MRI-based predictive model for differentiating epithelial ovarian tumors

MRI-based predictive model for differentiating epithelial ovarian tumors | 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 MRI-based predictive model for differentiating epithelial ovarian tumors Linghong Qi, Yinjian Zhou, Jiami Liu, Pengfei Wang, Zhi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9443484/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective To develop and validate a combined predictive model based on MRI features for differentiating benign from malignant epithelial ovarian tumors. Materials and methods Patients were classified as having benign or malignant tumors according to postoperative histopathology. MRI features were evaluated, and independent predictors were identified using univariate and multivariate analyses. A combined predictive model was constructed based on regression coefficients and externally validated. Results The benign group comprised 121 patients (133 lesions), and the malignant group comprised 36 patients (44 lesions). Tumor morphology, diffusion-weighted imaging (DWI) signal intensity, and the presence of ascites were independent predictors of malignancy, with AUCs of 0.765, 0.902, and 0.754, respectively. The combined model (1 × irregular morphology + 1.010 × high DWI signal + 1.029 × ascites) achieved an AUC of 0.933, with a sensitivity of 81.8% and specificity of 94.0%. The Hosmer–Lemeshow test indicated good calibration (P = 0.710). In the external validation cohort (41 benign patients with 44 lesions; 9 malignant patients with 12 lesions), the model yielded an AUC of 0.896, with a sensitivity of 77.3% and specificity of 100.0%. Conclusion The MRI-based combined model demonstrates high diagnostic performance and good stability, providing a practical and reliable tool for preoperative differentiation of epithelial ovarian tumors. Ovarian tumors Magnetic resonance imaging Prediction model LASSO regression Diffusion weighted imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ovarian cancer is a common malignancy in gynecology, with epithelial ovarian cancer accounting for 90% of cases. Each year, over 300,000 women are diagnosed with ovarian cancer worldwide, and approximately 152,000 die from the disease[ 1 ], representing a severe threat to women’s health and ranking as the leading cause of death among malignancies of the female reproductive system[ 2 , 3 ]. Due to the concealed anatomical location of the ovaries, most patients with ovarian cancer do not present with obvious clinical symptoms at an early stage. Consequently, approximately 70% of patients are diagnosed at an advanced stage, with a 5-year survival rate of only 20%–36%. In contrast, when ovarian cancer is detected at an early stage, the 5-year survival rate can reach up to 90%. Therefore, early detection and timely intervention are critical for improving patient prognosis[ 4 ]. However, effective screening and early diagnostic strategies remain limited, and the rate of preoperative misdiagnosis of ovarian cancer is still relatively high, particularly in primary healthcare settings.Magnetic resonance imaging (MRI), owing to its superior soft-tissue contrast, enables clear visualization of the anatomical relationships among pelvic organs and is widely regarded as the noninvasive gold standard for differentiating benign from malignant ovarian tumors[ 5 , 6 ]. Although integrated predictive models have been extensively applied across various medical fields[ 7 – 9 ], their application in preoperative MRI-based diagnosis of ovarian cancer remains relatively limited.This study analyzes clinical data and MRI characteristics of ovarian epithelial tumors to establish a combined predictive model for determining the benign or malignant nature of ovarian epithelial tumors before surgery, thereby enhancing diagnostic accuracy and guiding the selection of appropriate treatment strategies. Materials and Methods Participants Clinical data of patients who underwent surgical treatment for ovarian epithelial tumors in the Department of Gynecology between October 2021 and October 2024 were retrospectively collected. Relevant clinical information was extracted from the electronic medical record system, and MRI images were retrieved from the hospital Picture Archiving and Communication System (PACS). Inclusion criteria and exclusion criteria as shown in Fig. 1 . Patients with benign epithelial tumors confirmed by postoperative pathology were classified into the benign group, while those with malignant epithelial tumors were classified into the malignant group. This study is a retrospective study approved by the Ethics Committee of ** Maternity & Child Health Care Hospital (Approval No. : 2023-R-025), and the requirement for informed consent was waived. Imaging Methods A Siemens Avanto 1.5T MRI scanner was used, equipped with an abdominal coil. The standard slice thickness was 5 mm, with an interslice gap of 1.5 mm. The field of view (FOV) was 380 mm × 380 mm, with two excitations. Axial TSE-T1WI: TR 570 ms, TE 12 ms, matrix 256 × 256. Axial TSE-T2WI: TR 4000 ms, TE 79 ms, matrix 256 × 256. Sagittal TSE-T1WI: TR 618 ms, TE 10 ms, matrix 179 × 256. Sagittal TSE-T2WI: TR 4000 ms, TE 79 ms, matrix 179 × 256. Coronal TSE-T2WI: TR 3800 ms, TE 101 ms, matrix 224 × 320. Diffusion-weighted imaging (DWI) was performed using a single-shot spin-echo echo-planar imaging (EPI) sequence for axial scanning, with TR 3500 ms, TE 79 ms, and a matrix of 256 × 256. The contrast agent used was Gadovist, at a dose of 0.1 mmol/kg body weight, administered for both axial and sagittal T1WI imaging, with the same parameters as the pre-contrast scans. Image Analysis All images were reviewed by two experienced radiologists(J.L., 9 years and Z. L., 11 years), using a double-blind method. In cases of disagreement, consensus was reached through joint discussion. The following characteristics of the lesions were specifically observed: location, maximum lesion diameter, morphology, margin, tumor nature (cystic/cystic-solid/solid), presence of wall nodules, septal thickness, T1WI signal, T2WI signal, DWI signal (b = 1000), arterial phase enhancement rate, venous phase enhancement rate, maximum diameter of pelvic lymph nodes, and presence of ascites. Cystic lesions were defined as those in which the solid component occupies less than one-third of the lesion, cystic-solid lesions as those with a solid component occupying between one-third and two-thirds of the lesion, and solid lesions as those where the solid component occupies more than two-thirds of the lesion[ 10 ]. The T1WI and T2WI signals were compared with those of the iliopsoas muscle. Septal