标题： 基于生成人工智能的多模态虚拟活检增强智能乳腺癌 显示英文标题

标题： Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

摘要： 全视野数字乳腺摄影（FFDM）是常规乳腺癌筛查的主要成像方式；然而，其在致密乳腺组织或纤维囊性病变患者中的效果受到限制。对比增强光谱乳腺摄影（CESM），一种二级成像技术，能提高肿瘤检测的准确性。然而，由于辐射暴露较高、使用对比剂以及可及性有限，其应用受到限制。因此，CESM通常仅用于特定病例，许多患者仍只能依赖FFDM，尽管CESM的诊断性能更优。虽然活检仍是确诊的金标准，但它是一种侵入性操作，可能给患者带来不适。我们引入了一种多模态、多视角的深度学习方法进行虚拟活检，将FFDM和CESM模态在头尾位和内外侧斜位进行整合，以将病灶分类为恶性或良性。为解决CESM数据缺失的挑战，我们利用生成式人工智能从FFDM扫描中填补CESM图像。实验结果表明，结合CESM模态对于提高虚拟活检的性能至关重要。当真实CESM数据缺失时，合成的CESM图像表现有效，优于单独使用FFDM，特别是在结合FFDM和CESM模态的多模态配置中。所提出的方法有潜力改善诊断流程，为临床医生提供增强智能工具，以提高诊断准确性和患者护理。此外，作为对研究社区的贡献，我们公开发布了实验中使用的数据集，促进该领域的进一步发展。 显示英文摘要

摘要： Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.