistic Regression Model 2 (LR2) [ 12 ]. Of these, the LR2 model has been widely adopted in clinical practice [ 13 ]. Nevertheless, existing scoring systems and predictive models still have limitations in characterizing complex adnexal masses: studies report that approximately 18%–31% of masses remain indeterminate, potentially masking early ovarian malignancies and delaying optimal treatment [ 14 ]. Meanwhile, Contrast-Enhanced Ultrasound (CEUS) has rapidly advanced and demonstrated clear advantages in characterizing the vascular features of adnexal masses [ 15 ]. Compared with conventional color Doppler ultrasound, CEUS can dynamically and in real time depict microvascular perfusion within the lesion with greater clarity, thereby enhancing the accuracy of differentiating benign from malignant complex masses [ 16 ]. However, the clinical utility of combining IOTA LR2 with CEUS remains to be fully elucidated. In this study, we systematically evaluated the diagnostic performance of the IOTA LR2 model alone and in combination with CEUS, using postoperative histopathology as the reference standard, to explore their practical value in preoperative assessment and clinical decision-making for adnexal masses. 2. Materials and Methods 2.1 Participants Data were collected from 175 female patients with 202 ultrasound-diagnosed adnexal masses who underwent surgery at the Department of Gynecology, the Second Hospital of Hebei Medical University between July 2019 and June 2022. Recorded variables included age, menstrual status (patients aged ≥ 50 years or theose who had undergone hysterectomy were classified as postmenopausal), personal history of ovarian or breast cancers, number of first-degree relatives with ovarian or breast cancers, the record of this surgery, and the complete postoperative clinical information including pathology results. The inclusion criteria were as follows: 1. Adnexal mass detected by preoperative color Doppler ultrasonography; 2. surgical treatment at the Department of Gynecology, The Second Hospital of Hebei Medical University, within 120 days after the last ultrasonography; 3. Ultrasonography performed within 2 weeks before surgery; 4. Pathological results for the adnexal tumor; 5.Complete clinical data. The exclusion criteria were as follows: 1. physiological cysts; 2. Adnexal masses combined with pregnancy; 3. History of adnexal malignant tumors; 4. Unclear pathological results; 5. Poor image quality or images without diagnostic signs. Finally, based on the inclusion and exclusion criteria, a total of 171 patients (with 198 ultrasound-diagnosed adnexal masses) aged 9–79 years were included in this study; of the masses, 124 occurred in premenopausal women, and 74 in postmenopausal women. This study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University(2022-R763), and written informed consent was obtained from all patients. 2.2 Ultrasound examination Ultrasonography was performed by a sonographer with ≥ 7 years of experience in gynecological ultrasonography using a GE LOGIQ E10 ultrasound system. A comprehensive examination of the uterus, adnexa, and pelvis was conducted, and image acquisition included location, number, size, shape, echogenicity, borders, posterior echogenicity, blood flow, and concomitant ascites of the mass. Examinations were obtained primarily with a 5–9 MHz intracavitary ultrasound probe. If the mass was too large to be visualized with transcavitary ultrasound, and if the patient is unmarried or cannot tolerate the transvaginal approach, complementary transabdominal scans were performed using a 1–5 MHz curved probe. CEUS was performed using the contrast agent SonoVue (Bracco, Milan, Italy). Each SonoVue contained 59 mg of phospholipid-coated sulfur hexafluoride (SF6) gas microbubbles, which were added to 5 mL of saline for injection and shaken vigorously to obtain a microbubble suspension with an average microbubble diameter of 2.5 µm. For bilateral lesions with solid components, each lesion was assessed and classified independently. All ultrasound images were stored in DICOM format on the ultrasound working system of the Second Hospital of Hebei Medical University for subsequent analysis. 2.3 Image analysis The ultrasound characteristics of adnexal masses were evaluated according to the LR2 model and LR2 combined with CEUS. To minimize bias, all image analyses were performed in a blinded manner, with the sonographers unaware of the patients’ histopathological findings. The LR2 model included: 1. Age (years); 2. Presence of peritoneal effusion; 3. Presence of a papillary protuberance with blood flow; 4. Maximum diameter of the solid portion (if the maximum diameter was ≥ 50 mm, it was recorded as 50 mm because a diameter > 50 mm does not increase the risk value with its growth); 5. Presence of irregularities in the inner wall of the cyst; and 6. Presence of acoustic shadows. The benign and malignant risk values (%) were calculated by applying the LR2 software to the above six parameters. The risk value ranges from 1% to 95%, taking ≥ 10% as the malignant cutoff value, i.e., < 10% is considered benign and ≥ 10% is suspected malignant. Ultrasonography scoring criteria were used to assess the characteristics of peritoneal enhancement (0 points for intact, 1 point for inconspicuous or incomplete), the thickness of peritoneal enhancement (0 points for homogeneous, 1 point for uneven thickness), the characteristics of enhancement at peak enhancement compared with that of the peritoneum (0 points for no enhancement of the enhancement within the lesion, 1 point for low enhancement, and 2 points for equivocal or high enhancement), the internal area of lesion enhancement (homogeneous enhancement = 0 points, non-homogeneous enhancement = 1 point), and segregation within the lesion (no segregation-like enhancement = 0 points, segregation-like enhancement = 1 point), with a total score of 0–6 points, and a total score of ≥ 4 points was classified as malignant, and ≤ 3 points was classified as benign. 2.4 Histopathological examination All cases were evaluated by a pathologist with 10 years' experience in ovarian disease, who was blinded to the ultrasound findings and clinical data. Histopathological diagnoses were used as the reference standard and were compared with the ultrasound-based assessments (LR2 and LR2 combined with CEUS). 2.5 Statistical analysis SPSS 20.0 statistical software were applied for statistical analysis. The chi-square test was used for categorical variable comparisons and the independent sample t-test or rank-sum test was used for continuous variable comparisons. Kappa coefficient was used to assess intergroup consistency, with κ ≥ 0.75 indicating high consistency, 0.40 ≤ κ < 0.75 indicating medium consistency, and κ < 0.40 indicating low consistency. McNemar’s test was used to examine marginal asymmetry between paired classifications. The diagnostic performance of the ultrasound classification system was tested by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Statistical significance was set at P < 0.05. 