thickness was measured at its thickest part on the T2WI images. Ascites was diagnosed only after excluding other clinical conditions such as cirrhosis, pelvic inflammation, and pelvic tuberculosis that may cause abdominal effusion. Measurement of Enhancement Rate A circular region of interest (ROI), measuring 20–40 mm², was placed at the solid component of the lesion. The MR values for the pre-contrast scan, arterial phase, and venous phase were measured at the same slice level, with each MR value measured three times. The average value of each measurement was recorded as the final result. The MR arterial phase enhancement rate was calculated as: (arterial phase MR value - pre-contrast MR value) / pre-contrast MR value. The MR venous phase enhancement rate was calculated as: (venous phase MR value - pre-contrast MR value) / pre-contrast MR value. Construction of the predictive model Univariate analysis was performed for all candidate predictive variables. Variables that showed statistical significance in the univariate analysis ( P < 0.05) were subsequently entered into a multivariate logistic regression model to identify independent risk factors for ovarian cancer. The regression coefficients (β values) of the identified independent risk factors were then reassigned to construct the predictive model. External validation The predictive model was applied to a validation cohort comprising patients with pathologically confirmed ovarian epithelial tumors who underwent surgery between November 2024 and October 2025. The inclusion and exclusion criteria for the validation cohort were identical to those used for the modeling cohort. Statistical Methods Statistical analysis was performed using SPSS version 22.0 (Chinese edition) and Medcalc. Normally distributed continuous data are expressed as the mean ± standard deviation (± s), with comparisons made using the independent samples t-test. For continuous data not following a normal distribution, the median (25th–75th percentile) [M(P25–P75)] is presented, and inter-group comparisons are made using non-parametric tests. Categorical data are presented as frequencies or percentages, with comparisons made using the χ² test. Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC), and the model's goodness-of-fit was assessed using the Hosmer-Lemeshow test. The cutoff point was also determined. A P -value of < 0.05 was considered statistically significant. Results Clinical Characteristics and Multiparameter Body Composition The patient grouping is shown in Fig. 1 . A comparison of the clinical data and MRI characteristics of ovarian tumors between the two groups revealed statistically significant differences ( P < 0.05) in tumor margin regularity, tumor nature, presence of papillary wall nodules, T2WI signal, DWI signal, arterial phase enhancement rate, venous phase enhancement rate, short axis of lymph nodes, and the presence of ascites, as shown in Table 1 . Table 1 A comparison of clinical data and ovarian MRI features between the two patient groups Clinical Characteristics and MRI Features Benign(n = 133) Malignant(n = 44) Statistical Value P value Age 48.8 ± 18.1 52.1 ± 10.2 1.142 0.255 Postmenopausal 54(44.6%) 17(47.2%) 0.075 0.784 Premenopausal 67(55.4%) 19(52.8%) Unilateral 109(90.1%) 28(77.8%) 3.779 0.052 Bilateral 12(9.9%) 8(22.2%) Lesion Length( cm ) 6.5(4.2 ~ 10.8) 6.6(4.5 ~ 10.9) 0.087 0.931 Regular Morphology 128(96.2%) 19(43.2%) 66.123 <0.001 Irregular Morphology 5(3.8%) 25(56.8%) Cystic Lesion 114(85.7%) 5(11.4%) 83.673 <0.001 Cystic-solid Lesion 15(11.3%) 27(61.4%) Solid Lesion 4(3.0%) 12(27.3%) Papillary Wall Nodule Present 39(29.3%) 20(45.5%) 3.871 0.049 No Papillary Wall Nodule 94(70.7%) 24(54.5%) Septal Thickness( mm ) 2.5 ± 0.9 2.3 ± 0.9 0.606 0.545 Solid T1 Hypointensity 98(73.7%) 26(59.1%) 4.288 0.117 Isosignal on T1 32(24.1%) 15(34.1%) Hyperintensity on T1 3(2.2%) 3(6.8%) Solid T2 Hypointensity 101(75.9%) 6(13.6%) 68.066 <0.001 Isosignal on T2 18(13.5%) 7(15.9%) Hyperintensity on T2 14(10.5%) 31(70.5%) DWI Isosignal 116(87.2%) 3(6.8%) 97.009 <0.001 DWI Hyperintensity 17(12.8%) 41(93.2%) Arterial Phase Enhancement Rate 0.6 ± 0.4 1.1 ± 0.3 8.688 <0.001 Venous Phase Enhancement Rate 0.7(0.4 ~ 1.0) 1.2(1.0 ~ 1.4) 5.744 <0.001 Long Axis of Lymph Node(mm) 0.47 ± 0.1 0.52 ± 0.1 2.445 0.015 Ascites Present 20(15.0%) 29(65.9%) 42.739 <0.001 No Ascites 113(85.0%) 15(34.1%) Multivariate Logistic Regression Analysis of MRI Characteristics of Ovarian Epithelial Tumors Variables with P < 0.05 in the univariate analysis (as shown in the table above) were included in a logistic regression model. Ovarian epithelial tumor malignancy was set as the dependent variable, with tumor morphology, tumor nature, presence of papillary wall nodules, T2WI signal, DWI signal, arterial phase enhancement rate, venous phase enhancement rate, short axis of lymph nodes, and presence of ascites as independent variables. The multivariate analysis revealed that tumor morphology, DWI signal, and the presence of ascites were independent risk factors for the malignancy of ovarian epithelial tumors, as shown in Table 2 . Table 2 Results of the multivariate logistic regression analysis of MRI features in ovarian epithelial tumors Variable B Value Standard Deviation Wald X² Value OR (95% CI) P Value Irregular Shape 2.207 1.037 4.529 9.093(1.191 ~ 69.441) 0.033 DWI High Signal 2.230 0.988 5.099 9.302(1.342 ~ 64.454) 0.024 Ascites 2.272 0.863 6.937 9.701(1.788 ~ 52.621) 0.008 Development and Validation of the combined predictive model The AUC for tumor morphology, DWI signal, and the presence of ascites were 0.765, 0.902, and 0.754, respectively, as shown in Fig. 2 . A combined predictive model was established based on the regression coefficients of each independent risk factor: combined predictive model = 1 × irregular morphology + 1.010 × high DWI signal + 1.029 × ascites. The results showed that the AUC of the combined predictive model was 0.933, as shown in Fig. 3 . The cutoff value was 1.5195, with a sensitivity of 81.8%, specificity of 94.0%, and a Youden's index of 0.758. The Hosmer-Lemeshow test indicated good calibration of the combined predictive model (χ² = 5.436, df = 8, P = 0.710), demonstrating good agreement between the predicted and actual risk.The AUC of the combined predictive model was significantly higher than that of DWI signal and the presence of ascites ( P < 0.05), as shown in Table 3 . Table 3 Comparison of AUCs between the combined predictive model and individual independent risk factors Group comparison Z Value P Value Combined predictive modelཞtumor morphology 4.728 <0.001 Combined predictive modelཞDWI signal 1.673 0.094 Combined predictive