Yanli Wang, 1, Weihong Lin, 2, Yifang He, 1 Dandan Wang, 1 Xiuming Wu, 3 Shaozheng He, 1 Min Gong, 4 Luhong Li, 2 Guorong Lyu1, 5 1Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People’s Republic of China; 2Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People’s Republic of China; 3Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, People’s Republic of China; 4Department of Ultrasound, Chengdu Third People’s Hospital, Chengdu, People’s Republic of China; 5Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, People’s Republic of ChinaThese authors contributed equally to this workCorrespondence: Guorong Lyu, Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, Fujian Province, 362000, People’s Republic of China, Email lgrfeus@sina. com Luhong Li Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, Fujian Province, 362000, People’s Republic of China, Email liluhong@sina. comPurpose: Ovarian cancer (OC) is a prevalent gynecological malignancy, often diagnosed at an advanced stage with extra-pelvic metastases. The accurate identification of advanced OC is essential for guiding appropriate treatment plans and influencing outcomes. The purpose of this research was to establish a preoperative nomogram integrating clinical and ultrasonic features to predict extra-pelvic metastasis, which may facilitate precise diagnosis and personalized treatment for OC patients. Patients and Methods: This retrospective study included 347 women with OC from three medical centers who had clear pathology and ultrasonic examination before surgery from March 2016 to July 2025. They were divided into two groups according to whether extra-pelvic metastasis occurred: group A without extra-pelvic metastasis (n=164) and group B with extra-pelvic metastasis (n=183). The total patient population was randomly split between a training set (70%) and a validation set (30%). Predictors were selected using LASSO, followed by univariate and multivariate logistic regression analyses. A predictive nomogram was established to predict the risk of extra-pelvic metastasis of OC. Results: Four independent risk factors ascites (OR 7. 07, 95% CI 3. 54– 14. 11, p< 0. 001), maximum tumor diameter (OR 1. 08, 95% CI 1. 01– 1. 15, p=0. 029), ill-defined boundary (OR 4. 20, 95% CI 2. 18– 8. 12, p< 0. 001), and blood flow score level 4 (OR 4. 69, 95% CI 1. 70– 12. 97, p=0. 003) were screened using LASSO and logistic regression, and a nomogram was established. The model demonstrated high discriminatory power, with an AUC of 0. 860 (95% CI: 0. 812– 0. 908) in the training set and 0. 865 (95% CI: 0. 798– 0. 932) in the validation set. The calibration curve and decision curve analysis curve show great performance. Conclusion: The developed nomogram, incorporating readily available clinical and ultrasonic features, provides a valuable tool for individualized prediction of extrapelvic metastasis in OC patients. It serves as a practical tool for preoperative risk stratification to guide clinical decision-making. Keywords: ovarian cancer, extra-pelvic metastasis, ultrasound, nomogram
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