3. Results 3.1 Patient characteristics A total of 171 patients were included in this study, involving 198 ultrasound-diagnosed ovarian masses. The patients' age ranged from 9 to 79 years old. The postoperative pathology showed that 61 cases were malignant (30.8%), 7 cases were borderline (3.5%), and 130 cases were benign (65.7%) (Table 1). Malignant tumors included: serous cystadenocarcinoma (n = 40), mucinous cystadenocarcinoma (n = 8), granulosa cell tumor (n = 5), clear cell carcinoma (n = 3), endometrioid carcinoma (n = 3), sarcoma (n = 1), and dysgerminoma (n = 1). Borderline tumors comprised: borderline serous cystadenoma (n = 4), borderline mucinous cystadenoma (n = 2), and borderline seromucinous tumor (n = 2). Benign lesions included: mature teratoma (n = 37), paratubal (mesosalpinx) cyst (n = 21), serous cystadenoma (n = 13), hydrosalpinx (n = 13), fibrothecoma (n = 12), endometriotic cyst (endometrioma) (n = 11), hemorrhagic corpus luteum (n = 10), simple cyst (n = 5), mucinous cystadenoma (n = 4), and other benign entities (n = 4). 3.2 Overall diagnostic performance of IOTA LR2 As shown in Table 2, the IOTA LR2 model classified 118 of 198 lesions (59.6%) as benign and 80 (40.4%) as non-benign (including borderline and malignant lesions). Compared with postoperative histopathology, LR2 achieved a sensitivity of 93.94%, specificity of 86.36%, positive predictive value (PPV) of 77.5%, negative predictive value (NPV) of 96.61%, and overall accuracy of 88.89%. The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 6.89 and 0.07. Agreement between LR2 and histopathology was substantial (Cohen’s κ = 0.763), indicating good concordance between the two methods. McNemar’s test showed a statistically significant marginal asymmetry (P < 0.01), suggesting LR2 tended to classify more lesions as non-benign compared with histopathology. 3.3 Diagnostic performance of IOTA LR2 combined with CEUS When LR2 was combined with CEUS, 125 of 198 lesions (63.1%) were classified as benign and 73 (36.9%) as nonbenign (Table 2). The combined method demonstrated a sensitivity of 92.42%, specificity of 90.91%, PPV of 83.56%, NPV of 96 %, and overall accuracy of 91.41%. The PLR and NLR were 10.17 and 0.08. When LR2 was combined with CEUS, the diagnostic concordance with histopathology further improved (Cohen’s κ = 0.81), which exceeds the 0.75 threshold for high agreement. McNemar’s test yielded P = 0.143, indicating no significant marginal asymmetry between the combined method and histopathology. 3.4 Subgroup analyses In premenopausal women, 105 of 125 lesions (84.7%) were benign, and 19 (15.3%) were non-benign on pathology. LR2 assessed 96 lesions (77.4%) as benign and 28 (22.6%) as nonbenign (Table 3). LR2 achieved a sensitivity of 89.47%, specificity of 89.52%, PPV of 60.71%, NPV of 97.92%, PLR of 8.54, NLR of 0.12, and diagnostic accuracy of 89.52%. Concordance with pathological diagnosis was moderate (Cohen’s κ = 0.662). However, McNemar’s test indicated a significant marginal asymmetry (p = 0.022). When LR2 was combined with CEUS, 100 of 125 lesions (80.6%) were classified as benign and 24 (19.4%) as nonbenign (Table 3). The combined method demonstrated a sensitivity of 89.47%, specificity of 93.33%, PPV of 70.83%, NPV of 98%, and diagnostic accuracy of 92.74%. The PLR and NLR were 13.41 and 0.07. Concordance with pathological diagnosis was good (Cohen’s κ = 0.748), and McNemar’s test showed no significant marginal asymmetry (p = 0.18). In postmenopausal women, 24 of 74 lesions (36.5%) were benign, and 47 (63.5%) were non-benign on pathology (Table 4). LR2 assessed 22 lesions (29.7%) as benign and 52 (70.3%) as nonbenign. LR2 achieved a sensitivity of 95.74%, specificity of 74.07%, PPV of 86.54%, NPV of 90.91%, and diagnostic accuracy of 87.84%. The PLR and NLR were 3.69% and 0.06%. Concordance with pathological diagnosis was moderate (Cohen’s κ = 0.662). McNemar’s test revealed a significant marginal asymmetry (P < 0.01). When LR2 was combined with CEUS, 25 of 125 lesion (33.8%) were classified as benign and 49 (66.2%) as nonbenign. The combined method demonstrated a sensitivity of 93.62%, specificity of 81.48%, PPV of 89.8%, NPV of 88%, and diagnostic accuracy of 89.19%. The PLR and NLR were 5.06% and 0.23%. Concordance with the pathological diagnosis was good (Cohen’s κ = 0.763). McNemar’s test indicated no significant marginal asymmetry (P = 0.727). 3.5 ROC analysis ROC analysis demonstrated an AUC of 0.90 (95% CI: 0.85–0.94) for LR2 alone and an AUC of 0.92 (95% CI: 0.88–0.97) for LR2 combined with CEUS（(Table 2）。 4. Discussion Ovarian cancer remains the most lethal gynecologic malignancy, largely due to late-stage diagnosis and the lack of effective screening strategies [ 17 ]. Accurate preoperative differentiation of adnexal masses is therefore critical for optimizing treatment strategies and improving patient outcomes [ 18 , 19 ]. Conventional ultrasound is widely used as the first-line imaging tool, but its accuracy depends heavily on examiner experience and subjective interpretation. To improve diagnostic consistency, the IOTA group has developed standardized models, among which the LR2 model is one of the most validated tools for risk stratification [ 20 ]. IOTA LR2 is a product of IOTA's prospective study based on a large sample, which focuses more on ultrasound characteristics, including six parameters such as the patient's age, the presence of peritoneal effusion, the presence of papillae with blood flow, the maximal diameter of the solid portion, the regularity of the cyst's inner wall, and the presence of an acoustic shadow, and therefore has a high degree of sensitivity to detect as many true positive patients as possible [ 21 ]. In our study, we analyzed 171 patients with 198 adnexal masses and found that IOTA LR2 model achieved a sensitivity of 93.94%, a specificity of 86.36% and overall accuracy of 88.89%, findings that are comparable to those reported in previous multicenter investigations. Timmerman et al. first demonstrated that LR2 provides high sensitivity but moderate specificity in differentiating adnexal masses [ 22 ]. A subsequent meta-analysis by Meys et al. also confirmed that while LR2 offers robust sensitivity, its specificity remains suboptimal, resulting in a proportion of false-positive cases[ 23 ]. Our results align with these observations, underscoring the limitations of relying on LR2 alone in clinical practice. CEUS has been introduced as a complementary technique, as it provides real-time visualization of microvascular perfusion and reflects angiogenesis, a hallmark of malignancy[ 24 , 25 ]. Several studies have shown that CEUS improves diagnostic accuracy compared with conventional doppler ultrasound[ 26 , 27 ]. Our findings extend this knowledge by demonstrating that combining LR2 with CEUS significantly improves specificity (90.9% vs. 86.4%) and overall accuracy (91.4% vs. 88.9%), while maintaining high sensitivity. This combined approach yielded a positive likelihood ratio exceeding 10, meeting the threshold for an excellent diagnostic test, and clearly outperforming LR2 alone. This indicates that when used in combination with CEUS, LR2 can significantly enhance specificity and reduce the probability of misdiagnosis compared to using LR2 alone. In clinical practice, PPV and NPV are the diagnostic parameters most familiar to clinicians. In our study, LR2 combined with CEUS improved the PPV from 77.5% to 83.6% while maintaining a high NPV (> 96%), thereby reducing the risk of unnecessary surgery for benign lesions. Importantly, likelihood ratios, which are not influenced by disease prevalence, further confirmed the superiority of the combined method: the positive likelihood ratio of LR2 + CEUS exceeded 