Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.
妇科恶性肿瘤，尤其是淋巴结转移的诊断，即便采用CT、MRI和PET/CT等传统影像技术仍面临挑战。本研究旨在通过更全面、创新的方法探索并弥合这一诊断缺口。通过构建融合非影像数据与精细MRI图像分析的综合框架，本研究利用多模态联邦学习模型的能力，在联邦学习环境中采用复合神经网络，有效整合多源数据以提升预测准确性。同时，研究进一步引入基于增强型U-NET架构的深度卷积神经网络，实现对MRI图像的精细化处理。传统影像技术的灵敏度范围为32.63%至57.69%，而联邦学习模型在不包含影像数据时即达到约0.9231的灵敏度，融合MRI数据后更提升至0.9412。这些进展凸显了该方法的巨大潜力，表明联邦学习——特别是与MRI评估数据结合时——能够革新妇科恶性肿瘤淋巴结转移的检测方式。这为更精准的临床诊疗开辟了新路径，有望改变现有诊断模式并提升患者预后。
肿瘤(癌症)患者之家