文章：

OTC-NET：一种用于O-RADS 4类肿块中卵巢癌精准诊断的多模态方法

OTC-NET: A Multimodal Method for Accurate Diagnosis of Ovarian Cancer in O-RADS Category 4 Masses

原文发布日期：28 October 2025

DOI: 10.3390/cancers17213466

类型: Article

开放获取: 是

英文摘要：

Background: Ovarian cancer is the deadliest female reproductive malignancy. Accurate preoperative differentiation of benign and malignant ovarian masses is critical for appropriate treatment. O-RADS category 4 lesions present a wide range of malignant risk, challenging radiologists. Ultrasonic images are the primary focus of current deep learning models, with no consideration for clinical data. Methods: We proposed OTC-NET, a model that uses multimodal data for classification, which combines ultrasound images and clinical information to improve the classification ability of O-RADS 4 ovarian masses. Results: OTC-NET outperforms seven deep learning models and three radiologists of varying experience, with AUC significantly higher than junior (p< 0.001), intermediate (p< 0.01), and senior (p< 0.05) radiologists. Additionally, OTC-NET–assisted diagnosis notably improves AUC and accuracy of junior and senior radiologists (p< 0.05). Conclusions: These results indicate that OTC-NET provides superior diagnostic accuracy and has strong potential for clinical application.

摘要翻译： 

背景：卵巢癌是致死率最高的女性生殖系统恶性肿瘤。术前准确鉴别卵巢肿块良恶性对制定适宜治疗方案至关重要。O-RADS 4类病灶恶性风险跨度大，给放射科医师带来诊断挑战。现有深度学习模型主要聚焦超声图像，未纳入临床数据。方法：我们提出OTC-NET多模态分类模型，通过融合超声图像与临床信息提升O-RADS 4类卵巢肿块的分类能力。结果：OTC-NET在七种深度学习模型及三位不同资历放射科医师的对比评估中表现最优，其AUC值显著高于初级（p<0.001）、中级（p<0.01）及高级医师（p<0.05）。此外，OTC-NET辅助诊断显著提升了初级与高级医师的AUC值及准确率（p<0.05）。结论：研究表明OTC-NET具有更优的诊断准确性，展现出显著的临床应用潜力。

原文链接：

OTC-NET: A Multimodal Method for Accurate Diagnosis of Ovarian Cancer in O-RADS Category 4 Masses