10, meeting the criterion of an “ideal diagnostic test,” compared with 6.89 for LR2 alone. ROC analysis also supported this finding, with the AUC increasing from 0.90 to 0.92, indicating that both methods had high diagnostic efficacy, but LR2 + CEUS provided a more balanced and clinically reliable diagnostic profile. Subgroup analyses provided further insight into the robustness of this combined strategy. In both premenopausal and postmenopausal women, LR2 + CEUS consistently demonstrated higher specificity and better concordance with histopathology compared with LR2 alone. Previous research has suggested that menopausal status may influence diagnostic performance in models such as O-RADS or GI-RADS [ 28 , 29 ]. Our findings indicate that LR2 combined with CEUS retains diagnostic superiority across different hormonal backgrounds, thus broadening its applicability in routine practice. Several limitations of our study should be acknowledged. First, this was a single-center study with a relatively limited sample size, which may restrict the generalizability of the findings. Second, interobserver variability in CEUS interpretation was not formally assessed. Future multicenter prospective studies with larger populations and standardized CEUS assessment criteria are warranted to validate and extend our results. 5. Conclusion Our results show that the IOTA LR2 model has high sensitivity but low specificity, and can help sonographers initially determine the benign or malignant nature of adnexal masses. The combination of LR2 with CEUS can significantly reduce the misdiagnosis rate of LR2 alone; therefore, the combination of LR2 with CEUS has a higher value for clinical application, and can help sonographers more accurately assess the nature of adnexal masses in their clinical work. Declarations Acknowledgements We gratefully acknowledge all patients, investigators, and data managers for their valuable contributions to this study. Authors’ contributions Cai Tian: Conceptualization, Methodology, Project administration; Ying Hao: Writing – original draft, Data Curation, Formal analysis; Jingqiao Liu: Investigation, Visualization; Yiwei Han: Software, Visualization; Zijia Shi: Resources, Project administration; Tianyi Guo: Methodology, Statistical analysis; Xiaonan Yan: Supervision, Methodology, Writing - Review & Editing. Funding This research did not receive funding. Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the declaration of Helsinki. This study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University（2022-R763）. Written informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors have no conflict of interest to declare. References R.L. Siegel, K.D. Miller, H.E. Fuchs, A. Jemal, Cancer statistics, 2022, CA: a cancer journal for clinicians, 72 (2022) 7-33. H. Fei, S. Chen, C. Xu, Bioinformatics analysis of gene expression profile of serous ovarian carcinomas to screen key genes and pathways, Journal of ovarian research, 13 (2020) 82. C. Giuseppe, W. S John, C.J.J. William, Ovarian Cancer: A Review, (2025). E. Sarah, S. Kelly, Z. Qin, I. Alexia, S. Yukio, G. Ginger J, L.R. Kara, Z. William A 3rd, Y. Effi, O.C. Roisin E, Z. Oliver, C.J.G.O. Dennis S, Outcomes and long-term follow-up by treatment type for patients with advanced-stage ovarian cancer managed at a tertiary cancer center: A Memorial Sloan Kettering Cancer Center Team Ovary study, 169 (2022). Committee Opinion No. 716: The Role of the Obstetrician-Gynecologist in the Early Detection of Epithelial Ovarian Cancer in Women at Average Risk, Obstetrics and gynecology, 130 (2017) e146-e149. J. Ferlay, M. Colombet, I. Soerjomataram, D.M. Parkin, M. Piñeros, A. Znaor, F. Bray, Cancer statistics for the year 2020: An overview, International journal of cancer, (2021). T. Fan, Q. Zhao, J.J. Chen, W.T. Chen, M.L. Pearl, Clinical significance of circulating tumor cells detected by an invasion assay in peripheral blood of patients with ovarian cancer, Gynecologic oncology, 112 (2009) 185-191. A. Deslandes, N. Parange, J.T. Childs, B. Osborne, C. Panuccio, A. Croft, E. Bezak, How long does a transvaginal ultrasound examination for endometriosis take in comparison to a routine transvaginal ultrasound examination?, Australasian journal of ultrasound in medicine, 25 (2022) 20-27. L. Valentin, Use of morphology to characterize and manage common adnexal masses, Best practice & research. Clinical obstetrics & gynaecology, 18 (2004) 71-89. C. Van Holsbeke, A. Daemen, J. Yazbek, T.K. Holland, T. Bourne, T. Mesens, L. Lannoo, A.S. Boes, A. Joos, A. Van De Vijver, N. Roggen, B. de Moor, E. de Jonge, A.C. Testa, L. Valentin, D. Jurkovic, D. Timmerman, Ultrasound experience substantially impacts on diagnostic performance and confidence when adnexal masses are classified using pattern recognition, Gynecologic and obstetric investigation, 69 (2010) 160-168. J. Koneczny, A. Czekierdowski, M. Florczak, P. Poziemski, N. Stachowicz, D. Borowski, The use of sonographic subjective tumor assessment, IOTA logistic regression model 1, IOTA Simple Rules and GI-RADS system in the preoperative prediction of malignancy in women with adnexal masses, Ginekologia Polska, 88 (2017) 647-653. P. Buranaworathitikul, V. Wisanumahimachai, N. Phoblap, Y. Porngasemsart, W. Rugfoong, N. Yotchana, P. Uthaichalanont, T. Jiampochaman, C. Kunanukulwatana, A. Thiamkaew, S. Luewan, C. Tantipalakorn, T.J.C. Tongsong, Accuracy of O-RADS System in Differentiating Between Benign and Malignant Adnexal Masses Assessed via External Validation by Inexperienced Gynecologists, 16 (2024). D. Timmerman, A.C. Testa, T. Bourne, E. Ferrazzi, L. Ameye, M.L. Konstantinovic, B. Van Calster, W.P. Collins, I. Vergote, S. Van Huffel, L. Valentin, Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group, Journal of clinical oncology, 23 (2005) 8794-8801. E.M. Meys, J. Kaijser, R.F. Kruitwagen, B.F. Slangen, B. Van Calster, B. Aertgeerts, J.Y. Verbakel, D. Timmerman, T. Van Gorp, Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis, European journal of cancer (Oxford, England : 1990), 58 (2016) 17-29. J. Wang, F. Lv, X. Fei, Q. Cui, L. Wang, X. Gao, Z. Yuan, Q. Lin, Y. Lv, A. Liu, Study on the characteristics of contrast-enhanced ultrasound and its utility in assessing the microvessel density in ovarian tumors or tumor-like lesions, International journal of biological sciences, 7 (2011) 600-606. W. Shentu, Y. Zhang, J. Gu, F. Wang, W. Zhao, C. Liu, Z. Lin, Y. Wang, C. Liu, Y. Chen, Q. Fan, H. Wang, Contrast-enhanced ultrasonography for differential diagnosis of adnexal masses, Frontiers in oncology, 12 (2022) 968759. C. Stewart, C. Ralyea, S. Lockwood, Ovarian Cancer: An Integrated Review, Seminars in oncology nursing, 35 (2019) 151-156. S. Granberg, M. Wikland, I. Jansson, Macroscopic characterization of ovarian tumors and the relation to the histological diagnosis: criteria to be used for ultrasound evaluation, Gynecologic oncology, 35 (1989) 139-144. M.J. Engelen, A.G. van der Zee, E.G. de Vries, P.H. Willemse, Debulking surgery for ovarian epithelial cancer performed by a gynaecological oncologist improved survival compared with less specialised surgeons, Cancer treatment reviews, 32 (2006) 320-323. M.A.A. Basha, M.I. Metwally, S.A. Gamil, H.M. Khater, S.A. Aly, A.A. El Sammak, M.M.A. Zaitoun, E.M. Khattab, T.M. Azmy, N.A. Alayouty, N. Mohey, H.N. Almassry, H.Y. Yousef, S.A. Ibrahim, E.A. Mohamed, A.E.M. Mohamed, A.H.M. Afifi, O.A. Harb, H.Y. Algazzar, Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses, European