■准确和快速区分良性和恶性卵巢肿块对于优化患者管理至关重要。本研究旨在建立基于超声图像的列线图,影像组学,和深度迁移学习功能,根据卵巢附件报告和数据系统(O-RADS)自动将卵巢肿块分为低风险和中高风险的恶性肿瘤病变。
■纳入1,080例患者的超声图像,其中1,080例卵巢肿块。由深圳大学华南医院683名患者组成的培训队列。在深圳大学总医院收集了由397名患者组成的测试队列。工作流程包括图像分割,特征提取,特征选择,和模型建设。
■预训练的Resnet-101模型实现了最佳性能。在不同的单模态特征和融合特征模型中,列线图达到了最高水平的诊断性能(AUC:0.930,准确性:84.9%,灵敏度:93.5%,特异性:81.7%,PPV:65.4%,净现值:97.1%,精度:65.4%)。列线图的诊断指标高于初级放射科医生,在该模型的帮助下,初级放射科医生的诊断指标显着提高。校准曲线显示,列线图的预测与卵巢肿块的实际分类之间具有良好的一致性。决策曲线分析表明,列线图在临床上有用。
■与初级放射科医生相比,该模型表现出令人满意的诊断性能。它有可能提高初级放射科医生的专业知识水平,并为卵巢癌筛查提供快速有效的方法。
UNASSIGNED: Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS).
UNASSIGNED: The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction.
UNASSIGNED: The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful.
UNASSIGNED: This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.