Lin Cuiyan, Chen Wanming, Lai Jichuang, Huang Jieyi, Ye Xiaolu, Chen Sijia, Guo Xinmin, Yang Yichun
Department of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, China.
Department of Ultrasound, The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Front Oncol. 2025 Jul 8;15:1600242. doi: 10.3389/fonc.2025.1600242. eCollection 2025.
Endometrial cancer (EC) is one of the most prevalent malignancies affecting the female reproductive system. It poses significant health risks to women and imposes a substantial economic burden on healthcare systems. Early and accurate diagnosis is critical for improving patient outcomes. While traditional diagnostic methods rely on clinical evaluation and imaging, there is growing interest in leveraging artificial intelligence, particularly deep learning (DL), to enhance diagnostic accuracy.
This study developed a DL-based predictive model integrating multimodal ultrasound features and clinical risk factors to improve early EC diagnosis. A retrospective, multicenter analysis was conducted using 1,443 multimodal ultrasound images-including two-dimensional (2D) and color Doppler images-from 611 patients, of whom 132 were diagnosed with EC and 479 were non-EC cases. Clinical risk factors such as body mass index (BMI), menopausal status, irregular vaginal bleeding, and hypertension were identified as significant predictors (P < 0.05) and incorporated into a clinical model. Separate DL models were trained on 2D and color Doppler ultrasound images, and their performance was evaluated individually and in combination with the clinical model.
The area under the receiver operating characteristic curve (AUC) for the clinical model was 0.772 (95% CI: 0.690-0.854). The 2D and color Doppler DL models achieved AUCs of 0.792 (95% CI: 0.719-0.864) and 0.813 (95% CI: 0.745-0.881), respectively. When combined with the clinical model, the merged model demonstrated superior predictive performance. In the external validation cohort, the merged model achieved an AUC of 0.892 (95% CI: 0.846-0.938), indicating high diagnostic accuracy.
The integration of multimodal ultrasound imaging and clinical risk factors using DL significantly enhances the accuracy of endometrial cancer diagnosis. The merged model demonstrated strong generalizability in external validation, underscoring its potential clinical utility. Future studies should focus on larger, prospective multicenter trials to further validate these findings and explore the implementation of this approach in personalized patient care.
子宫内膜癌（EC）是影响女性生殖系统的最常见恶性肿瘤之一。它给女性带来重大健康风险，并给医疗保健系统带来沉重经济负担。早期准确诊断对于改善患者预后至关重要。虽然传统诊断方法依赖临床评估和影像学检查，但利用人工智能，尤其是深度学习（DL）来提高诊断准确性的兴趣与日俱增。
本研究开发了一种基于深度学习的预测模型，该模型整合了多模态超声特征和临床风险因素，以改善早期子宫内膜癌诊断。使用来自611例患者的1443幅多模态超声图像（包括二维（2D）和彩色多普勒图像）进行了一项回顾性多中心分析，其中132例被诊断为子宫内膜癌，479例为非子宫内膜癌病例。体重指数（BMI）、绝经状态、不规则阴道出血和高血压等临床风险因素被确定为显著预测因素（P<0.05），并纳入临床模型。分别在二维和彩色多普勒超声图像上训练独立的深度学习模型，并分别评估其性能，以及与临床模型结合后的性能。
临床模型的受试者操作特征曲线（AUC）下面积为0.772（95%CI：0.690-0.854）。二维和彩色多普勒深度学习模型的AUC分别为0.792（95%CI：0.719-0.864）和0.813（95%CI：0.745-0.881）。与临床模型结合时，合并模型表现出卓越的预测性能。在外部验证队列中，合并模型的AUC为0.892（95%CI：0.846-0.938），表明诊断准确性高。
使用深度学习整合多模态超声成像和临床风险因素可显著提高子宫内膜癌诊断的准确性。合并模型在外部验证中表现出很强的通用性，突出了其潜在的临床实用性。未来研究应聚焦于更大规模的前瞻性多中心试验，以进一步验证这些发现，并探索该方法在个性化患者护理中的应用。