radiology, 31 (2021) 674-684. D. Timmerman, T.H. Bourne, A. Tailor, W.P. Collins, H. Verrelst, K. Vandenberghe, I. Vergote, A comparison of methods for preoperative discrimination between malignant and benign adnexal masses: the development of a new logistic regression model, American journal of obstetrics and gynecology, 181 (1999) 57-65. T. Dirk, T. Antonia C, B. Tom, F. Enrico, A. Lieveke, K. Maja L, V.C. Ben, C. William P, V. Ignace, V.H. Sabine, V.J.J.C.O. Lil, Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group, 23 (2005). M. E M J, K. J, K. R F P M, S. B F M, V.C. B, A. B, V. J Y, T. D, V.G.J.E.J.C. T, Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis, 58 (2016). L.M. Sconfienza, N. Perrone, A. Delnevo, F. Lacelli, C. Murolo, N. Gandolfo, G. Serafini, Diagnostic value of contrast-enhanced ultrasonography in the characterization of ovarian tumors(), Journal of ultrasound, 13 (2010) 9-15. J.E. Palmer, L.J. Sant Cassia, C.J. Irwin, A.G. Morris, T.P. Rollason, Prognostic value of measurements of angiogenesis in serous carcinoma of the ovary, International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists, 26 (2007) 395-403. T. Cai, H. Yi-Wei, S. Zi-Jia, L. Ya-Wei, X. Lei, L. Xiao-Li, L.J.I.J.G.C. Jing-Qiao, Diagnostic value of the International Ovarian Tumor Analysis simple rules combined with contrast-enhanced ultrasound for adnexal masses, 35 (2025). K. Shoko, U. Tomokazu, K. Yuko, K. Naohisa, S. Shiro, M. Mika, K. Hiroaki, S. Kiyosumi, K.J.J.M.U. Fumitaka, Contrast-enhanced ultrasonography using Sonazoid(®) is useful for diagnosis of malignant ovarian tumors: comparison with Doppler ultrasound, 40 (2013). B. Mohammad Abd Alkhalik, M. Maha Ibrahime, G. Shrif A, K. Hamada M, A. Sameh Abdelaziz, E.S. Ahmed A, Z. Mohamed M A, K. Enass M, A. Taghreed M, A. Nader Ali, M. Nesreen, A. Hosam Nabil, Y. Hala Y, I. Safaa A, M. Ekramy A, M. Abd El Motaleb, A. Amira Hamed Mohamed, H. Ola A, A.J.E.R. Hesham Youssef, Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses, 31 (2020). G. Sugandha, K. Amarjit, M. Jaswinder Kaur, S. Preet Kanwal, K.J.J.C.D.R. Navkiran, Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours, 11 (2017). Tables Table 1：Pathological distribution of the resected adnexal masses Table 2：Overall diagnostic performance — LR2 vs. LR2 + CEUS Table 3. Diagnostic performance in premenopausal subgroup LR2 vs. LR2 + CEUS Table 4. Diagnostic performance in postmenopausal subgroup — LR2 vs. LR2 + CEUS Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviews received at journal 14 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Editor invited by journal 12 Nov, 2025 Editor assigned by journal 13 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Hao","suffix":""},{"id":528103929,"identity":"5dc631cf-4cef-4fd3-86c9-14ebb116adf3","order_by":2,"name":"Jingqiao Liu","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingqiao","middleName":"","lastName":"Liu","suffix":""},{"id":528103930,"identity":"cf23daab-8af8-484b-9264-cac14d4d3b8c","order_by":3,"name":"Yiwei Han","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiwei","middleName":"","lastName":"Han","suffix":""},{"id":528103931,"identity":"1a4df906-2b45-45c0-bed4-dde36c85a8d7","order_by":4,"name":"Zijia Shi","email":"","orcid":"","institution":"The First Hospital of Hebei Medical 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16:00:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":599841,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7698811/v1/4f7a0b7a-cbc0-47a0-a998-ed235b6205dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved Clinical Value of Combining IOTA LR2 with CEUS for Preoperative Assessment of Adnexal Masses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOvarian cancer is one of the most common gynecologic malignancies and the fifth leading cause of cancer-related death among women. It ranks third in incidence among female reproductive system tumors\u0026mdash;after uterine corpus and cervical cancers\u0026mdash;but first in mortality, posing a serious threat to women\u0026rsquo;s health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2022, approximately 324,000 new cases were diagnosed worldwide, resulting in over 206,000 deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the asymptomatic nature of early-stage ovarian cancer and inadequate screening, over 70% of patients present with advanced-stage disease at initial diagnosis, resulting in a 5-year survival rate of approximately 28% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which imposes a significant burden on global public health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, early and accurate differentiation of benign from malignant adnexal masses has become a critical step in clinical decision-making and is key to improving patient prognosis and survival.\u003c/p\u003e\u003cp\u003eSince the mid-1980s, ultrasound has been the first-line imaging modality for evaluating adnexal lesions due to its noninvasiveness, cost-effectiveness, and lack of ionizing radiation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, conventional ultrasound assessment of adnexal masses relies heavily on the examiner\u0026rsquo;s subjective judgment, and diagnostic accuracy and consistency are dependent on operator skill and experience [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To improve objectivity and diagnostic performance, various ultrasound-based guidelines, scoring systems, and predictive models have been developed in recent years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among these, the International Ovarian Tumor Analysis (IOTA) group has conducted the largest multicenter, prospective study to date, establishing a series of standardized ultrasound diagnostic algorithms\u0026mdash;including the Simple Rules (SR), Simple Rules Risk (SRR), and IOTA Logistic Regression Model 2 (LR2) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Of these, the LR2 model has been widely adopted in clinical practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, existing scoring systems and predictive models still have limitations in characterizing complex adnexal masses: studies report that approximately 18%\u0026ndash;31% of masses remain indeterminate, potentially masking early ovarian malignancies and delaying optimal treatment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMeanwhile, Contrast-Enhanced Ultrasound (CEUS) has rapidly advanced and demonstrated clear advantages in characterizing the vascular features of adnexal masses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Compared with conventional color Doppler ultrasound, CEUS can dynamically and in real time depict microvascular perfusion within the lesion with greater clarity, thereby enhancing the accuracy of differentiating benign from malignant complex masses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the clinical utility of combining IOTA LR2 with CEUS remains to be fully elucidated. In this study, we systematically evaluated the diagnostic performance of the IOTA LR2 model alone and in combination with CEUS, using postoperative histopathology as the reference standard, to explore their practical value in preoperative assessment and clinical decision-making for adnexal masses.