modelཞpresence of ascites 5.683 <0.001 External validation of the combined predictive model The external validation cohort consisted of patients with ovarian epithelial tumors confirmed by postoperative histopathology between November 2024 and October 2025, the benign group comprised 41 patients with a total of 44 lesions, while the malignant group included 9 patients with a total of 12 lesions. The combined predictive model achieved an AUC of 0.896 (Fig. 4 ), with a sensitivity of 77.3%, a specificity of 100.0%, and a Youden index of 0.773. Discussion Preoperative imaging plays a crucial role in determining the origin of ovarian tumors and differentiating between benign and malignant lesions. Ultrasound, CT, and MRI are commonly used for preoperative evaluation, with ultrasound being the preferred method for suspected pelvic masses. However, MRI offers superior advantages, including high soft tissue resolution and the absence of ionizing radiation, making it more favorable than CT for evaluating ovarian tumors[ 11 – 14 ]. Due to its non-invasive nature and multi-planar imaging capabilities, MRI has become an indispensable tool in the assessment of pelvic lesions[ 15 ]. Previous studies have primarily focused on diagnosing ovarian tumors based on MRI imaging features[ 16 – 18 ]. This diagnostic approach relies heavily on long-term clinical experience and is feasible for senior radiologists; however, limited experience among junior radiologists may lead to reduced accuracy in preoperative diagnosis[ 19 ]. This study aimed to perform univariate and multivariate analyses of MRI imaging features in benign and malignant ovarian epithelial tumors to identify independent risk factors for malignancy based on MRI findings. The regression coefficients of each independent risk factor were quantified numerically to establish a combined predictive model, which was then used to diagnose ovarian epithelial tumor malignancy based on a determined cutoff value. The multivariate analysis revealed that tumor morphology, DWI signal, and the presence of ascites were independent risk factors for malignancy in ovarian epithelial tumors. Umor morphology observed through MRI is often indicative of the lesion’s nature and can significantly guide subsequent diagnosis and therapeutic intervention[ 20 ]. Benign ovarian epithelial tumors typically demonstrate expansive growth with a complete capsule, displacing surrounding tissues without infiltration, which results in regular morphology. However, inflammatory infections or the repair process following torsion or rupture may cause the lesion to present with irregular morphology. In contrast, malignant ovarian epithelial tumors usually exhibit infiltrative growth, leading to the destruction of normal tissue structures and the invasion or destruction of surrounding tissues, which results in an irregular morphology. In this study’s univariate analysis, the proportion of lesions with irregular morphology in the malignant group was 56.8%(25/44), significantly higher than the 3.8%༈5/133༉ observed in the benign group ( P < 0.05). The multivariate analysis identified irregular lesion morphology as an independent risk factor for ovarian cancer, with an AUC of 0.765. DWI is a non-invasive imaging technique that provides functional information and overcomes the limitations of traditional MRI[ 21 ]. DWI has become an essential part of gynecological MRI examinations, significantly enhancing the accuracy of imaging reports for gynecological malignant tumors[ 22 ]. Several studies have found that the solid component of ovarian cancer, due to rapid cell growth, increased cell density, and a higher nucleus-to-cytoplasm ratio, restricts the free movement of water molecules, resulting in high signal intensity on DWI[ 23 , 24 ]. In contrast, benign ovarian tumors typically have solid components characterized by fibrous capsules, septa, and wall nodules, which do not restrict water molecule diffusion, resulting in isointensity or low signal on DWI[ 25 ]. In this study, the incidence of high signal intensity on DWI in the malignant group was 93.2% (41/44), which was significantly higher than the 12.8% (17/133) observed in the benign group (P < 0.05). The multivariate analysis found that high signal intensity on DWI is an independent risk factor for ovarian cancer, with an AUC of 0.902. Ascites refers to the accumulation of fluid in the abdominal cavity due to an increase in fluid production or a decrease in absorption under pathological conditions. The primary causes include increased microvascular permeability and lymphatic obstruction[ 26 ]. Ascites plays a significant role in the progression of ovarian malignancies and is often considered an indicator of late-stage ovarian cancer with poor prognosis[ 27 – 29 ]. Previous studies have reported that more than one-third of ovarian cancer patients develop ascites[ 30 ]. In this study, the incidence of ascites in the malignant group was 65.9%(29/44), which is higher than reported in the literature. This discrepancy may be due to varying thresholds for the volume of ascites included in previous reports. For this study, any amount of ascites was considered positive after excluding conditions such as cirrhosis, pelvic inflammatory disease, and pelvic tuberculosis, which can also cause abdominal effusion. This definition is consistent with the one used by Ozyilmaz et al[ 31 ]. The multivariate analysis identified ascites as an independent risk factor for ovarian cancer, with an AUC of 0.754. While individual MRI characteristics of ovarian epithelial tumors provide valuable information, they also have limitations and cannot cover all diagnostic aspects comprehensively. Therefore, a more integrated and effective method is required to improve diagnostic accuracy. This study identified three independent risk factors for ovarian epithelial tumors—tumor morphology, DWI signal, and the presence of ascites—with AUCs of 0.765, 0.902, and 0.754, respectively. The AUC of the combined predictive model increased to 0.933, with a sensitivity of 81.8%, a specificity of 94.0%, and a Youden index of 0.758. The Hosmer–Lemeshow test indicated good calibration and satisfactory model stability. External validation further demonstrated that the combined predictive model achieved an AUC of 0.896, with a sensitivity of 77.3%, a specificity of 100.0%, and a Youden index of 0.773, indicating favorable predictive performance.. In recent years, numerous studies and reviews have reported the application of radiomics for predicting the benign or malignant nature of ovarian tumors[ 32 – 36 ]. However, radiomics has not yet been widely adopted in routine clinical practice, particularly