A diagnostic nomogram model integrating the O-RADS and serum indexes for predicting malignancy in patients with ovarian masses

A diagnostic nomogram model integrating the O-RADS and serum indexes for predicting malignancy in patients with ovarian 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 A diagnostic nomogram model integrating the O-RADS and serum indexes for predicting malignancy in patients with ovarian masses Yifan Gu, Lingling Zhao, Weiwei Chen, Lei Yang, Cheng Qian, Mengdan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5296411/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To design a diagnostic nomogram model integrating the Ovarian-Adnexal Reporting and Data System (O-RADS) and serum indexes for predicting malignancy in patients with ovarian masses. Methods This was a retrospective study including 201 benign ovarian masses patients and 136 ovarian cancer (OC) patients from June 2011 to April 2022. Before surgical resection, all patients underwent transvaginal ultrasound, as well as transabdominal ultrasound examination, and tumour parameters according to O-RADS (morphology, internal echo, blood supply, etc.) were assessed. Meanwhile, serum indexes, including cancer antigen 125 (CA125) and neutrophil to lymphocyte ratio (NLR), were tested. After surgical resection, all patients were pathologically diagnosed. The differences in serological indexes and O-RADS scores between the benign and malignant groups were analysed using the Mann-Whitney U test. ROC curves were firstly used to determine their optimal cut-off values. Univariate and multivariate logistic regression analyses were used to identify CA125, NLR and O-RADS for OC. Then, the prediction nomogram model was established. A decision curve analysis (DCA) was performed to assess the clinical net benefit of the model. The calibration curve and the Hosmer–Lemeshow test were performed to assess the calibration and the goodness-of-fit of the nomogram model respectively. Results A total of 337 women [median age:45(32–57)] with 337 ovarian masses were included. Of the 337 ovarian masses, 201 were benign (benign group) and 136 were malignant (malignant group). CA125, NLR and O-RADS in the malignant group were significantly higher compared to the benign group. These parameters were then incorporated to develop a nomogram model, and this model showed an area under the ROC curve of 0.942 (95% confidence interval, 0.917–0.968), with 97.800% sensitivity and 76.600% specificity. The calibration curve showed a good fitting degree. Meanwhile, DCA provided a net benefit for a range of threshold probabilities. Conclusions This nomogram model yielded a favourable diagnostic accuracy for predicting malignancy in patients with ovarian masses. O-RADS CA-125 NLR ovarian nomogram model Figures Figure 1 Figure 2 Figure 3 Figure 4 Simple Summary Ovarian cancer (OC) is one of the most common malignancy in women worldwide, accounting for more deaths than any other type of cancer in gynecology. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages. Our nomogram model yielded a favourable diagnostic accuracy for predicting malignancy in patients with ovarian masses. Background Ovarian cancer (OC) is the eighth most common malignancy in women worldwide, accounting for more deaths than any other type of cancer in gynecology [ 1 ] . Due to late detection, as 67% patients are diagnosed at advanced stages, the overall 5-year survival rate is approximately 46%. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages [ 2 , 3 ] . As the first-line imaging method for ovarian masses, ultrasound has the advantages of high resolution, low price and no ionising radiation exposure. Many studies and institutions have proposed many different diagnostic scoring models to distinguish benign ovarian masses from malignant, such as international ovarian tumour analysis (IOTA) simple rules, gynaecologic imaging reporting and data system (GI-RADS) and the Ovarian-Adnexal Reporting and Data System (O-RADS) [ 4 ] . O-RADS was released by the American College of Radiology (ACR) O-RADS Ultrasound committee in 2018 [ 5 – 7 ] . It includes six categories: (1) main classification (physiological or pathological), (2) size, (3) solid or solid-like lesions (overall morphology and internal echo), (4) cystic lesions (inner edge or wall containing solid components, contents and cystic components), (5) blood supply and (6) extra-ovarian manifestations. Compared with IOTA simple rules and GI-RADS, O-RADS demonstrated a higher sensitivity with relatively similar specificity and reliability [ 4 ] . However, some studies revealed that the specificity of O-RADS was slightly decreased, indicating an increase in false-positive outcomes. In other terms, more benign ovarian masses may be diagnosed as malignant, resulting in overtreatment of ovarian masses [ 4 ] . Therefore, it is essential to establish an available method to predict the malignancy of ovarian masses and guide clinical treatment. Serological examination is a test that can be performed in patients suspected of malignancies or as a routine preoperative examination. CA125 is the most commonly used tumour marker for ovarian cancer (OC) [ 1 , 8 ] . Meanwhile, inflammation also plays a key role in the occurrence and progression of cancer. Many studies have demonstrated that neutrophil to lymphocyte ratio (NLR), which is a useful marker in the assessment of inflammatory response, can be used as an indicator for the diagnosis and prognosis of many cancers [ 9 – 13 ] . Many previous studies have reported that NLR can distinguish between benign and malignant ovarian masses [ 14 – 17 ] , but its truncation values remain different [ 18 ] . Therefore, the integration of the two serological parameters and O-RADS into a model would be helpful to improve the diagnostic accuracy and optimise the subsequent treatment plans and surgical choices. In our study, we aimed to design a diagnostic nomogram model for predicting malignant ovarian masses by integrating some items, including O-RADS, CA125 and NLR. Materials and methods Patients This study is a retrospective study. Patients who underwent ovarian mass resection in the Affiliated Hospital of XX University from June 2011 to April 2022 were included. Inclusion criteria encompassed: (1) patients had undergone an initial operation and the pathological result was OC or benign ovarian mass; (2) patients had already performed blood routine and tumour marker tests within two weeks before operation. Exclusion criteria were as follows: (1) patients who had undergone preoperative radiotherapy, chemotherapy, targeted therapy or other antitumor therapy; (2) patients with a history of other malignant tumours; (3) loss of ultrasonic diagnostic data or serological data; (4) the pathological diagnosis is borderline tumour or metastatic tumour. A total of 337 patients (201 benign tumours and 136 ovarian cancers) were included in the analysis (Fig. 1 ). Informed consent was obtained for using the patients’ medical records for research purposes, and this study was approved by the institutional review board of the Affiliated Hospital of XX University. Diagnostic ultrasound data were obtained retrospectively from the patients’ medical records. According to pathological findings, patients were divided into two groups: the OC group and benign ovarian mass groups. CA125 and NLR were tested two weeks before surgery. Diagnostic ultrasound reports were reviewed within two weeks before surgery and scored according to the O-RADS scoring rules. This was carried out by two radiologists with more than five years of experience in ultrasound. Statistical analysis The kappa coefficients were applied to assess the observer consistency of the O-RADS classification through κ value. The differences in serological indexes and O-RADS scores between the benign and malignant groups were analysed using the Mann-Whitney U test. ROC curves were firstly used to determine their optimal cut-off values. Univariate and multivariate logistic regression analyses were utilized to analyse risk factors for OC. Then, a nomogram model was established to help diagnose ovarian malignancies. ROC curves were used to evaluate the ability of these indexes and the model to distinguish benign from malignant groups. Estimates were given as 95% confidence intervals (CIs). The calibration curve and the Hosmer–Lemeshow test were performed to assess the calibration and the goodness-of-fit of the nomogram model respectively. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model. Finally, we used the 10 fold cross validation method for internal validation. All statistical analysis was performed using the R version 4.0.1 and SPSS version 26.0. Results Patient characteristics In our study, 1075 patients were screened, and 738 patients were excluded according to the exclusion criteria. A total of 337 eligible patients were included, including 201 patients with benign tumours, and 136 patients with malignant tumours on the basis of pathological diagnosis. The pathological characteristics and O-RADS of the patients are shown in Table 1 . The κ values were 0.906 (95%CI:0.868–0.940) and 0.885 (95%CI:0.842–0.923) for intra and inter-observer agreement respectively, indicating perfect agreement in O-RADS classification. Compared to the benign group, the CA125, NLR levels and O-RADS findings in the malignant group were significantly higher (P < 0.01) (Table 1 ). The histologic types of malignant tumours included serous, endometrioid carcinoma, clear cell carcinoma, granulosa cell tumour, asexual cell tumour, mucinous carcinoma, teratoma, embryo sinus tumour, transitional cell carcinoma and adenosarcoma (Table 2 ). Table 1 Pre-operative clinical characteristics of patients with ovarian masses. Variables Total (N = 337) Benign (N = 201) Malignant (N = 136) P -value Age (years) Median (IQR) 45(32–57) 36(29–53) 53(45.75-60) < 0.001 CA125 (U/ml) Median (IQR) 23.8(13.6-155.75) 16.1(11.3–24) 215.9(58.625–833.1) < 0.001 NLR Median (IQR) 1.945(1.4419–2.8008) 1.7898(1.388–2.374) 2.321(1.651–3.6856) < 0.001 Menopausal Yes 125 53 72 No 212 148 64 < 0.001 O_RADS 0–2 169 160 9 3–6 168 41 127 < 0.001 CA125: cancer antigen 125, NLR: neutrophil to lymphocyte ratio, O_RADS: Ovarian-Adnexal Reporting and Data System, IQR: interquartile range Table 2 Malignant ovarian masses of different pathological types. Subtype N Percentage (%) Serous carcinoma 69 50.74 Mucinous carcinoma 4 2.94 Endometrioid carcinoma 16 11.76 Clear cell carcinoma 11 8.09 Granulosa cell tumor 7 5.15 Dysgerminoma 4 2.94 Teratoma 3 2.21 Embryo sinus tumor 4 2.94 Transitional cell carcinoma 3 2.21 Adenosarcoma 1 0.74 Building a nomogram prediction model According to the ROC analysis, a cut-off value of 40.3 U/mL was determined for CA125, whereas a cut-off value of 2.159 was determined for NLR. Those with O-RADS scores of 0, 1 and 2 were divided into a group, and those with O-RADS scores of 3, 4 and 5 were divided into a group. The data were converted into binary variables for univariate and multivariate logistic regression analysis. In accordance with univariate and multivariate logistic regression analyses, CA125, NLR and O-RADS were independent risk factors of OC (Table 3 ). Subsequently, a nomogram integrating CA125, NLR and O-RADS was designed to visualise the model and effectively predict the probability risk of OC for patients with ovarian masses (Fig. 2 a). The diagnostic equation was established as follows: \(\:\) \(\:Ln\frac{p}{p(1-p)}=-3.1+3.21\times\:CA125+3.10\times\:O-RADS+0.96\times\:NLR\) Table 3 Univariate and multivariate logistic regression analysis. Variables Univariate analysis Multivariate analysis OR (95%CI) P -value OR (95%CI) P -value CA125 > cut-off (40.3) 28.476(15.728,51.557) < 0.001 14.612(6.862,31.116) < 0.001 O-RADS (1–2,3–5) 55.068(25.801,117.532) < 0.001 35.202(14.764,83.935) cut-off (2.159) 2.772(1.768,4.345) < 0.001 2.306 (1.086,4.898) < 0.001 OR: odds ratio, CA125: cancer antigen 125, NLR: neutrophil to lymphocyte ratio, O_RADS: Ovarian-Adnexal Reporting and Data System, CI: confidence interval Logit P=-4.208 + 3.561×O-RADS + 2.682×CA125 + 0.835×NLR ROC analysis indicated that the AUC of nomogram model was 0.942(95%CI, 0.917,0.968), with 97.800% sensitivity and 76.6% specificity (Fig. 2 b, Table 4 ). The AUC of CA125, NLR and O-RADS were performed as well, and the area under the ROC curve (AUC) was 0.840(95%CI, 0.793,0.887) with 80.90% sensitivity and 87.100% specificity, 0.624(95%CI, 0.563,0.685), with 58.100% sensitivity and 66.700% specificity and 0.865(95%CI, 0.824,0.906), with 93.400% sensitivity and 79.600% specificity respectively (Fig. 2 , Table 4 ). The value of the nomogram model can be assessed using DCA, which exhibited a net benefit in clinical practice. These findings demonstrated that the model is a reliable tool for prediction of malignant tumours in patients with ovarian masses (Fig. 2 d). The nomogram model was also well calibrated with a non-significant result (P = 0.797) (Fig. 2 c). Finally, we used the 10 fold cross validation method for internal validation and it got a good concordance of 0.919 for C-index. Table 4 The ROC analysis of CA125, NLR, O-RADS and the model. Variable AUC SN (%) SP (%) P -value 95%CI Lower Upper CA125 0.840 80.9 87.1 < 0.001 0.793 0.887 NLR 0.624 58.1 66.7 < 0.001 0.563 0.685 O-RADS 0.865 93.4 79.6 < 0.001 0.824 0.906 Model 0.942 97.8 76.6 < 0.001 0.917 0.968 AUC: area under curve, SN: Sensitivity, SP: Specificity, CI: confidence interval We used this model to score two cases. Case 1 was pathologically diagnosed as high-grade serous carcinoma of the left ovary after operation in our hospital. Its ultrasound examination showed a 74*46mm hypoechoic mass with irregular shape, unclear boundary and abundant blood flow (Fig. 3 a). Nomogram showed that this case had a total of 175 points after summing all points (100 + 75 + 0), which corresponded to a 88.0% probability of ovarian cancer (Fig. 3 b). Another case was pathologically diagnosed as multilocular cystic serous gonadal fibroma after surgery in our hospital. Its ultrasonic examination showed a mixed echo (mainly cystic) with regular shape, clear boundary and no blood flow signal (65*44mm in size) (Fig. 4 a). Nomogram showed that this case had a total of 23 points after summing all points (0 + 0 + 23), which corresponded to a 4.0% probability of ovarian cancer (Fig. 4 b). Discussion OC is often fatal because it has not been detected until the disease has progressed to advanced stages. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages. However, there is still a lack of widely available, cost-effective methods to predict the potential malignant ovarian masses. Herein, we present the first retrospective study documenting the diagnostic performance of a model that combined O-RADS and serum indicators in patients with ovarian masses. This model showed an area under the ROC curve (AUC) of 0.942 (95% confidence interval [CI], 0.917–0.968), with 97.800% sensitivity and 76.600% specificity. Based on its higher or identical sensitivity compared to a separate indicator, this model can be used as an alternative method for the differential diagnosis of benign and malignant ovarian masses. Failure to detect malignant tumours in time during the examination is bound to reduce both the survival rate and quality of life of patients. Therefore, our model is crucial because it can distinguish between OC and benign tumours, which is essential for taking timely and effective measures. Because of the unsatisfied performance of a single parameter in distinguishing malignant ovarian masses, a variety of integrative models have been developed. Nearly 30 years ago, the Risk of Malignancy Index (RMI) was developed into a diagnostic model incorporating with CA125, imaging score and the menopausal status. The risk of ovarian malignancy algorithm (ROMA), integrating HE4, CA125 and the menopause status, indicating better performance than RMI for the prediction of malignant ovarian masses [ 19 ] . However, some reports demonstrated that ROMA did not exhibit better performance than CA125 or HE4 alone [ 20 , 21 ] . Consequently, it is essential to develop a method to distinguish the OC from benign ovarian masses. Previous studies in this field have proposed that O-RADS has similar sensitivity with some scoring systems in preoperative differentiation of benign and malignant pelvic tumours [ 22 ] . Pi et al. have pointed out that O-RADS has excellent specificity and specificity in experimental studies that evaluated benign and malignant ovarian masses [ 23 ] . In recent years, inflammation has been shown to be a major factor in the progress and spread of cancer in the human body [ 24 ] . The inflammatory response to tumour cells may bring about irreversible DNA damage by inhibiting OC cell apoptosis and triggering angiogenesis [ 25 ] . Therefore, it has been shown that inflammatory markers in the blood may achieve significant levels in various types of cancer [ 26 ] . Cho et al found that the preoperative NLR of patients with OC was significantly higher than that of patients with benign ovarian masses [ 14 ] . In the past three decades, CA125 has become the most widely used biomarker of OC [ 27 ] . Moreover, CA125 concentrations are increased in many non-malignant conditions, affecting its specificity significantly. Thus, in contrast to only focusing on a single index, it is reasonable to construct a diagnostic model that incorporates several useful parameters to predict the malignancy of ovarian masses. To our knowledge, this is the first retrospective study to construct a nomogram model by incorporating O-RADS, CA125 and NLR. This model yielded excellent performance, with an AUC of 0.942, sensitivity of 97.800% and specificity of 76.600%. In contrast, these values for CA125 alone were 0.840, 80.900% and 87.100%, respectively. Similarly, NLR alone exhibited an AUC of 0.624, and sensitivity and specificity values of 58.100% and 66.700%, respectively, and O-RADS showed an AUC of 0.865, sensitivity of 93.400% and specificity of 79.600%. These results suggest that the nomogram model proposed in our study has better performance than CA125, NLR, or O-RADS alone. In addition, the calibration curve showed that the nomogram has a good fitness and point We believe that the clinical application of this nomogram can play a crucial role in guiding decision-making processes in clinicians and avoiding unnecessary surgery in patients with benign ovarian masses. The strength of this study lies in its use of ultrasound technology and serum indexes for ovarian masses. The final results confirmed that our model could effectively combine O-RADS with serum indexes to distinguish malignant ovarian masses from benign group with good diagnostic efficiency. Our study also has several limitations. First, this study is a retrospective study, thus a prospective study with a large sample size is necessary for the clinical application of our model as a screening tool for OC. Second, although the nomogram model has better performance, external validation is still required. Conclusion In conclusion, we designed a diagnostic nomogram model to predict malignancy in patients with ovarian masses by integrating CA125, NLR and US O-RADS. This nomogram model has good diagnostic efficiency in distinguish benign and malignant ovarian masses. We would like to see more studies on the use of serum markers in combination with ultrasound, especially novel ultrasound techniques, to select for the diagnosis of female patients with OC. Abbreviations O-RADS Ovarian-Adnexal Reporting and Data System OC ovarian cancer CA125 cancer antigen 125 NLR neutrophil to lymphocyte ratio DCA Decision curve analysis Declarations Authors’ contributions Data curation, Yifan Gu, Weiwei Chen, Lei Yang and Cheng Qian; Formal analysis, Xiaoyang Chen; Funding acquisition, Yifei Yin; Investigation, Yifei Yin; Methodology, Yifan Gu, Lingling Zhao, Mengdan Li and Yifei Yang; Resources, Lingling Zhao, Weiwei Chen and Yifei Yin; Software, Lei Yang; Validation, Yifan Gu, Cheng Qian, Mengdan Li and Yifei Yang; Writing – original draft, Yifan Gu and Lingling Zhao; Writing – review & editing, Xiaoyang Chen and Yifei Yin. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor. Funding This research was funded by China Postdoctoral Science Foundation(2022M711721). Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Affiliated Hospital of XX University (2022-k101-01). Informed Consent Statement: Not applicable. Data Availability Statement: The data and the code used for the experiments are available on request. Acknowledgements We thank the study participants for permitting us to use their personal data. Conflicts of Interest The authors declare no conflict of interest. References Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics. CA Cancer J Clin. 2018;68(4):284–96. Goff BA, Mandel LS, Melancon CH, et al. Frequency of symptoms of ovarian cancer in women presenting to primary care clinics. JAMA. 2004;291(22):2705–12. Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2020;70(1):7–30. Basha MA, Metwally MI, Gamil SA, et al. Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses. Eur Radiol. 2021;31(2):674–84. Cao L, Wei M, Liu Y, et al. Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses. Gynecol Oncol. 2021;162(1):107–12. Andreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):168–85. Strachowski LM, Jha P, Chawla TP, et al. O-RADS for Ultrasound: A User's Guide, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol. 2021;216(5):1150–65. Nossov V, Amneus M, Su F, et al. The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol. 2008;199(3):215–23. Diem S, Schmid S, Krapf M, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176–81. He YF, Luo HQ, Wang W, et al. Preoperative NLR and PLR in the middle or lower ESCC patients with radical operation. Eur J Cancer Care. 2017;26(2):e12445. Miyamoto R, Inagawa S, Sano N, et al. The neutrophil-to-lymphocyte ratio (NLR) predicts short-term and long-term outcomes in gastric cancer patients. Ejso-Eur J Surg Onc. 2018;44(5):607–12. Hu H, Yao X, Xie X, et al. Prognostic value of preoperative NLR, dNLR, PLR and CRP in surgical renal cell carcinoma patients. World J Urol. 2017;35(2):261–70. Ozmen S, Timur O, Calik I, et al. Neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) may be superior to C-reactive protein (CRP) for predicting the occurrence of differentiated thyroid cancer. Endocr Regul. 