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eData were collected from 175 female patients with 202 ultrasound-diagnosed adnexal masses who underwent surgery at the Department of Gynecology, the Second Hospital of Hebei Medical University between July 2019 and June 2022. Recorded variables included age, menstrual status (patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years or theose who had undergone hysterectomy were classified as postmenopausal), personal history of ovarian or breast cancers, number of first-degree relatives with ovarian or breast cancers, the record of this surgery, and the complete postoperative clinical information including pathology results. The inclusion criteria were as follows: 1. Adnexal mass detected by preoperative color Doppler ultrasonography; 2. surgical treatment at the Department of Gynecology, The Second Hospital of Hebei Medical University, within 120 days after the last ultrasonography; 3. Ultrasonography performed within 2 weeks before surgery; 4. Pathological results for the adnexal tumor; 5.Complete clinical data. The exclusion criteria were as follows: 1. physiological cysts; 2. Adnexal masses combined with pregnancy; 3. History of adnexal malignant tumors; 4. Unclear pathological results; 5. Poor image quality or images without diagnostic signs. Finally, based on the inclusion and exclusion criteria, a total of 171 patients (with 198 ultrasound-diagnosed adnexal masses) aged 9\u0026ndash;79 years were included in this study; of the masses, 124 occurred in premenopausal women, and 74 in postmenopausal women. This study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University(2022-R763), and written informed consent was obtained from all patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ultrasound examination\u003c/h2\u003e\u003cp\u003eUltrasonography was performed by a sonographer with \u0026ge;\u0026thinsp;7 years of experience in gynecological ultrasonography using a GE LOGIQ E10 ultrasound system. A comprehensive examination of the uterus, adnexa, and pelvis was conducted, and image acquisition included location, number, size, shape, echogenicity, borders, posterior echogenicity, blood flow, and concomitant ascites of the mass. Examinations were obtained primarily with a 5\u0026ndash;9 MHz intracavitary ultrasound probe. If the mass was too large to be visualized with transcavitary ultrasound, and if the patient is unmarried or cannot tolerate the transvaginal approach, complementary transabdominal scans were performed using a 1\u0026ndash;5 MHz curved probe.\u003c/p\u003e\u003cp\u003eCEUS was performed using the contrast agent SonoVue (Bracco, Milan, Italy). Each SonoVue contained 59 mg of phospholipid-coated sulfur hexafluoride (SF6) gas microbubbles, which were added to 5 mL of saline for injection and shaken vigorously to obtain a microbubble suspension with an average microbubble diameter of 2.5 \u0026micro;m. For bilateral lesions with solid components, each lesion was assessed and classified independently. All ultrasound images were stored in DICOM format on the ultrasound working system of the Second Hospital of Hebei Medical University for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Image analysis\u003c/h2\u003e\u003cp\u003eThe ultrasound characteristics of adnexal masses were evaluated according to the LR2 model and LR2 combined with CEUS. To minimize bias, all image analyses were performed in a blinded manner, with the sonographers unaware of the patients\u0026rsquo; histopathological findings. The LR2 model included: 1. Age (years); 2. Presence of peritoneal effusion; 3. Presence of a papillary protuberance with blood flow; 4. Maximum diameter of the solid portion (if the maximum diameter was \u0026ge;\u0026thinsp;50 mm, it was recorded as 50 mm because a diameter\u0026thinsp;\u0026gt;\u0026thinsp;50 mm does not increase the risk value with its growth); 5. Presence of irregularities in the inner wall of the cyst; and 6. Presence of acoustic shadows. The benign and malignant risk values (%) were calculated by applying the LR2 software to the above six parameters. The risk value ranges from 1% to 95%, taking\u0026thinsp;\u0026ge;\u0026thinsp;10% as the malignant cutoff value, i.e., \u0026lt; 10% is considered benign and \u0026ge;\u0026thinsp;10% is suspected malignant.\u003c/p\u003e\u003cp\u003eUltrasonography scoring criteria were used to assess the characteristics of peritoneal enhancement (0 points for intact, 1 point for inconspicuous or incomplete), the thickness of peritoneal enhancement (0 points for homogeneous, 1 point for uneven thickness), the characteristics of enhancement at peak enhancement compared with that of the peritoneum (0 points for no enhancement of the enhancement within the lesion, 1 point for low enhancement, and 2 points for equivocal or high enhancement), the internal area of lesion enhancement (homogeneous enhancement\u0026thinsp;=\u0026thinsp;0 points, non-homogeneous enhancement\u0026thinsp;=\u0026thinsp;1 point), and segregation within the lesion (no segregation-like enhancement\u0026thinsp;=\u0026thinsp;0 points, segregation-like enhancement\u0026thinsp;=\u0026thinsp;1 point), with a total score of 0\u0026ndash;6 points, and a total score of \u0026ge;\u0026thinsp;4 points was classified as malignant, and \u0026le;\u0026thinsp;3 points was classified as benign.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Histopathological examination\u003c/h2\u003e\u003cp\u003eAll cases were evaluated by a pathologist with 10 years' experience in ovarian disease, who was blinded to the ultrasound findings and clinical data. Histopathological diagnoses were used as the reference standard and were compared with the ultrasound-based assessments (LR2 and LR2 combined with CEUS).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eSPSS 20.0 statistical software were applied for statistical analysis. The chi-square test was used for categorical variable comparisons and the independent sample t-test or rank-sum test was used for continuous variable comparisons. Kappa coefficient was used to assess intergroup consistency, with κ\u0026thinsp;\u0026ge;\u0026thinsp;0.75 indicating high consistency, 0.40\u0026thinsp;\u0026le;\u0026thinsp;κ\u0026thinsp;\u0026lt;\u0026thinsp;0.75 indicating medium consistency, and κ\u0026thinsp;\u0026lt;\u0026thinsp;0.40 indicating low consistency. McNemar\u0026rsquo;s test was used to examine marginal asymmetry between paired classifications. The diagnostic performance of the ultrasound classification system was tested by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Patient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 171 patients were included in this study, involving 198 ultrasound-diagnosed ovarian masses. The patients' age ranged from 9 to 79 years old. The postoperative pathology showed that 61 cases were malignant (30.8%), 7 cases were borderline (3.5%), and 130 cases were benign (65.7%) (Table 1). \u0026nbsp;Malignant tumors included: serous cystadenocarcinoma (n = 40), mucinous cystadenocarcinoma (n = 8), granulosa cell tumor (n = 5), clear cell carcinoma (n = 3), endometrioid carcinoma (n = 3), sarcoma (n = 1), and dysgerminoma (n = 1). Borderline tumors comprised: borderline serous cystadenoma (n = 4), borderline mucinous cystadenoma (n = 2), and borderline seromucinous tumor (n = 2). Benign lesions included: mature teratoma (n = 37), paratubal (mesosalpinx) cyst (n = 21), serous cystadenoma (n = 13), hydrosalpinx (n = 13), fibrothecoma (n = 12), endometriotic cyst (endometrioma) (n = 11), hemorrhagic corpus luteum (n = 10), simple cyst (n = 5), mucinous cystadenoma (n = 4), and other benign entities (n = 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Overall diagnostic performance of IOTA LR2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the IOTA LR2 model classified 118 of 198 lesions (59.6%) as benign and 80 (40.4%) as non-benign (including borderline and malignant lesions). Compared with postoperative histopathology, LR2 achieved a sensitivity of 93.94%, specificity of 86.36%, positive predictive value (PPV) of 77.5%, negative predictive value (NPV) of 96.61%, and overall accuracy of 88.89%. The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 6.89 and 0.07. Agreement between LR2 and histopathology was substantial (Cohen’s κ = 0.763), indicating good concordance between the two methods. McNemar’s test showed a statistically significant marginal asymmetry (P \u0026lt; 0.01), suggesting LR2 tended to classify more lesions as non-benign compared with histopathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Diagnostic performance of IOTA LR2 combined with CEUS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen LR2 was combined with CEUS, 125 of 198 lesions (63.1%) were classified as benign and 73 (36.9%) as nonbenign (Table 2). The combined method demonstrated a sensitivity of 92.42%, specificity of 90.91%, PPV of 83.56%, NPV of 96 %, and overall accuracy of 91.41%. The PLR and NLR were 10.17 and 0.08. When LR2 was combined with CEUS, the diagnostic concordance with histopathology further improved (Cohen’s κ = 0.81), which exceeds the 0.75 threshold for high agreement. McNemar’s test yielded P = 0.143, indicating no significant marginal asymmetry between the combined method and histopathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Subgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn premenopausal women, 105 of 125 lesions (84.7%) were benign, and 19 \u0026nbsp;(15.3%) were non-benign on pathology. LR2 assessed 96 lesions (77.4%) as benign and 28 (22.6%) as nonbenign (Table 3). LR2 achieved \u0026nbsp;a sensitivity of 89.47%, specificity of 89.52%, PPV of 60.71%, NPV of 97.92%, PLR of 8.54, NLR of 0.12, and diagnostic accuracy of 89.52%. Concordance with pathological diagnosis was moderate (Cohen’s κ = 0.662). However, McNemar’s test indicated a significant marginal asymmetry (p = 0.022). When LR2 was combined with CEUS, 100 of 125 lesions (80.6%) were classified as benign and 24 (19.4%) as nonbenign (Table 3). The combined method demonstrated a sensitivity of 89.47%, specificity of 93.33%, PPV of 70.83%, NPV of 98%, and diagnostic accuracy of 92.74%. The PLR and NLR were 13.41 and 0.07. Concordance with pathological diagnosis was good (Cohen’s κ = 0.748), and McNemar’s test showed no significant marginal asymmetry (p = 0.18).\u003c/p\u003e\n\u003cp\u003eIn postmenopausal women, 24 of 74 lesions (36.5%) were benign, and 47 (63.5%) were non-benign on pathology (Table 4). LR2 assessed 22 lesions (29.7%) as benign and 52 (70.3%) as nonbenign. LR2 achieved \u0026nbsp;a sensitivity of 95.74%, specificity of 74.07%, PPV of 86.54%, NPV of 90.91%, and diagnostic accuracy of 87.84%. The PLR and NLR were 3.69% and 0.06%. Concordance with pathological diagnosis was moderate (Cohen’s κ = 0.662). McNemar’s test revealed a significant marginal asymmetry (P \u0026lt; 0.01). When LR2 was combined with CEUS, 25 of 125 lesion (33.8%) were classified as benign and 49 (66.2%) as nonbenign.\u0026nbsp;The combined method demonstrated a sensitivity of 93.62%, specificity of 81.48%, PPV of 89.8%, NPV of 88%, and diagnostic accuracy of 89.19%. The PLR and NLR were 5.06% and 0.23%. Concordance with the pathological diagnosis was good (Cohen’s κ = 0.763). McNemar’s test indicated no significant marginal asymmetry (P = 0.727).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 ROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC analysis demonstrated an AUC of 0.90 (95% CI: 0.85–0.94) for LR2 alone and an AUC of 0.92 (95% CI: 0.88–0.97) for LR2 combined with CEUS（(Table 2）。\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOvarian cancer remains the most lethal gynecologic malignancy, largely due to late-stage diagnosis and the lack of effective screening strategies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Accurate preoperative differentiation of adnexal masses is therefore critical for optimizing treatment strategies and improving patient outcomes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Conventional ultrasound is widely used as the first-line imaging tool, but its accuracy depends heavily on examiner experience and subjective interpretation. To improve diagnostic consistency, the IOTA group has developed standardized models, among which the LR2 model is one of the most validated tools for risk stratification [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. IOTA LR2 is a product of IOTA's prospective study based on a large sample, which focuses more on ultrasound characteristics, including six parameters such as the patient's age, the presence of peritoneal effusion, the presence of papillae with blood flow, the maximal diameter of the solid portion, the regularity of the cyst's inner wall, and the presence of an acoustic shadow, and therefore has a high degree of sensitivity to detect as many true positive patients as possible [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, we analyzed 171 patients with 198 adnexal masses and found that IOTA LR2 model achieved a sensitivity of 93.94%, a specificity of 86.36% and overall accuracy of 88.89%, findings that are comparable to those reported in previous multicenter investigations. Timmerman et al. first demonstrated that LR2 provides high sensitivity but moderate specificity in differentiating adnexal masses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A subsequent meta-analysis by Meys et al. also confirmed that while LR2 offers robust sensitivity, its specificity remains suboptimal, resulting in a proportion of false-positive cases[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results align with these observations, underscoring the limitations of relying on LR2 alone in clinical practice.\u003c/p\u003e\u003cp\u003eCEUS has been introduced as a complementary technique, as it provides real-time visualization of microvascular perfusion and reflects angiogenesis, a hallmark of malignancy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Several studies have shown that CEUS improves diagnostic accuracy compared with conventional doppler ultrasound[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings extend this knowledge by demonstrating that combining LR2 with CEUS significantly improves specificity (90.9% vs. 86.4%) and overall accuracy (91.4% vs. 88.9%), while maintaining high sensitivity. This combined approach yielded a positive likelihood ratio exceeding 10, meeting the threshold for an excellent diagnostic test, and clearly outperforming LR2 alone. This indicates that when used in combination with CEUS, LR2 can significantly enhance specificity and reduce the probability of misdiagnosis compared to using LR2 alone. In clinical practice, PPV and NPV are the diagnostic parameters most familiar to clinicians. In our study, LR2 combined with CEUS improved the PPV from 77.5% to 83.6% while maintaining a high NPV (\u0026gt;\u0026thinsp;96%), thereby reducing the risk of unnecessary surgery for benign lesions. Importantly, likelihood ratios, which are not influenced by disease prevalence, further confirmed the superiority of the combined method: the positive likelihood ratio of LR2\u0026thinsp;+\u0026thinsp;CEUS exceeded 10, meeting the criterion of an \u0026ldquo;ideal diagnostic test,\u0026rdquo; compared with 6.89 for LR2 alone. ROC analysis also supported this finding, with the AUC increasing from 0.90 to 0.92, indicating that both methods had high diagnostic efficacy, but LR2\u0026thinsp;+\u0026thinsp;CEUS provided a more balanced and clinically reliable diagnostic profile.