in primary hospitals where the required technical infrastructure and expertise are often lacking. In the present study, independent risk factors were quantitatively integrated using a simplified mathematical formula, thereby transforming a complex diagnostic process into an easily applicable clinical tool. This approach is feasible for implementation across hospitals at different levels and may assist junior radiologists in improving the accuracy of preoperative diagnostic assessment. This study was conducted at a single center, which may have introduced selection bias and limited the generalizability of the findings. In addition, the relatively small sample size and the imbalance between the two groups may increase the risk of model overfitting. Future studies should include larger, multicenter cohorts, incorporate additional clinical and laboratory variables, and perform external validation to develop a more robust and reliable predictive model for clinical decision-making. Conclusions This study developed a combined predictive model for epithelial ovarian tumors based on clinical data and MRI imaging features. The model demonstrated favorable predictive performance and good stability. Owing to its simplicity and ease of calculation, this combined predictive model may serve as a practical and reliable tool to assist preoperative diagnosis in clinical practice. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing interests The authors declare no competing interests. Author Contribution L.Q. and Y.Z. contributed equally to this work. L.Q. and Y.Z. were responsible for data curation, data analysis and manuscript drafting of the manuscript. J.L. performed medical image processing and radiomic feature extraction. P.W. participated in the critical review, editing, and intellectual refinement of the manuscript. Z.L. contributed to statistical analysis, experimental design, conceptualized and supervised the project, provided funding acquisition, and approved the final manuscript. All authors read and approved the submitted version. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74:229–263. https://doi.org/10.3322/caac.21834 Lheureux S, Gourley C, Vergote I, Oza AM (2019) Epithelial ovarian cancer. 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Abdom Radiol (NY). 46:2308–2322. https://doi.org/10.1007/s00261-020-02820-z Zeng S, Wang XL, Yang H (2024) Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer. Mil Med Res. 11:77. https://doi.org/10.1186/s40779-024-00580-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 16 Apr, 2026 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9443484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633578065,"identity":"a87f7018-8675-4d53-a1eb-a7eb7aa6ea2c","order_by":0,"name":"Linghong Qi","email":"","orcid":"","institution":"Huzhou Women and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linghong","middleName":"","lastName":"Qi","suffix":""},{"id":633578066,"identity":"1275facb-effe-4669-ae36-2c6137d13521","order_by":1,"name":"Yinjian Zhou","email":"","orcid":"","institution":"Huzhou Women and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yinjian","middleName":"","lastName":"Zhou","suffix":""},{"id":633578067,"identity":"6f5ef94d-5b3a-437c-9a20-f334f6405793","order_by":2,"name":"Jiami Liu","email":"","orcid":"","institution":"Huzhou Women and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiami","middleName":"","lastName":"Liu","suffix":""},{"id":633578068,"identity":"486fd1d2-4313-4ff4-9902-420f6300d2c6","order_by":3,"name":"Pengfei Wang","email":"","orcid":"","institution":"Huzhou Women and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Wang","suffix":""},{"id":633578069,"identity":"01230f56-9867-4127-95ec-9eedb99ebfc3","order_by":4,"name":"Zhi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RMQrCMBTG8YRCujycHwjtFZ44q1dpEOoioqM4WCnEpeBaUPAqlQwuegIddHHWraCDQVwEaevmkN/0Df2TlDBmWX9IuLPZiZFZyN+jTA20pncSU6XEwzDE10ImsNrFWJ/qMGx7/jJW43x49Jm7z0qSHTVS6jb5aqMOQJdGBIOgOOEJBVdyZIxSHRhpHiEU/5FwgLKAplKZZJST7pQnQsjoSlomJmFgRnkCjuYpbZuIMq6bpKugX5z46/P8Do+J56e98y1/6NbC3VV5nY9zf/zesizL+uIJYQo/npYlr4MAAAAASUVORK5CYII=","orcid":"","institution":"Huzhou Women and Children's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-04-17 03:24:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9443484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9443484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108412335,"identity":"51188e75-bf59-4ddf-b88e-c65499402ffc","added_by":"auto","created_at":"2026-05-04 10:25:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":351081,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9443484/v1/73fff292819f314d3d4ab6d5.png"},{"id":108412341,"identity":"deb85dd6-f6c6-41d4-920a-7081eb516542","added_by":"auto","created_at":"2026-05-04 10:25:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148683,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC of tumor morphology, DWI signal, and the presence of ascites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9443484/v1/9d47324488552b1d8df0338c.png"},{"id":108412333,"identity":"2dee7756-a516-4a1b-a3b1-891df63523b3","added_by":"auto","created_at":"2026-05-04 10:25:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135598,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC of the combined predictive model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9443484/v1/55563b2a7e6b81fe2d0fa6b5.png"},{"id":108412414,"identity":"027f9323-1cc6-484f-99f4-0955a7f61841","added_by":"auto","created_at":"2026-05-04 10:25:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136216,"visible":true,"origin":"","legend":"\u003cp\u003eExternal validation AUC of combined predictive model\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9443484/v1/4a55e94279bec6c6e92a443a.png"},{"id":108493475,"identity":"20a01788-d605-4261-a7ff-9f4e9c8e15e5","added_by":"auto","created_at":"2026-05-05 10:00:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1025393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9443484/v1/56c36055-433f-4382-81a7-993b3bcf7ffa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRI-based predictive model for differentiating epithelial ovarian tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer is a common malignancy in gynecology, with epithelial ovarian cancer accounting for 90% of cases. Each year, over 300,000 women are diagnosed with ovarian cancer worldwide, and approximately 152,000 die from the disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], representing a severe threat to women\u0026rsquo;s health and ranking as the leading cause of death among malignancies of the female reproductive