PDF(Pubmed)
Abstract：
Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian-Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.
摘要：
卵巢癌,一组异质性疾病，具有广泛的特征，在妇科恶性肿瘤中死亡率最高。准确、早期诊断卵巢癌具有重要意义。这里,我们介绍OvcaFinder,从基于超声图像的深度学习（DL）预测构建的可解释模型，放射科医生的卵巢附件报告和数据系统评分，和常规临床变量。OvcaFinder优于临床模型和DL模型，在内部和外部测试数据集中，曲线下面积（AUC）为0.978和0.947，分别。OvcaFinder的协助改善了放射科医师的AUC和读者之间的协议。平均AUC从0.927提高到0.977，从0.904提高到0.941，内部和外部测试数据集中的假阳性率分别降低了13.4%和8.3%，分别。这凸显了OvcaFinder提高诊断准确性的潜力，以及放射科医生在识别卵巢癌方面的一致性。