2017;51(3):131–6. Cho H, Hur HW, Kim SW, et al. Pre-treatment neutrophil to lymphocyte ratio is elevated in epithelial ovarian cancer and predicts survival after treatment. Cancer Immunol Immun. 2009;58(1):15–23. Eo WK, Kim KH, Park EJ, et al. Diagnostic accuracy of inflammatory markers for distinguishing malignant and benign ovarian masses. J Cancer. 2018;9(7):1165–72. Yildirim M, Demir Cendek B, Filiz Avsar A. Differentiation between benign and malignant ovarian masses in the preoperative period using neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios. Mol Clin Oncol. 2015;3(2):317–21. Bakacak M, Serin S, Ercan O, et al. Utility of preoperative neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios to distinguish malignant from benign ovarian masses. J Turk Ger Gynecol Assoc. 2016;17(1):21–5. Li L, Tian J, Zhang L, et al. Utility of Preoperative Inflammatory Markers to Distinguish Epithelial Ovarian Cancer from Benign Ovarian Masses. J Cancer. 2021;12(9):2687–93. Moore RG, McMeekin DS, Brown AK, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112(1):40–6. Van Gorp T, Cadron I, Despierre E, et al. HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm. Br J Cancer. 2011;104(5):863–70. Aslan K, Onan MA, Yilmaz C, et al. Comparison of HE 4, CA 125, ROMA score and ultrasound score in the differential diagnosis of ovarian masses. J Gynecol Obstet Hum Reprod. 2020;49(5):101713. Hiett AK, Sonek J, Guy M, et al. Performance of IOTA Simple Rules, Simple Rules Risk assessment, ADNEX model and O-RADS in discriminating between benign and malignant adnexal lesions in North American population. Ultrasound Obstet Gynecol. 2021;59(5):668–76. Pi Y, Wilson MP, Katlariwala P, et al. Diagnostic accuracy and inter-observer reliability of the O-RADS scoring system among staff radiologists in a North American academic clinical setting. Abdom Radiol (NY). 2021;46(10):4967–73. Jackson JR, Seed MP, Kircher CH, et al. The codependence of angiogenesis and chronic inflammation. FASEB J. 1997;11(6):457–65. Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–99. Cedres S, Torrejon D, Martínez A, et al. Neutrophil to lymphocyte ratio (NLR) as an indicator of poor prognosis in stage IV non-small cell lung cancer. Clin Transl Oncol. 2012;14(11):864–9. Nowak M, Janas L, Stachowiak G, et al. Current clinical application of serum biomarkers to detect ovarian cancer. Prz Menopauzalny. 2015;14(4):254–9. Additional Declarations No competing interests reported. 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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-5296411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368672535,"identity":"5ececaed-f0a8-4b74-a844-798a75c094ef","order_by":0,"name":"Yifan Gu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Gu","suffix":""},{"id":368672536,"identity":"65849077-d689-416b-9cb1-85b3245aedb6","order_by":1,"name":"Lingling Zhao","email":"","orcid":"","institution":"Second Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Zhao","suffix":""},{"id":368672537,"identity":"c99c10e0-c952-4f27-9503-840d92193f93","order_by":2,"name":"Weiwei Chen","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Chen","suffix":""},{"id":368672538,"identity":"3f46615e-9cff-40ff-8f79-3f543ba3eb7a","order_by":3,"name":"Lei Yang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yang","suffix":""},{"id":368672539,"identity":"dafee064-7814-4d40-bacd-916cd300c216","order_by":4,"name":"Cheng Qian","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Qian","suffix":""},{"id":368672540,"identity":"2fe7a010-4f28-43b8-b722-45294da989b0","order_by":5,"name":"Mengdan Li","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Mengdan","middleName":"","lastName":"Li","suffix":""},{"id":368672541,"identity":"ec5f7557-19b0-4eee-abde-f1f6c14b4161","order_by":6,"name":"Yifei Yang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Yang","suffix":""},{"id":368672542,"identity":"78506c04-d52e-40a0-829a-27adc8a6f3fd","order_by":7,"name":"Xiaoyang Chen","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Chen","suffix":""},{"id":368672543,"identity":"89c623c0-d8fd-4851-8aa7-6ba62fc7dfad","order_by":8,"name":"Yifei Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PsWrDMBCA4RMq8XJxVgUKzSMoFEIG4bzKBYO6ZMgjnMmQJZDVj9FHkHskWQJdW+hQKHT26KHQJmMX22Oh+uAWcT+HAKLoL9KXIQeo9gWH2rqsZ+IBdSlFVa593vOUB0j4YSNYPynuWran4dG8k7sdcsXibNCQyOGxNZHUGyKP403BsrJvKaD3L+0JzsyyEZxer6zspwaDs+6E6BsXYckyt6K4ZxJQ8SWBPslY8H5OlKMqK6521ueDrr+kz+fpa0PZQu23H3Xz5bJRIsfWZBLgxvx6GbStX90x6LprKYqi6J/7AfD2UNfRAq5jAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2024-10-20 01:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5296411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5296411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68540859,"identity":"202b880a-4b6d-47cf-8616-6e14a708b27e","added_by":"auto","created_at":"2024-11-08 10:48:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59353,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5296411/v1/5b5b6edb52b524099de227c4.jpg"},{"id":68540860,"identity":"3439ab0a-04b5-4beb-a888-57b76fe3ffe6","added_by":"auto","created_at":"2024-11-08 10:48:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363184,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A diagnostic nomogram to predict the risk probability of ovarian cancer for patients with ovarian masses. (b) ROC analysis of CA125, NLR and O-RADS and the model. This model showed an area under the ROC curve (AUC) of 0.942 (95% confidence interval [CI], 0.917-0.968), with 97.800% sensitivity and 76.600% specificity. (c)The calibration curve of the model. (d) DCA for the nomogram model. AUC=area under curve, DCA=decision curve analysis.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5296411/v1/1b49a13d3a9aec40251b1bc5.jpg"},{"id":68540862,"identity":"06d38ae2-3666-433f-bd3a-783040d48885","added_by":"auto","created_at":"2024-11-08 10:48:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":216533,"visible":true,"origin":"","legend":"\u003cp\u003eThe case was pathologically diagnosed as high-grade serous carcinoma of the left ovary after operation in our hospital. (a) Ultrasound examination showed a 74*46mm hypoechoic mass with irregular shape, unclear boundary and abundant blood flow. (b)Nomogram shows that this case had a total of 175 points after summing all points (100+75+0), which corresponds to an 88% probability of ovarian cancer.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5296411/v1/1bec4036a7d09469a945ad2c.jpg"},{"id":68540861,"identity":"1fe2f852-e641-4c2c-bb87-eeba2233fceb","added_by":"auto","created_at":"2024-11-08 10:48:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":236208,"visible":true,"origin":"","legend":"\u003cp\u003eThe case was pathologically diagnosed as multilocular cystic serous gonadal fibroma after surgery in our hospital. (a)Ultrasonic examination showed a mixed echo (mainly cystic) with regular shape, clear boundary and no blood flow signal (65*44mm in size). (b)Nomogram shows that this case had a total of 23 points after summing all points (0+0+23),which corresponds to a 4% probability of ovarian cancer.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5296411/v1/a6d74a0d076dbda8b643e117.jpg"},{"id":75503296,"identity":"25125829-964f-4369-9fef-d6e70f4a4149","added_by":"auto","created_at":"2025-02-05 09:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1588163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5296411/v1/78c434b3-d416-4392-bc20-7159c182e32c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A diagnostic nomogram model integrating the O-RADS and serum indexes for predicting malignancy in patients with ovarian masses","fulltext":[{"header":"Simple Summary","content":"\u003cp\u003eOvarian cancer (OC) is one of the most common malignancy in women worldwide, accounting for more deaths than any other type of cancer in gynecology. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages. Our nomogram model yielded a favourable diagnostic accuracy for predicting malignancy in patients with ovarian masses.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eOvarian cancer (OC) is the eighth most common malignancy in women worldwide, accounting for more deaths than any other type of cancer in gynecology\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Due to late detection, as 67% patients are diagnosed at advanced stages, the overall 5-year survival rate is approximately 46%. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs the first-line imaging method for ovarian masses, ultrasound has the advantages of high resolution, low price and no ionising radiation exposure. Many studies and institutions have proposed many different diagnostic scoring models to distinguish benign ovarian masses from malignant, such as international ovarian tumour analysis (IOTA) simple rules, gynaecologic imaging reporting and data system (GI-RADS) and the Ovarian-Adnexal Reporting and Data System (O-RADS)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. O-RADS was released by the American College of Radiology (ACR) O-RADS Ultrasound committee in 2018\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. It includes six categories: (1) main classification (physiological or pathological), (2) size, (3) solid or solid-like lesions (overall morphology and internal echo), (4) cystic lesions (inner edge or wall containing solid components, contents and cystic components), (5) blood supply and (6) extra-ovarian manifestations. Compared with IOTA simple rules and GI-RADS, O-RADS demonstrated a higher sensitivity with relatively similar specificity and reliability\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, some studies revealed that the specificity of O-RADS was slightly decreased, indicating an increase in false-positive outcomes. In other terms, more benign ovarian masses may be diagnosed as malignant, resulting in overtreatment of ovarian masses\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is essential to establish an available method to predict the malignancy of ovarian masses and guide clinical treatment.\u003c/p\u003e \u003cp\u003eSerological examination is a test that can be performed in patients suspected of malignancies or as a routine preoperative examination. CA125 is the most commonly used tumour marker for ovarian cancer (OC)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, inflammation also plays a key role in the occurrence and progression of cancer. Many studies have demonstrated that neutrophil to lymphocyte ratio (NLR), which is a useful marker in the assessment of inflammatory response, can be used as an indicator for the diagnosis and prognosis of many cancers\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Many previous studies have reported that NLR can distinguish between benign and malignant ovarian masses\u003csup\u003e[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, but its truncation values remain different\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, the integration of the two serological parameters and O-RADS into a model would be helpful to improve the diagnostic accuracy and optimise the subsequent treatment plans and surgical choices. In our study, we aimed to design a diagnostic nomogram model for predicting malignant ovarian masses by integrating some items, including O-RADS, CA125 and NLR.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis study is a retrospective study. Patients who underwent ovarian mass resection in the Affiliated Hospital of XX University from June 2011 to April 2022 were included. Inclusion criteria encompassed: (1) patients had undergone an initial operation and the pathological result was OC or benign ovarian mass; (2) patients had already performed blood routine and tumour marker tests within two weeks before operation. Exclusion criteria were as follows: (1) patients who had undergone preoperative radiotherapy, chemotherapy, targeted therapy or other antitumor therapy; (2) patients with a history of other malignant tumours; (3) loss of ultrasonic diagnostic data or serological data; (4) the pathological diagnosis is borderline tumour or metastatic tumour. A total of 337 patients (201 benign tumours and 136 ovarian cancers) were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Informed consent was obtained for using the patients\u0026rsquo; medical records for research purposes, and this study was approved by the institutional review board of the Affiliated Hospital of XX University.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiagnostic ultrasound data were obtained retrospectively from the patients\u0026rsquo; medical records. According to pathological findings, patients were divided into two groups: the OC group and benign ovarian mass groups. CA125 and NLR were tested two weeks before surgery. Diagnostic ultrasound reports were reviewed within two weeks before surgery and scored according to the O-RADS scoring rules. This was carried out by two radiologists with more than five years of experience in ultrasound.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe kappa coefficients were applied to assess the observer consistency of the O-RADS classification through κ value. The differences in serological indexes and O-RADS scores between the benign and malignant groups were analysed using the Mann-Whitney U test. ROC curves were firstly used to determine their optimal cut-off values. Univariate and multivariate logistic regression analyses were utilized to analyse risk factors for OC. Then, a nomogram model was established to help diagnose ovarian malignancies. ROC curves were used to evaluate the ability of these indexes and the model to distinguish benign from malignant groups. Estimates were given as 95% confidence intervals (CIs). The calibration curve and the Hosmer\u0026ndash;Lemeshow test were performed to assess the calibration and the goodness-of-fit of the nomogram model respectively. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model. Finally, we used the 10 fold cross validation method for internal validation. All statistical analysis was performed using the R version 4.0.1 and SPSS version 26.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eIn our study, 1075 patients were screened, and 738 patients were excluded according to the exclusion criteria. A total of 337 eligible patients were included, including 201 patients with benign tumours, and 136 patients with malignant tumours on the basis of pathological diagnosis. The pathological characteristics and O-RADS of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The κ values were 0.906 (95%CI:0.868\u0026ndash;0.940) and 0.885 (95%CI:0.842\u0026ndash;0.923) for intra and inter-observer agreement respectively, indicating perfect agreement in O-RADS classification. Compared to the benign group, the CA125, NLR levels and O-RADS findings in the malignant group were significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The histologic types of malignant tumours included serous, endometrioid carcinoma, clear cell carcinoma, granulosa cell tumour, asexual cell tumour, mucinous carcinoma, teratoma, embryo sinus tumour, transitional cell carcinoma and adenosarcoma (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003ePre-operative clinical characteristics of patients with ovarian masses.