\u003c/p\u003e\u003cp\u003eSubgroup analyses provided further insight into the robustness of this combined strategy. In both premenopausal and postmenopausal women, LR2\u0026thinsp;+\u0026thinsp;CEUS consistently demonstrated higher specificity and better concordance with histopathology compared with LR2 alone. Previous research has suggested that menopausal status may influence diagnostic performance in models such as O-RADS or GI-RADS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our findings indicate that LR2 combined with CEUS retains diagnostic superiority across different hormonal backgrounds, thus broadening its applicability in routine practice.\u003c/p\u003e\u003cp\u003eSeveral limitations of our study should be acknowledged. First, this was a single-center study with a relatively limited sample size, which may restrict the generalizability of the findings. Second, interobserver variability in CEUS interpretation was not formally assessed. Future multicenter prospective studies with larger populations and standardized CEUS assessment criteria are warranted to validate and extend our results.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results show that the IOTA LR2 model has high sensitivity but low specificity, and can help sonographers initially determine the benign or malignant nature of adnexal masses. The combination of LR2 with CEUS can significantly reduce the misdiagnosis rate of LR2 alone; therefore, the combination of LR2 with CEUS has a higher value for clinical application, and can help sonographers more accurately assess the nature of adnexal masses in their clinical work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all patients, investigators, and data managers for their valuable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCai Tian:\u003c/strong\u003e Conceptualization, Methodology, Project administration; \u003cstrong\u003eYing Hao:\u003c/strong\u003e Writing \u0026ndash; original draft, Data Curation, Formal analysis; \u003cstrong\u003eJingqiao Liu:\u003c/strong\u003e Investigation, Visualization; \u003cstrong\u003eYiwei Han:\u003c/strong\u003e Software, Visualization; \u003cstrong\u003eZijia Shi:\u003c/strong\u003e Resources, Project administration; \u003cstrong\u003eTianyi Guo:\u0026nbsp;\u003c/strong\u003eMethodology, Statistical analysis;\u003cstrong\u003e\u0026nbsp;Xiaonan Yan:\u0026nbsp;\u003c/strong\u003eSupervision, Methodology, Writing - Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the declaration of Helsinki. This study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University（2022-R763）. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR.L. Siegel, K.D. Miller, H.E. Fuchs, A. Jemal, Cancer statistics, 2022, CA: a cancer journal for clinicians, 72 (2022) 7-33.\u003c/li\u003e\n\u003cli\u003eH. Fei, S. Chen, C. Xu, Bioinformatics analysis of gene expression profile of serous ovarian carcinomas to screen key genes and pathways, Journal of ovarian research, 13 (2020) 82.\u003c/li\u003e\n\u003cli\u003eC. Giuseppe, W. S John, C.J.J. William, Ovarian Cancer: A Review, (2025).\u003c/li\u003e\n\u003cli\u003eE. Sarah, S. Kelly, Z. Qin, I. Alexia, S. Yukio, G. Ginger J, L.R. Kara, Z. William A 3rd, Y. Effi, O.C. Roisin E, Z. Oliver, C.J.G.O. Dennis S, Outcomes and long-term follow-up by treatment type for patients with advanced-stage ovarian cancer managed at a tertiary cancer center: A Memorial Sloan Kettering Cancer Center Team Ovary study, 169 (2022).\u003c/li\u003e\n\u003cli\u003eCommittee Opinion No. 716: The Role of the Obstetrician-Gynecologist in the Early Detection of Epithelial Ovarian Cancer in Women at Average Risk, Obstetrics and gynecology, 130 (2017) e146-e149.\u003c/li\u003e\n\u003cli\u003eJ. Ferlay, M. Colombet, I. Soerjomataram, D.M. Parkin, M. Pi\u0026ntilde;eros, A. Znaor, F. Bray, Cancer statistics for the year 2020: An overview, International journal of cancer, (2021).\u003c/li\u003e\n\u003cli\u003eT. Fan, Q. Zhao, J.J. Chen, W.T. Chen, M.L. Pearl, Clinical significance of circulating tumor cells detected by an invasion assay in peripheral blood of patients with ovarian cancer, Gynecologic oncology, 112 (2009) 185-191.\u003c/li\u003e\n\u003cli\u003eA. Deslandes, N. Parange, J.T. Childs, B. Osborne, C. Panuccio, A. Croft, E. Bezak, How long does a transvaginal ultrasound examination for endometriosis take in comparison to a routine transvaginal ultrasound examination?, Australasian journal of ultrasound in medicine, 25 (2022) 20-27.\u003c/li\u003e\n\u003cli\u003eL. Valentin, Use of morphology to characterize and manage common adnexal masses, Best practice \u0026amp; research. Clinical obstetrics \u0026amp; gynaecology, 18 (2004) 71-89.\u003c/li\u003e\n\u003cli\u003eC. Van Holsbeke, A. Daemen, J. Yazbek, T.K. Holland, T. Bourne, T. Mesens, L. Lannoo, A.S. Boes, A. Joos, A. Van De Vijver, N. Roggen, B. de Moor, E. de Jonge, A.C. Testa, L. Valentin, D. Jurkovic, D. Timmerman, Ultrasound experience substantially impacts on diagnostic performance and confidence when adnexal masses are classified using pattern recognition, Gynecologic and obstetric investigation, 69 (2010) 160-168.\u003c/li\u003e\n\u003cli\u003eJ. Koneczny, A. Czekierdowski, M. Florczak, P. Poziemski, N. Stachowicz, D. Borowski, The use of sonographic subjective tumor assessment, IOTA logistic regression model 1, IOTA Simple Rules and GI-RADS system in the preoperative prediction of malignancy in women with adnexal masses, Ginekologia Polska, 88 (2017) 647-653.\u003c/li\u003e\n\u003cli\u003eP. Buranaworathitikul, V. Wisanumahimachai, N. Phoblap, Y. Porngasemsart, W. Rugfoong, N. Yotchana, P. Uthaichalanont, T. Jiampochaman, C. Kunanukulwatana, A. Thiamkaew, S. Luewan, C. Tantipalakorn, T.J.C. Tongsong, Accuracy of O-RADS System in Differentiating Between Benign and Malignant Adnexal Masses Assessed via External Validation by Inexperienced Gynecologists, 16 (2024).\u003c/li\u003e\n\u003cli\u003eD. Timmerman, A.C. Testa, T. Bourne, E. Ferrazzi, L. Ameye, M.L. Konstantinovic, B. Van Calster, W.P. Collins, I. Vergote, S. Van Huffel, L. Valentin, Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group, Journal of clinical oncology, 23 (2005) 8794-8801.\u003c/li\u003e\n\u003cli\u003eE.M. Meys, J. Kaijser, R.F. Kruitwagen, B.F. Slangen, B. Van Calster, B. Aertgeerts, J.Y. Verbakel, D. Timmerman, T. Van Gorp, Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis, European journal of cancer (Oxford, England : 1990), 58 (2016) 17-29.\u003c/li\u003e\n\u003cli\u003eJ. Wang, F. Lv, X. Fei, Q. Cui, L. Wang, X. Gao, Z. Yuan, Q. Lin, Y. Lv, A. Liu, Study on the characteristics of contrast-enhanced ultrasound and its utility in assessing the microvessel density in ovarian tumors or tumor-like lesions, International journal of biological sciences, 7 (2011) 600-606.