system[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the concealed anatomical location of the ovaries, most patients with ovarian cancer do not present with obvious clinical symptoms at an early stage. Consequently, approximately 70% of patients are diagnosed at an advanced stage, with a 5-year survival rate of only 20%\u0026ndash;36%. In contrast, when ovarian cancer is detected at an early stage, the 5-year survival rate can reach up to 90%. Therefore, early detection and timely intervention are critical for improving patient prognosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, effective screening and early diagnostic strategies remain limited, and the rate of preoperative misdiagnosis of ovarian cancer is still relatively high, particularly in primary healthcare settings.Magnetic resonance imaging (MRI), owing to its superior soft-tissue contrast, enables clear visualization of the anatomical relationships among pelvic organs and is widely regarded as the noninvasive gold standard for differentiating benign from malignant ovarian tumors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although integrated predictive models have been extensively applied across various medical fields[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], their application in preoperative MRI-based diagnosis of ovarian cancer remains relatively limited.This study analyzes clinical data and MRI characteristics of ovarian epithelial tumors to establish a combined predictive model for determining the benign or malignant nature of ovarian epithelial tumors before surgery, thereby enhancing diagnostic accuracy and guiding the selection of appropriate treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eClinical data of patients who underwent surgical treatment for ovarian epithelial tumors in the Department of Gynecology between October 2021 and October 2024 were retrospectively collected. Relevant clinical information was extracted from the electronic medical record system, and MRI images were retrieved from the hospital Picture Archiving and Communication System (PACS). Inclusion criteria and exclusion criteria as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with benign epithelial tumors confirmed by postoperative pathology were classified into the benign group, while those with malignant epithelial tumors were classified into the malignant group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study is a retrospective study approved by the Ethics Committee of ** Maternity \u0026amp; Child Health Care Hospital (Approval No. : 2023-R-025), and the requirement for informed consent was waived.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging Methods\u003c/h3\u003e\n\u003cp\u003eA Siemens Avanto 1.5T MRI scanner was used, equipped with an abdominal coil. The standard slice thickness was 5 mm, with an interslice gap of 1.5 mm. The field of view (FOV) was 380 mm \u0026times; 380 mm, with two excitations. Axial TSE-T1WI: TR 570 ms, TE 12 ms, matrix 256 \u0026times; 256. Axial TSE-T2WI: TR 4000 ms, TE 79 ms, matrix 256 \u0026times; 256. Sagittal TSE-T1WI: TR 618 ms, TE 10 ms, matrix 179 \u0026times; 256. Sagittal TSE-T2WI: TR 4000 ms, TE 79 ms, matrix 179 \u0026times; 256. Coronal TSE-T2WI: TR 3800 ms, TE 101 ms, matrix 224 \u0026times; 320. Diffusion-weighted imaging (DWI) was performed using a single-shot spin-echo echo-planar imaging (EPI) sequence for axial scanning, with TR 3500 ms, TE 79 ms, and a matrix of 256 \u0026times; 256. The contrast agent used was Gadovist, at a dose of 0.1 mmol/kg body weight, administered for both axial and sagittal T1WI imaging, with the same parameters as the pre-contrast scans.\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eAll images were reviewed by two experienced radiologists(J.L., 9 years and Z. L., 11 years), using a double-blind method. In cases of disagreement, consensus was reached through joint discussion. The following characteristics of the lesions were specifically observed: location, maximum lesion diameter, morphology, margin, tumor nature (cystic/cystic-solid/solid), presence of wall nodules, septal thickness, T1WI signal, T2WI signal, DWI signal (b\u0026thinsp;=\u0026thinsp;1000), arterial phase enhancement rate, venous phase enhancement rate, maximum diameter of pelvic lymph nodes, and presence of ascites. Cystic lesions were defined as those in which the solid component occupies less than one-third of the lesion, cystic-solid lesions as those with a solid component occupying between one-third and two-thirds of the lesion, and solid lesions as those where the solid component occupies more than two-thirds of the lesion[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The T1WI and T2WI signals were compared with those of the iliopsoas muscle. Septal thickness was measured at its thickest part on the T2WI images. Ascites was diagnosed only after excluding other clinical conditions such as cirrhosis, pelvic inflammation, and pelvic tuberculosis that may cause abdominal effusion.\u003c/p\u003e\n\u003ch3\u003eMeasurement of Enhancement Rate\u003c/h3\u003e\n\u003cp\u003eA circular region of interest (ROI), measuring 20\u0026ndash;40 mm\u0026sup2;, was placed at the solid component of the lesion. The MR values for the pre-contrast scan, arterial phase, and venous phase were measured at the same slice level, with each MR value measured three times. The average value of each measurement was recorded as the final result. The MR arterial phase enhancement rate was calculated as: (arterial phase MR value - pre-contrast MR value) / pre-contrast MR value. The MR venous phase enhancement rate was calculated as: (venous phase MR value - pre-contrast MR value) / pre-contrast MR value.\u003c/p\u003e\n\u003ch3\u003eConstruction of the predictive model\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis was performed for all candidate predictive variables. Variables that showed statistical significance in the univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were subsequently entered into a multivariate logistic regression model to identify independent risk factors for ovarian cancer. The regression coefficients (β values) of the identified independent risk factors were then reassigned to construct the predictive model.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation\u003c/h2\u003e \u003cp\u003eThe predictive model was applied to a validation cohort comprising patients with\u003c/p\u003e \u003cp\u003epathologically confirmed ovarian epithelial tumors who underwent surgery between November 2024 and October 2025. The inclusion and exclusion criteria for the validation cohort were identical to those used for the modeling cohort.