\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=\"left\" 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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;136)\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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(32\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(29\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(45.75-60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125 (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8(13.6-155.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1(11.3\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215.9(58.625\u0026ndash;833.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.945(1.4419\u0026ndash;2.8008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7898(1.388\u0026ndash;2.374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.321(1.651\u0026ndash;3.6856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCA125: cancer antigen 125, NLR: neutrophil to lymphocyte ratio, O_RADS: Ovarian-Adnexal Reporting and Data System, IQR: interquartile range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eMalignant ovarian masses of different pathological types.\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\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranulosa cell tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysgerminoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeratoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbryo sinus tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransitional cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\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\n\u003ch3\u003eBuilding a nomogram prediction model\u003c/h3\u003e\n\u003cp\u003eAccording to the ROC analysis, a cut-off value of 40.3 U/mL was determined for CA125, whereas a cut-off value of 2.159 was determined for NLR. Those with O-RADS scores of 0, 1 and 2 were divided into a group, and those with O-RADS scores of 3, 4 and 5 were divided into a group. The data were converted into binary variables for univariate and multivariate logistic regression analysis. In accordance with univariate and multivariate logistic regression analyses, CA125, NLR and O-RADS were independent risk factors of OC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subsequently, a nomogram integrating CA125, NLR and O-RADS was designed to visualise the model and effectively predict the probability risk of OC for patients with ovarian masses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The diagnostic equation was established as follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ln\\frac{p}{p(1-p)}=-3.1+3.21\\times\\:CA125+3.10\\times\\:O-RADS+0.96\\times\\:NLR\\)\u003c/span\u003e\u003c/span\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\u003eUnivariate and multivariate logistic regression analysis.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\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 \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\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\u003eCA125\u0026thinsp;\u0026gt;\u0026thinsp;cut-off (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.476(15.728,51.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.612(6.862,31.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO-RADS (1\u0026ndash;2,3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.068(25.801,117.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.202(14.764,83.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u0026thinsp;\u0026gt;\u0026thinsp;cut-off (2.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.772(1.768,4.345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.306 (1.086,4.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR: odds ratio, CA125: cancer antigen 125, NLR: neutrophil to lymphocyte ratio, O_RADS: Ovarian-Adnexal Reporting and Data System, CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLogit P=-4.208\u0026thinsp;+\u0026thinsp;3.561\u0026times;O-RADS\u0026thinsp;+\u0026thinsp;2.682\u0026times;CA125\u0026thinsp;+\u0026thinsp;0.835\u0026times;NLR\u003c/p\u003e \u003cp\u003eROC analysis indicated that the AUC of nomogram model was 0.942(95%CI, 0.917,0.968), with 97.800% sensitivity and 76.6% specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The AUC of CA125, NLR and O-RADS were performed as well, and the area under the ROC curve (AUC) was 0.840(95%CI, 0.793,0.887) with 80.90% sensitivity and 87.100% specificity, 0.624(95%CI, 0.563,0.685), with 58.100% sensitivity and 66.700% specificity and 0.865(95%CI, 0.824,0.906), with 93.400% sensitivity and 79.600% specificity respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The value of the nomogram model can be assessed using DCA, which exhibited a net benefit in clinical practice. These findings demonstrated that the model is a reliable tool for prediction of malignant tumours in patients with ovarian masses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The nomogram model was also well calibrated with a non-significant result (P\u0026thinsp;=\u0026thinsp;0.797) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Finally, we used the 10 fold cross validation method for internal validation and it got a good concordance of 0.919 for C-index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ROC analysis of CA125, NLR, O-RADS and the model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSP (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAUC: area under curve, SN: Sensitivity, SP: Specificity, CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe used this model to score two cases. Case 1 was pathologically diagnosed as high-grade serous carcinoma of the left ovary after operation in our hospital. Its ultrasound examination showed a 74*46mm hypoechoic mass with irregular shape, unclear boundary and abundant blood flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Nomogram showed that this case had a total of 175 points after summing all points (100\u0026thinsp;+\u0026thinsp;75\u0026thinsp;+\u0026thinsp;0), which corresponded to a 88.0% probability of ovarian cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Another case was pathologically diagnosed as multilocular cystic serous gonadal fibroma after surgery in our hospital. Its ultrasonic examination showed a mixed echo (mainly cystic) with regular shape, clear boundary and no blood flow signal (65*44mm in size) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Nomogram showed that this case had a total of 23 points after summing all points (0\u0026thinsp;+\u0026thinsp;0\u0026thinsp;+\u0026thinsp;23), which corresponded to a 4.0% probability of ovarian cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOC is often fatal because it has not been detected until the disease has progressed to advanced stages. Therefore, it is very urgent to distinguish symptoms of cancer from symptoms associated with benign masses at earlier stages. However, there is still a lack of widely available, cost-effective methods to predict the potential malignant ovarian masses. Herein, we present the first retrospective study documenting the diagnostic performance of a model that combined O-RADS and serum indicators in patients with ovarian masses. This model showed an area under the ROC curve (AUC) of 0.942 (95% confidence interval [CI], 0.917\u0026ndash;0.968), with 97.800% sensitivity and 76.600% specificity. Based on its higher or identical sensitivity compared to a separate indicator, this model can be used as an alternative method for the differential diagnosis of benign and malignant ovarian masses. Failure to detect malignant tumours in time during the examination is bound to reduce both the survival rate and quality of life of patients. Therefore, our model is crucial because it can distinguish between OC and benign tumours, which is essential for taking timely and effective measures.