Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025
Cesarean Scar Pregnancy Prognostic Classification System Based on Machine-Learning and Traditional Linear Scoring Models.
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Wang Xin, Ma Liyuan, Peng Siting, Yang Yuqing, Wu Jihao, Su Na, Qi Zhenhong, Liu Xinyan, Dai Qing, Li Jianchu, Liu Zhenzhen
Аннотация
OBJECTIVES: Cesarean scar pregnancy (CSP) refers to a special type of pregnancy with a variable prognosis. We aimed to establish a prognostic classification system using ultrasound and clinical features to provide a reference for management strategies.
METHODS: Exactly 230 patients with CSP were included and categorized into three groups based on treatment and prognosis: Group A (favorable prognosis), Group B (moderate prognosis), and Group C (poor prognosis). A total of 26 ultrasound features and 8 clinical features were collected for further analysis. Machine learning and traditional scoring models were then constructed for Group A and Group C and integrated to predict CSP prognosis using the significant features.
RESULTS: In the univariate analysis, 26 variables were significantly correlated with Group C, while 21 variables were significantly correlated with Group A. For Group C, a linear scoring model was established using three key features: the criteria length of the implantation portion (IMPL) ≥2.43 cm, the height of the gestational sac or mass protruding above the uterine cavity line (GSUCL) ≥1.4 cm, and absent residual myometrial thickness (RMT), achieving an area under the curve (AUC) of 0.939 (0.872, 1.000), which demonstrated comparable performance to the machine learning model (P = .814). For Group A, 13 significant univariate variables were utilized to construct the machine learning model with an AUC of 0.917 (0.842, 0.993).
CONCLUSION: Multiple features were associated with CSP prognosis, such as GSUCL, IMPL, RMT, and the anterior-posterior diameter of the gestational sac at the level of the niche (GSSH). The CSP prognostic prediction can be achieved by integrating machine learning and linear scoring models to balance performance and interpretability, which can assist clinicians in treatment decisions.
METHODS: Exactly 230 patients with CSP were included and categorized into three groups based on treatment and prognosis: Group A (favorable prognosis), Group B (moderate prognosis), and Group C (poor prognosis). A total of 26 ultrasound features and 8 clinical features were collected for further analysis. Machine learning and traditional scoring models were then constructed for Group A and Group C and integrated to predict CSP prognosis using the significant features.
RESULTS: In the univariate analysis, 26 variables were significantly correlated with Group C, while 21 variables were significantly correlated with Group A. For Group C, a linear scoring model was established using three key features: the criteria length of the implantation portion (IMPL) ≥2.43 cm, the height of the gestational sac or mass protruding above the uterine cavity line (GSUCL) ≥1.4 cm, and absent residual myometrial thickness (RMT), achieving an area under the curve (AUC) of 0.939 (0.872, 1.000), which demonstrated comparable performance to the machine learning model (P = .814). For Group A, 13 significant univariate variables were utilized to construct the machine learning model with an AUC of 0.917 (0.842, 0.993).
CONCLUSION: Multiple features were associated with CSP prognosis, such as GSUCL, IMPL, RMT, and the anterior-posterior diameter of the gestational sac at the level of the niche (GSSH). The CSP prognostic prediction can be achieved by integrating machine learning and linear scoring models to balance performance and interpretability, which can assist clinicians in treatment decisions.