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS version 22.0 (Chinese edition) and Medcalc. Normally distributed continuous data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;s), with comparisons made using the independent samples t-test. For continuous data not following a normal distribution, the median (25th\u0026ndash;75th percentile) [M(P25\u0026ndash;P75)] is presented, and inter-group comparisons are made using non-parametric tests. Categorical data are presented as frequencies or percentages, with comparisons made using the χ\u0026sup2; test. Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC), and the model's goodness-of-fit was assessed using the Hosmer-Lemeshow test. The cutoff point was also determined. A \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics and Multiparameter Body Composition\u003c/h2\u003e \u003cp\u003eThe patient grouping is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A comparison of the clinical data and MRI characteristics of ovarian tumors between the two groups revealed statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in tumor margin regularity, tumor nature, presence of papillary wall nodules, T2WI signal, DWI signal, arterial phase enhancement rate, venous phase enhancement rate, short axis of lymph nodes, and the presence of ascites, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison of clinical data and ovarian MRI features between the two patient groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Characteristics and MRI Features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign(n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(47.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(52.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109(90.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion Length(\u003cem\u003ecm\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5(4.2\u0026thinsp;~\u0026thinsp;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6(4.5\u0026thinsp;~\u0026thinsp;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular Morphology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e66.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular Morphology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(56.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic Lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e83.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic-solid Lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(61.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid Lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(27.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary Wall Nodule Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Papillary Wall Nodule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(54.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptal Thickness(\u003cem\u003emm\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid T1 Hypointensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsosignal on T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(34.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperintensity on T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid T2 Hypointensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e68.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsosignal on T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperintensity on T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(70.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWI Isosignal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(87.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e97.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWI Hyperintensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(93.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial Phase Enhancement Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenous Phase Enhancement Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7(0.4\u0026thinsp;~\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(1.0\u0026thinsp;~\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong Axis of Lymph Node(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Ascites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(34.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Logistic Regression Analysis of MRI Characteristics of Ovarian Epithelial Tumors\u003c/h2\u003e \u003cp\u003eVariables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis (as shown in the table above) were included in a logistic regression model. Ovarian epithelial tumor malignancy was set as the dependent variable, with tumor morphology, tumor nature, presence of papillary wall nodules, T2WI signal, DWI signal, arterial phase enhancement rate, venous phase enhancement rate, short axis of lymph nodes, and presence of ascites as independent variables. The multivariate analysis revealed that tumor morphology, DWI signal, and the presence of ascites were independent risk factors for the malignancy of ovarian epithelial tumors, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the multivariate logistic regression analysis of MRI features in ovarian epithelial tumors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald \u003cem\u003eX\u0026sup2;\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular Shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.093(1.191\u0026thinsp;~\u0026thinsp;69.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWI High Signal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.302(1.342\u0026thinsp;~\u0026thinsp;64.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.701(1.788\u0026thinsp;~\u0026thinsp;52.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Validation of the combined predictive model\u003c/h2\u003e \u003cp\u003eThe AUC for tumor morphology, DWI signal, and the presence of ascites were 0.765, 0.902, and 0.754, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A combined predictive model was established based on the regression coefficients of each independent risk factor: combined predictive model\u0026thinsp;=\u0026thinsp;1 \u0026times; irregular morphology\u0026thinsp;+\u0026thinsp;1.010 \u0026times; high DWI signal\u0026thinsp;+\u0026thinsp;1.029 \u0026times; ascites. The results showed that the AUC of the combined predictive model was 0.933, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The cutoff value was 1.5195, with a sensitivity of 81.8%, specificity of 94.0%, and a Youden's index of 0.758. The Hosmer-Lemeshow test indicated good calibration of the combined predictive model (χ\u0026sup2; = 5.436, df\u0026thinsp;=\u0026thinsp;8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.710), demonstrating