\u003c/p\u003e \u003cp\u003eBecause of the unsatisfied performance of a single parameter in distinguishing malignant ovarian masses, a variety of integrative models have been developed. Nearly 30 years ago, the Risk of Malignancy Index (RMI) was developed into a diagnostic model incorporating with CA125, imaging score and the menopausal status. The risk of ovarian malignancy algorithm (ROMA), integrating HE4, CA125 and the menopause status, indicating better performance than RMI for the prediction of malignant ovarian masses\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, some reports demonstrated that ROMA did not exhibit better performance than CA125 or HE4 alone\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Consequently, it is essential to develop a method to distinguish the OC from benign ovarian masses.\u003c/p\u003e \u003cp\u003ePrevious studies in this field have proposed that O-RADS has similar sensitivity with some scoring systems in preoperative differentiation of benign and malignant pelvic tumours\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Pi \u003cem\u003eet al.\u003c/em\u003e have pointed out that O-RADS has excellent specificity and specificity in experimental studies that evaluated benign and malignant ovarian masses\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In recent years, inflammation has been shown to be a major factor in the progress and spread of cancer in the human body\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The inflammatory response to tumour cells may bring about irreversible DNA damage by inhibiting OC cell apoptosis and triggering angiogenesis\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, it has been shown that inflammatory markers in the blood may achieve significant levels in various types of cancer\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Cho \u003cem\u003eet al\u003c/em\u003e found that the preoperative NLR of patients with OC was significantly higher than that of patients with benign ovarian masses\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In the past three decades, CA125 has become the most widely used biomarker of OC\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Moreover, CA125 concentrations are increased in many non-malignant conditions, affecting its specificity significantly. Thus, in contrast to only focusing on a single index, it is reasonable to construct a diagnostic model that incorporates several useful parameters to predict the malignancy of ovarian masses.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first retrospective study to construct a nomogram model by incorporating O-RADS, CA125 and NLR. This model yielded excellent performance, with an AUC of 0.942, sensitivity of 97.800% and specificity of 76.600%. In contrast, these values for CA125 alone were 0.840, 80.900% and 87.100%, respectively. Similarly, NLR alone exhibited an AUC of 0.624, and sensitivity and specificity values of 58.100% and 66.700%, respectively, and O-RADS showed an AUC of 0.865, sensitivity of 93.400% and specificity of 79.600%. These results suggest that the nomogram model proposed in our study has better performance than CA125, NLR, or O-RADS alone. In addition, the calibration curve showed that the nomogram has a good fitness and point We believe that the clinical application of this nomogram can play a crucial role in guiding decision-making processes in clinicians and avoiding unnecessary surgery in patients with benign ovarian masses. The strength of this study lies in its use of ultrasound technology and serum indexes for ovarian masses. The final results confirmed that our model could effectively combine O-RADS with serum indexes to distinguish malignant ovarian masses from benign group with good diagnostic efficiency.\u003c/p\u003e \u003cp\u003eOur study also has several limitations. First, this study is a retrospective study, thus a prospective study with a large sample size is necessary for the clinical application of our model as a screening tool for OC. Second, although the nomogram model has better performance, external validation is still required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we designed a diagnostic nomogram model to predict malignancy in patients with ovarian masses by integrating CA125, NLR and US O-RADS. This nomogram model has good diagnostic efficiency in distinguish benign and malignant ovarian masses. We would like to see more studies on the use of serum markers in combination with ultrasound, especially novel ultrasound techniques, to select for the diagnosis of female patients with OC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eO-RADS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian-Adnexal Reporting and Data System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCA125\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecancer antigen 125\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil to lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData curation, Yifan Gu,\u0026nbsp;Weiwei Chen,\u0026nbsp;Lei Yang\u0026nbsp;and Cheng Qian; Formal analysis, Xiaoyang Chen; Funding acquisition, Yifei Yin; Investigation, Yifei Yin; Methodology, Yifan Gu, Lingling Zhao,\u0026nbsp;Mengdan Li and Yifei Yang; Resources, Lingling Zhao,\u0026nbsp;Weiwei Chen\u0026nbsp;and Yifei Yin; Software, Lei Yang; Validation, Yifan Gu,\u0026nbsp;Cheng Qian, Mengdan Li and Yifei Yang; Writing \u0026ndash; original draft, Yifan Gu and Lingling Zhao; Writing \u0026ndash; review \u0026amp; editing, Xiaoyang Chen and Yifei Yin.\u003c/p\u003e\n\u003cp\u003eAll authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by\u0026nbsp;China Postdoctoral Science Foundation(2022M711721).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of\u0026nbsp;the Affiliated Hospital of XX University (2022-k101-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and the code used for the experiments are available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study participants for permitting us to use their personal data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTorre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics. CA Cancer J Clin. 2018;68(4):284\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoff BA, Mandel LS, Melancon CH, et al. Frequency of symptoms of ovarian cancer in women presenting to primary care clinics. JAMA. 2004;291(22):2705\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2020;70(1):7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasha MA, Metwally MI, Gamil SA, et al. Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses. Eur Radiol. 2021;31(2):674\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao L, Wei M, Liu Y, et al. Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses. Gynecol Oncol. 