Moro Francesca, Ciancia Marianna, Sciuto Maria, Baldassari Giulia, Tran Huong Elena, Carcagnì Antonella, Fagotti Anna, Testa Antonia Carla
UniCamillus-International Medical University, Rome, Italy.
Department of Women's, Child and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
Acta Obstet Gynecol Scand. 2025 May 1. doi: 10.1111/aogs.15146.
We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze their accuracy in differentiating between benign and malignant adnexal masses.
Web of Science, PubMed, and Scopus databases were searched. All studies were imported into RAYYAN QCRI software. All studies that developed and internally or externally validated ML models using only radiomics features extracted from ultrasound images were included. The overall quality of the included studies was assessed using the QUADAS-AI tool. Summary sensitivity and specificity analyses with corresponding 95% confidence intervals (CIs) were reported.
12 studies developed ML models including only radiomics features extracted from ultrasound images, and six of them were included in the meta-analysis. The overall sensitivity and specificity for differentiating benign from malignant adnexal masses were 0.80 (95% CI 0.74-0.87) and 0.86 (95% CI 0.80-0.90), respectively, in the validation set. All studies demonstrated a high risk of bias in subject selection (e.g., lack of details on image sources or scanner models; absence of image preprocessing), and the majority also showed a high risk in the index test (e.g., models were not validated on external datasets) domain. In contrast, the risk of bias was generally low for the reference standard (i.e., most studies used a reference that accurately identified the target condition) and the testing workflow (i.e., the time interval between the index test and reference standard was appropriate) domains.
The good performance of ultrasound-based radiomics models in the validation set supports that radiomics is worth exploring to improve the diagnosis of adnexal masses. So far, the studies have a high risk of bias due to the small sample size, single-setting design, and no external validation included.
我们介绍了基于超声的机器学习（ML）放射组学模型在卵巢肿块方面的最新进展，并分析了它们在鉴别良性和恶性附件肿块方面的准确性。
检索了科学网、PubMed和Scopus数据库。所有研究均导入RAYYAN QCRI软件。纳入所有仅使用从超声图像中提取的放射组学特征开发并进行内部或外部验证的ML模型的研究。使用QUADAS-AI工具评估纳入研究的整体质量。报告了汇总敏感性和特异性分析以及相应的95%置信区间（CI）。
12项研究开发了仅包含从超声图像中提取的放射组学特征的ML模型，其中6项纳入了荟萃分析。在验证集中，鉴别良性与恶性附件肿块的总体敏感性和特异性分别为0.80（95%CI 0.74-0.87）和0.86（95%CI 0.80-0.90）。所有研究在受试者选择方面均显示出高偏倚风险（例如，图像来源或扫描仪型号缺乏详细信息；未进行图像预处理），并且大多数研究在指标测试（例如，模型未在外部数据集上进行验证）领域也显示出高偏倚风险。相比之下，参考标准（即大多数研究使用准确识别目标疾病的参考）和测试工作流程（即指标测试与参考标准之间的时间间隔合适）领域的偏倚风险通常较低。
基于超声的放射组学模型在验证集中表现良好，支持放射组学在改善附件肿块诊断方面值得探索。到目前为止，由于样本量小、单中心设计且未纳入外部验证，这些研究存在较高的偏倚风险。