\u003c/li\u003e\n\u003cli\u003eW. Shentu, Y. Zhang, J. Gu, F. Wang, W. Zhao, C. Liu, Z. Lin, Y. Wang, C. Liu, Y. Chen, Q. Fan, H. Wang, Contrast-enhanced ultrasonography for differential diagnosis of adnexal masses, Frontiers in oncology, 12 (2022) 968759.\u003c/li\u003e\n\u003cli\u003eC. Stewart, C. Ralyea, S. Lockwood, Ovarian Cancer: An Integrated Review, Seminars in oncology nursing, 35 (2019) 151-156.\u003c/li\u003e\n\u003cli\u003eS. Granberg, M. Wikland, I. Jansson, Macroscopic characterization of ovarian tumors and the relation to the histological diagnosis: criteria to be used for ultrasound evaluation, Gynecologic oncology, 35 (1989) 139-144.\u003c/li\u003e\n\u003cli\u003eM.J. Engelen, A.G. van der Zee, E.G. de Vries, P.H. Willemse, Debulking surgery for ovarian epithelial cancer performed by a gynaecological oncologist improved survival compared with less specialised surgeons, Cancer treatment reviews, 32 (2006) 320-323.\u003c/li\u003e\n\u003cli\u003eM.A.A. Basha, M.I. Metwally, S.A. Gamil, H.M. Khater, S.A. Aly, A.A. El Sammak, M.M.A. Zaitoun, E.M. Khattab, T.M. Azmy, N.A. Alayouty, N. Mohey, H.N. Almassry, H.Y. Yousef, S.A. Ibrahim, E.A. Mohamed, A.E.M. Mohamed, A.H.M. Afifi, O.A. Harb, H.Y. Algazzar, Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses, European radiology, 31 (2021) 674-684.\u003c/li\u003e\n\u003cli\u003eD. Timmerman, T.H. Bourne, A. Tailor, W.P. Collins, H. Verrelst, K. Vandenberghe, I. Vergote, A comparison of methods for preoperative discrimination between malignant and benign adnexal masses: the development of a new logistic regression model, American journal of obstetrics and gynecology, 181 (1999) 57-65.\u003c/li\u003e\n\u003cli\u003eT. Dirk, T. Antonia C, B. Tom, F. Enrico, A. Lieveke, K. Maja L, V.C. Ben, C. William P, V. Ignace, V.H. Sabine, V.J.J.C.O. Lil, Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group, 23 (2005).\u003c/li\u003e\n\u003cli\u003eM. E M J, K. J, K. R F P M, S. B F M, V.C. B, A. B, V. J Y, T. D, V.G.J.E.J.C. T, Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis, 58 (2016).\u003c/li\u003e\n\u003cli\u003eL.M. Sconfienza, N. Perrone, A. Delnevo, F. Lacelli, C. Murolo, N. Gandolfo, G. Serafini, Diagnostic value of contrast-enhanced ultrasonography in the characterization of ovarian tumors(), Journal of ultrasound, 13 (2010) 9-15.\u003c/li\u003e\n\u003cli\u003eJ.E. Palmer, L.J. Sant Cassia, C.J. Irwin, A.G. Morris, T.P. Rollason, Prognostic value of measurements of angiogenesis in serous carcinoma of the ovary, International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists, 26 (2007) 395-403.\u003c/li\u003e\n\u003cli\u003eT. Cai, H. Yi-Wei, S. Zi-Jia, L. Ya-Wei, X. Lei, L. Xiao-Li, L.J.I.J.G.C. Jing-Qiao, Diagnostic value of the International Ovarian Tumor Analysis simple rules combined with contrast-enhanced ultrasound for adnexal masses, 35 (2025).\u003c/li\u003e\n\u003cli\u003eK. Shoko, U. Tomokazu, K. Yuko, K. Naohisa, S. Shiro, M. Mika, K. Hiroaki, S. Kiyosumi, K.J.J.M.U. Fumitaka, Contrast-enhanced ultrasonography using Sonazoid(\u0026reg;) is useful for diagnosis of malignant ovarian tumors: comparison with Doppler ultrasound, 40 (2013).\u003c/li\u003e\n\u003cli\u003eB. Mohammad Abd Alkhalik, M. Maha Ibrahime, G. Shrif A, K. Hamada M, A. Sameh Abdelaziz, E.S. Ahmed A, Z. Mohamed M A, K. Enass M, A. Taghreed M, A. Nader Ali, M. Nesreen, A. Hosam Nabil, Y. Hala Y, I. Safaa A, M. Ekramy A, M. Abd El Motaleb, A. Amira Hamed Mohamed, H. Ola A, A.J.E.R. Hesham Youssef, Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses, 31 (2020).\u003c/li\u003e\n\u003cli\u003eG. Sugandha, K. Amarjit, M. Jaswinder Kaur, S. Preet Kanwal, K.J.J.C.D.R. Navkiran, Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours, 11 (2017).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1：Pathological distribution of the resected adnexal masses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"529\" height=\"136\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 2：Overall diagnostic performance \u0026mdash; LR2 vs. LR2 + CEUS\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"529\" height=\"321\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Diagnostic performance in premenopausal subgroup LR2 vs. LR2 + CEUS\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"535\" height=\"239\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 4. Diagnostic performance in postmenopausal subgroup \u0026mdash; LR2 vs. LR2 + CEUS\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IOTA LR2, CEUS, adnexal mass, ultrasound diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7698811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7698811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the diagnostic performance of the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2) alone and in combination with contrast-enhanced ultrasound (CEUS) for preoperative differentiation of adnexal masses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 198 surgically resected adnexal masses from 171 patients were assessed by LR2 and LR2 combined with CEUS, with histopathology as the reference standard. Diagnostic accuracy was compared using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e LR2 achieved an AUC of 0.90 (95% CI: 0.85–0.94), with 93.9% sensitivity and 86.4% specificity. The addition of CEUS improved specificity to 90.9% and accuracy to 91.4% (AUC = 0.92, 95% CI: 0.88–0.97). Subgroup analyses confirmed the superiority of LR2 + CEUS over LR2 alone in both pre- and postmenopausal women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e While LR2 offers high sensitivity, its specificity is limited. Combining LR2 with CEUS significantly reduces misdiagnosis, providing greater clinical utility for preoperative assessment of adnexal tumors.\u003c/p\u003e","manuscriptTitle":"Improved Clinical Value of Combining IOTA LR2 with CEUS for Preoperative Assessment of Adnexal Masses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 15:35:19","doi":"10.21203/rs.3.rs-7698811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-24T18:06:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36354122790348718751055887282873050703","date":"2025-12-15T13:17:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T10:44:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T10:08:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4669319825694866585478163277592000511","date":"2025-12-09T09:32:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258966119426560116003976110037422615067","date":"2025-12-05T13:03:00+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-12T08:57:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T07:14:06+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T17:06:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T02:21:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-09-30T02:14:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5ca07d3-efc7-4dac-96a6-df4484493753","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T15:35:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 15:35:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7698811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7698811","identity":"rs-7698811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}