2021;162(1):107\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):168\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrachowski LM, Jha P, Chawla TP, et al. O-RADS for Ultrasound: A User's Guide, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol. 2021;216(5):1150\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNossov V, Amneus M, Su F, et al. The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol. 2008;199(3):215\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiem S, Schmid S, Krapf M, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe YF, Luo HQ, Wang W, et al. Preoperative NLR and PLR in the middle or lower ESCC patients with radical operation. Eur J Cancer Care. 2017;26(2):e12445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyamoto R, Inagawa S, Sano N, et al. The neutrophil-to-lymphocyte ratio (NLR) predicts short-term and long-term outcomes in gastric cancer patients. Ejso-Eur J Surg Onc. 2018;44(5):607\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu H, Yao X, Xie X, et al. Prognostic value of preoperative NLR, dNLR, PLR and CRP in surgical renal cell carcinoma patients. World J Urol. 2017;35(2):261\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzmen S, Timur O, Calik I, et al. Neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) may be superior to C-reactive protein (CRP) for predicting the occurrence of differentiated thyroid cancer. Endocr Regul. 2017;51(3):131\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho H, Hur HW, Kim SW, et al. Pre-treatment neutrophil to lymphocyte ratio is elevated in epithelial ovarian cancer and predicts survival after treatment. Cancer Immunol Immun. 2009;58(1):15\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEo WK, Kim KH, Park EJ, et al. Diagnostic accuracy of inflammatory markers for distinguishing malignant and benign ovarian masses. J Cancer. 2018;9(7):1165\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYildirim M, Demir Cendek B, Filiz Avsar A. Differentiation between benign and malignant ovarian masses in the preoperative period using neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios. Mol Clin Oncol. 2015;3(2):317\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakacak M, Serin S, Ercan O, et al. Utility of preoperative neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios to distinguish malignant from benign ovarian masses. J Turk Ger Gynecol Assoc. 2016;17(1):21\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Tian J, Zhang L, et al. Utility of Preoperative Inflammatory Markers to Distinguish Epithelial Ovarian Cancer from Benign Ovarian Masses. J Cancer. 2021;12(9):2687\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore RG, McMeekin DS, Brown AK, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112(1):40\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Gorp T, Cadron I, Despierre E, et al. HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm. Br J Cancer. 2011;104(5):863\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAslan K, Onan MA, Yilmaz C, et al. Comparison of HE 4, CA 125, ROMA score and ultrasound score in the differential diagnosis of ovarian masses. J Gynecol Obstet Hum Reprod. 2020;49(5):101713.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiett AK, Sonek J, Guy M, et al. Performance of IOTA Simple Rules, Simple Rules Risk assessment, ADNEX model and O-RADS in discriminating between benign and malignant adnexal lesions in North American population. Ultrasound Obstet Gynecol. 2021;59(5):668\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePi Y, Wilson MP, Katlariwala P, et al. Diagnostic accuracy and inter-observer reliability of the O-RADS scoring system among staff radiologists in a North American academic clinical setting. Abdom Radiol (NY). 2021;46(10):4967\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson JR, Seed MP, Kircher CH, et al. The codependence of angiogenesis and chronic inflammation. FASEB J. 1997;11(6):457\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCedres S, Torrejon D, Mart\u0026iacute;nez A, et al. Neutrophil to lymphocyte ratio (NLR) as an indicator of poor prognosis in stage IV non-small cell lung cancer. Clin Transl Oncol. 2012;14(11):864\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak M, Janas L, Stachowiak G, et al. Current clinical application of serum biomarkers to detect ovarian cancer. Prz Menopauzalny. 2015;14(4):254\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"O-RADS, CA-125, NLR, ovarian, nomogram model","lastPublishedDoi":"10.21203/rs.3.rs-5296411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5296411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo design a diagnostic nomogram model integrating the Ovarian-Adnexal Reporting and Data System (O-RADS) and serum indexes for predicting malignancy in patients with ovarian masses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a retrospective study including 201 benign ovarian masses patients and 136 ovarian cancer (OC) patients from June 2011 to April 2022. Before surgical resection, all patients underwent transvaginal ultrasound, as well as transabdominal ultrasound examination, and tumour parameters according to O-RADS (morphology, internal echo, blood supply, etc.) were assessed. Meanwhile, serum indexes, including cancer antigen 125 (CA125) and neutrophil to lymphocyte ratio (NLR), were tested. After surgical resection, all patients were pathologically diagnosed. The differences in serological indexes and O-RADS scores between the benign and malignant groups were analysed using the Mann-Whitney U test. ROC curves were firstly used to determine their optimal cut-off values. Univariate and multivariate logistic regression analyses were used to identify CA125, NLR and O-RADS for OC. Then, the prediction nomogram model was established. A decision curve analysis (DCA) was performed to assess the clinical net benefit of the model. The calibration curve and the Hosmer\u0026ndash;Lemeshow test were performed to assess the calibration and the goodness-of-fit of the nomogram model respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 337 women [median age:45(32\u0026ndash;57)] with 337 ovarian masses were included. Of the 337 ovarian masses, 201 were benign (benign group) and 136 were malignant (malignant group). CA125, NLR and O-RADS in the malignant group were significantly higher compared to the benign group. These parameters were then incorporated to develop a nomogram model, and this model showed an area under the ROC curve of 0.942 (95% confidence interval, 0.917\u0026ndash;0.968), with 97.800% sensitivity and 76.600% specificity. The calibration curve showed a good fitting degree. Meanwhile, DCA provided a net benefit for a range of threshold probabilities.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis nomogram model yielded a favourable diagnostic accuracy for predicting malignancy in patients with ovarian masses.\u003c/p\u003e","manuscriptTitle":"A diagnostic nomogram model integrating the O-RADS and serum indexes for predicting malignancy in patients with ovarian masses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 10:48:20","doi":"10.21203/rs.3.rs-5296411/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2eafe8fc-36f5-4286-b930-e889aec2004c","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-05T09:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-08 10:48:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5296411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5296411","identity":"rs-5296411","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}