利用CT图像提取的放射组学和深度学习（DL）特征开发出一种人工智能（AI）模型来区分良性和恶性卵巢肿瘤。我们招募了149名病理学确认的卵巢肿瘤患者，共包括185个肿瘤，并按7:3的比例将其分为训练组和测试组。所有肿瘤都是手动从术前增强CT图像中分割出来的。使用放射组学和DL提取CT图像特征。我们构建了五种不同特征组合的模型。使用机器学习（ML）分类器对良性和恶性肿瘤进行分类。将模型与测试集上的五名放射科医生进行比较。在五个模型中，表现最佳的是使用放射组学，DL和临床特征组合的集合模型。该模型的准确度为82％，特异度为89％，敏感度为68％。与初级放射科医生的平均结果相比，该模型准确度（82％ vs 66％）和特异度（89％ vs 65％）更高，敏感度（68％ vs 67％）相当。在模型的帮助下，初级放射科医生的平均准确度（81％ vs 66％）、特异度（80％ vs 65％）和敏感度（82％ vs 67％）都更高，接近于资深放射科医生的表现。我们开发了一种基于CT的AI模型，可以高精度地区分良性和恶性卵巢肿瘤。该模型显著提高了经验不足的放射科医生在卵巢肿瘤评估方面的表现，并可能指导妇科医生为这些患者提供更好的治疗策略。©2023。作者（们）。

To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.© 2023. The Author(s).