Cesarean Scar Pregnancy Prognostic Classification System Based on Machine-Learning and Traditional Linear Scoring Models.

IF 2.1 4区 医学 Q2 ACOUSTICS
Xin Wang, Liyuan Ma, Siting Peng, Yuqing Yang, Jihao Wu, Na Su, Zhenhong Qi, Xinyan Liu, Qing Dai, Jianchu Li, Zhenzhen Liu
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Abstract

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.

基于机器学习和传统线性评分模型的剖宫产疤痕妊娠预后分类系统。
目的:剖宫产瘢痕妊娠(CSP)是一种特殊类型的妊娠,预后多变。我们旨在建立超声及临床特征的预后分类体系,为治疗策略提供参考。方法:选取230例CSP患者,根据治疗和预后分为A组(预后良好)、B组(预后中等)和C组(预后差)。收集26个超声特征和8个临床特征作进一步分析。然后为A组和C组构建机器学习和传统评分模型,并结合显著特征预测CSP预后。结果:在单因素分析中,26个变量与C组显著相关,21个变量与a组显著相关。对于C组,采用三个关键特征建立线性评分模型:标准着床部分长度(IMPL)≥2.43 cm,妊娠囊或肿块突出子宫腔线以上高度(GSUCL)≥1.4 cm,无残留肌层厚度(RMT),曲线下面积(AUC)为0.939(0.872,1.000),与机器学习模型性能相当(P = .814)。A组采用13个显著单变量构建机器学习模型,AUC为0.917(0.842,0.993)。结论:GSUCL、IMPL、RMT、胎位水平孕囊前后直径(GSSH)与CSP预后相关。CSP预后预测可以通过整合机器学习和线性评分模型来实现,以平衡性能和可解释性,这可以帮助临床医生做出治疗决策。
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来源期刊
CiteScore
5.10
自引率
4.30%
发文量
205
审稿时长
1.5 months
期刊介绍: The Journal of Ultrasound in Medicine (JUM) is dedicated to the rapid, accurate publication of original articles dealing with all aspects of medical ultrasound, particularly its direct application to patient care but also relevant basic science, advances in instrumentation, and biological effects. The journal is an official publication of the American Institute of Ultrasound in Medicine and publishes articles in a variety of categories, including Original Research papers, Review Articles, Pictorial Essays, Technical Innovations, Case Series, Letters to the Editor, and more, from an international bevy of countries in a continual effort to showcase and promote advances in the ultrasound community. Represented through these efforts are a wide variety of disciplines of ultrasound, including, but not limited to: -Basic Science- Breast Ultrasound- Contrast-Enhanced Ultrasound- Dermatology- Echocardiography- Elastography- Emergency Medicine- Fetal Echocardiography- Gastrointestinal Ultrasound- General and Abdominal Ultrasound- Genitourinary Ultrasound- Gynecologic Ultrasound- Head and Neck Ultrasound- High Frequency Clinical and Preclinical Imaging- Interventional-Intraoperative Ultrasound- Musculoskeletal Ultrasound- Neurosonology- Obstetric Ultrasound- Ophthalmologic Ultrasound- Pediatric Ultrasound- Point-of-Care Ultrasound- Public Policy- Superficial Structures- Therapeutic Ultrasound- Ultrasound Education- Ultrasound in Global Health- Urologic Ultrasound- Vascular Ultrasound
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