good agreement between the predicted and actual risk.The AUC of the combined predictive model was significantly higher than that of DWI signal and the presence of ascites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of AUCs between the combined predictive model and individual independent risk factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined predictive modelཞtumor morphology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined predictive modelཞDWI signal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined predictive modelཞpresence of ascites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation of the combined predictive model\u003c/h2\u003e \u003cp\u003eThe external validation cohort consisted of patients with ovarian epithelial tumors confirmed by postoperative histopathology between November 2024 and October 2025, the benign group comprised 41 patients with a total of 44 lesions, while the malignant group included 9 patients with a total of 12 lesions. The combined predictive model achieved an AUC of 0.896 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with a sensitivity of 77.3%, a specificity of 100.0%, and a Youden index of 0.773.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePreoperative imaging plays a crucial role in determining the origin of ovarian tumors and differentiating between benign and malignant lesions. Ultrasound, CT, and MRI are commonly used for preoperative evaluation, with ultrasound being the preferred method for suspected pelvic masses. However, MRI offers superior advantages, including high soft tissue resolution and the absence of ionizing radiation, making it more favorable than CT for evaluating ovarian tumors[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Due to its non-invasive nature and multi-planar imaging capabilities, MRI has become an indispensable tool in the assessment of pelvic lesions[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous studies have primarily focused on diagnosing ovarian tumors based on MRI imaging features[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This diagnostic approach relies heavily on long-term clinical experience and is feasible for senior radiologists; however, limited experience among junior radiologists may lead to reduced accuracy in preoperative diagnosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study aimed to perform univariate and multivariate analyses of MRI imaging features in benign and malignant ovarian epithelial tumors to identify independent risk factors for malignancy based on MRI findings. The regression coefficients of each independent risk factor were quantified numerically to establish a combined predictive model, which was then used to diagnose ovarian epithelial tumor malignancy based on a determined cutoff value. The multivariate analysis revealed that tumor morphology, DWI signal, and the presence of ascites were independent risk factors for malignancy in ovarian epithelial tumors.\u003c/p\u003e \u003cp\u003eUmor morphology observed through MRI is often indicative of the lesion\u0026rsquo;s nature and can significantly guide subsequent diagnosis and therapeutic intervention[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Benign ovarian epithelial tumors typically demonstrate expansive growth with a complete capsule, displacing surrounding tissues without infiltration, which results in regular morphology. However, inflammatory infections or the repair process following torsion or rupture may cause the lesion to present with irregular morphology. In contrast, malignant ovarian epithelial tumors usually exhibit infiltrative growth, leading to the destruction of normal tissue structures and the invasion or destruction of surrounding tissues, which results in an irregular morphology. In this study\u0026rsquo;s univariate analysis, the proportion of lesions with irregular morphology in the malignant group was 56.8%(25/44), significantly higher than the 3.8%༈5/133༉ observed in the benign group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The multivariate analysis identified irregular lesion morphology as an independent risk factor for ovarian cancer, with an AUC of 0.765.\u003c/p\u003e \u003cp\u003eDWI is a non-invasive imaging technique that provides functional information and overcomes the limitations of traditional MRI[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. DWI has become an essential part of gynecological MRI examinations, significantly enhancing the accuracy of imaging reports for gynecological malignant tumors[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Several studies have found that the solid component of ovarian cancer, due to rapid cell growth, increased cell density, and a higher nucleus-to-cytoplasm ratio, restricts the free movement of water molecules, resulting in high signal intensity on DWI[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, benign ovarian tumors typically have solid components characterized by fibrous capsules, septa, and wall nodules, which do not restrict water molecule diffusion, resulting in isointensity or low signal on DWI[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, the incidence of high signal intensity on DWI in the malignant group was 93.2% (41/44), which was significantly higher than the 12.8% (17/133) observed in the benign group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The multivariate analysis found that high signal intensity on DWI is an independent risk factor for ovarian cancer, with an AUC of 0.902.\u003c/p\u003e \u003cp\u003eAscites refers to the accumulation of fluid in the abdominal cavity due to an increase in fluid production or a decrease in absorption under pathological conditions. The primary causes include increased microvascular permeability and lymphatic obstruction[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Ascites plays a significant role in the progression of ovarian malignancies and is often considered an indicator of late-stage ovarian cancer with poor prognosis[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Previous studies have reported that more than one-third of ovarian cancer patients develop ascites[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, the incidence of ascites in the malignant group was 65.9%(29/44), which is higher than reported in the literature. This discrepancy may be due to varying thresholds for the volume of ascites included in previous reports. For this study, any amount of ascites was considered positive after excluding conditions such as cirrhosis, pelvic inflammatory disease, and pelvic tuberculosis, which can also cause abdominal effusion. This definition is consistent with the one used by Ozyilmaz et al[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The multivariate analysis identified ascites as an independent risk factor for ovarian cancer, with an AUC of 0.754.