标题： CSF-Net：用于肺结节恶性预测的跨模态时空融合网络 显示英文标题

标题： CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting

摘要： 肺结节是肺癌的早期征兆，及早检测它们对于提高患者的生存率至关重要。 目前大多数方法仅使用单个计算机断层扫描（CT）图像来评估结节的恶性程度。然而，在临床实践中，医生通常通过整合随访的CT扫描与临床数据来进行综合评估。 为了改进这一过程，我们的研究引入了一种跨模态时空融合网络，名为CSF-Net，旨在利用随访的CT扫描预测肺结节的恶性程度。这种方法模拟了临床医生结合随访影像与临床信息的决策过程。 CSF-Net由三个关键组件组成：空间特征提取模块、时间残差融合模块和跨模态注意力融合模块。这些模块共同实现了对结节恶性程度的精确预测。 此外，我们利用公开可用的NLST数据集筛选并标注了肺结节的具体位置，并创建了一个新的数据集，名为NLST-cmst。 我们在NLST-cmst数据集上的实验结果显示了显著的性能提升，准确率为0.8974，精确度为0.8235，F1得分为0.8750，AUC为0.9389，召回率为0.9333。 这些结果表明，我们的多模态时空融合方法，通过结合随访数据与临床信息，超越了现有方法，突显了其在预测结节恶性程度方面的有效性。 显示英文摘要

摘要： Pulmonary nodules are an early sign of lung cancer, and detecting them early is vital for improving patient survival rates. Most current methods use only single Computed Tomography (CT) images to assess nodule malignancy. However, doctors typically make a comprehensive assessment in clinical practice by integrating follow-up CT scans with clinical data. To enhance this process, our study introduces a Cross-Modal Spatiotemporal Fusion Network, named CSF-Net, designed to predict the malignancy of pulmonary nodules using follow-up CT scans. This approach simulates the decision-making process of clinicians who combine follow-up imaging with clinical information. CSF-Net comprises three key components: spatial feature extraction module, temporal residual fusion module, and cross-modal attention fusion module. Together, these modules enable precise predictions of nodule malignancy. Additionally, we utilized the publicly available NLST dataset to screen and annotate the specific locations of pulmonary nodules and created a new dataset named NLST-cmst. Our experimental results on the NLST-cmst dataset demonstrate significant performance improvements, with an accuracy of 0.8974, a precision of 0.8235, an F1 score of 0.8750, an AUC of 0.9389, and a recall of 0.9333. These findings indicate that our multimodal spatiotemporal fusion approach, which combines follow-up data with clinical information, surpasses existing methods, underscoring its effectiveness in predicting nodule malignancy.