The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients

He Q, Zhang C, Hu Y, Deng J, Zhang S
2025 · W6982400185
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This retrospective study evaluated whether an improved endometriosis fertility index (EFI) prediction model could forecast natural pregnancy outcomes in 496 patients undergoing their first laparoscopic surgery for infertility at Jingzhou Central Hospital (2016–2023). Using multi-omics data collected at initial admission, the authors built a machine-learning nomogram integrating five ultrasound radiomics parameters and three urinary proteomics markers, and compared it with the traditional EFI model using C-index, calibration, AUC, and decision curve analysis. The improved radiomics–urine proteomics model showed higher discrimination than the classical EFI, with AUCs of 0.921 (training) and 0.909 (validation) versus 0.889 and 0.873, and demonstrated better net benefit on decision curve analysis. This paper is centrally about endometriosis—development and validation of the Improved-EFI multi-omics predictive tool for natural fertility after first laparoscopic surgery in endometriosis patients.

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Abstract

Qiumin He,1,* Chongyuan Zhang,2,* Yao Hu,1 Jinfang Deng,1 Shuirong Zhang1 1Department of Gynaecology,Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, 434020, People’s Republic of China; 2Department of Gynaecology,Jingzhou Maternal and Child Health Hospital, Jingzhou, Hubei, 434000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuirong Zhang, Email [email protected]: Infertility caused by endometriosis (EM) directly affects the possibility of pregnancy in women of gestational age. This study aims to establish a prediction model to accurately predict the natural pregnancy outcome of patients with EM, providing valuable information for clinical decision-making.Methods: We retrospectively selected a total of 496 patients who underwent their first laparoscopic surgery for infertility at the Obstetrics and Gynecology Department of Jingzhou Central Hospital from January 2016 to June 2023. An improved endometriosis fertility index (EFI) predictive model was created based on ultrasound radiomics and urinary proteomics gathered during the patient’s initial admission, using two machine learning algorithms. The predictive model was evaluated for C-index, calibration, and clinical applicability through receiver working characteristic curve, decision curve analysis.Results: The improved EFI prediction model nomogram, based on five ultrasound radiomics parameters and three urine proteomics, had AUC values of 0.921 (95% CI: 0.864– 0.978) and 0.909 (95% CI: 0.852– 0.966) in the training and validation sets, respectively, while the traditional EFI prediction model had AUC values of 0.889 (95% CI: 0.832– 0.946) and 0.873 (95% CI: 0.816– 0.930) in the training and validation sets, respectively. Additionally, the nomogram exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the EFI model and decision tree in the decision curve analysis (DCA).Conclusion: The combined ultrasound radiomics–urine proteomics model was better able to predict natural pregnancy-associated patients with EM compared to the classical EFI score. This can help clinicians better predict an individual patient’s risk of natural pregnancy following a first-ever laparoscopic surgery and facilitate earlier diagnosis and treatment.Keywords: endometriosis, infertility, Endometriosis Fertility Index, radiomics, uromics, prediction
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International Journal of General Medicine (Feb 2025) The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients Abstract Qiumin He,1,* Chongyuan Zhang,2,* Yao Hu,1 Jinfang Deng,1 Shuirong Zhang1 1Department of Gynaecology,Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, 434020, People’s Republic of China; 2Department of Gynaecology,Jingzhou Maternal and Child Health Hospital, Jingzhou, Hubei, 434000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuirong Zhang, Email [email protected]: Infertility caused by endometriosis (EM) directly affects the possibility of pregnancy in women of gestational age. This study aims to establish a prediction model to accurately predict the natural pregnancy outcome of patients with EM, providing valuable information for clinical decision-making.Methods: We retrospectively selected a total of 496 patients who underwent their first laparoscopic surgery for infertility at the Obstetrics and Gynecology Department of Jingzhou Central Hospital from January 2016 to June 2023. An improved endometriosis fertility index (EFI) predictive model was created based on ultrasound radiomics and urinary proteomics gathered during the patient’s initial admission, using two machine learning algorithms. The predictive model was evaluated for C-index, calibration, and clinical applicability through receiver working characteristic curve, decision curve analysis.Results: The improved EFI prediction model nomogram, based on five ultrasound radiomics parameters and three urine proteomics, had AUC values of 0.921 (95% CI: 0.864– 0.978) and 0.909 (95% CI: 0.852– 0.966) in the training and validation sets, respectively, while the traditional EFI prediction model had AUC values of 0.889 (95% CI: 0.832– 0.946) and 0.873 (95% CI: 0.816– 0.930) in the training and validation sets, respectively. Additionally, the nomogram exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the EFI model and decision tree in the decision curve analysis (DCA).Conclusion: The combined ultrasound radiomics–urine proteomics model was better able to predict natural pregnancy-associated patients with EM compared to the classical EFI score. This can help clinicians better predict an individual patient’s risk of natural pregnancy following a first-ever laparoscopic surgery and facilitate earlier diagnosis and treatment.Keywords: endometriosis, infertility, Endometriosis Fertility Index, radiomics, uromics, prediction

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