\u003c/p\u003e \u003cp\u003eWhile individual MRI characteristics of ovarian epithelial tumors provide valuable information, they also have limitations and cannot cover all diagnostic aspects comprehensively. Therefore, a more integrated and effective method is required to improve diagnostic accuracy. This study identified three independent risk factors for ovarian epithelial tumors\u0026mdash;tumor morphology, DWI signal, and the presence of ascites\u0026mdash;with AUCs of 0.765, 0.902, and 0.754, respectively. The AUC of the combined predictive model increased to 0.933, with a sensitivity of 81.8%, a specificity of 94.0%, and a Youden index of 0.758. The Hosmer\u0026ndash;Lemeshow test indicated good calibration and satisfactory model stability. External validation further demonstrated that the combined predictive model achieved an AUC of 0.896, with a sensitivity of 77.3%, a specificity of 100.0%, and a Youden index of 0.773, indicating favorable predictive performance.. In recent years, numerous studies and reviews have reported the application of radiomics for predicting the benign or malignant nature of ovarian tumors[\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, radiomics has not yet been widely adopted in routine clinical practice, particularly in primary hospitals where the required technical infrastructure and expertise are often lacking. In the present study, independent risk factors were quantitatively integrated using a simplified mathematical formula, thereby transforming a complex diagnostic process into an easily applicable clinical tool. This approach is feasible for implementation across hospitals at different levels and may assist junior radiologists in improving the accuracy of preoperative diagnostic assessment.\u003c/p\u003e \u003cp\u003eThis study was conducted at a single center, which may have introduced selection bias and limited the generalizability of the findings. In addition, the relatively small sample size and the imbalance between the two groups may increase the risk of model overfitting. Future studies should include larger, multicenter cohorts, incorporate additional clinical and laboratory variables, and perform external validation to develop a more robust and reliable predictive model for clinical decision-making.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study developed a combined predictive model for epithelial ovarian tumors based on clinical data and MRI imaging features. The model demonstrated favorable predictive performance and good stability. Owing to its simplicity and ease of calculation, this combined predictive model may serve as a practical and reliable tool to assist preoperative diagnosis in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eL.Q. and Y.Z. contributed equally to this work. L.Q. and Y.Z. were responsible for data curation, data analysis and manuscript drafting of the manuscript. J.L. performed medical image processing and radiomic feature extraction. P.W. participated in the critical review, editing, and intellectual refinement of the manuscript. Z.L. contributed to statistical analysis, experimental design, conceptualized and supervised the project, provided funding acquisition, and approved the final manuscript. All authors read and approved the submitted version.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Mil Med Res. 11:77.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40779-024-00580-1\u003c/span\u003e\u003cspan address=\"10.1186/s40779-024-00580-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ovarian tumors, Magnetic resonance imaging, Prediction model, LASSO regression, Diffusion weighted imaging","lastPublishedDoi":"10.21203/rs.3.rs-9443484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9443484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo develop and validate a combined predictive model based on MRI features for differentiating benign from malignant epithelial ovarian tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials\u003c/strong\u003e \u003cstrong\u003eand methods\u003c/strong\u003e Patients were classified as having benign or malignant tumors according to postoperative histopathology. MRI features were evaluated, and independent predictors were identified using univariate and multivariate analyses. A combined predictive model was constructed based on regression coefficients and externally validated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eThe benign group comprised 121 patients (133 lesions), and the malignant group comprised 36 patients (44 lesions). Tumor morphology, diffusion-weighted imaging (DWI) signal intensity, and the presence of ascites were independent predictors of malignancy, with AUCs of 0.765, 0.902, and 0.754, respectively. The combined model (1 × irregular morphology + 1.010 × high DWI signal + 1.029 × ascites) achieved an AUC of 0.933, with a sensitivity of 81.8% and specificity of 94.0%. The Hosmer–Lemeshow test indicated good calibration (P = 0.710). In the external validation cohort (41 benign patients with 44 lesions; 9 malignant patients with 12 lesions), the model yielded an AUC of 0.896, with a sensitivity of 77.3% and specificity of 100.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eThe MRI-based combined model demonstrates high diagnostic performance and good stability, providing a practical and reliable tool for preoperative differentiation of epithelial ovarian tumors.\u003c/p\u003e","manuscriptTitle":"MRI-based predictive model for differentiating epithelial ovarian tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:24:53","doi":"10.21203/rs.3.rs-9443484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69534307362783934389994075659299506347","date":"2026-04-26T17:02:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T09:55:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T15:33:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T09:59:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2026-04-17T03:16:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a95b738e-f818-4ed8-ba7d-3209803584e5","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:17:46+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:24:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:24:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9443484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9443484","identity":"rs-9443484","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}