肿瘤生长模式的准确定量可以指示疾病的发展过程。根据肿瘤生长速率和扩张的重要特征，医生可以更高效地干预和诊断患者，提高治愈率。然而，现有的纵向生长模型无法很好地分析长时空中肿瘤生长像素之间的依赖关系，并且不能有效拟合肿瘤的非线性生长规律。因此，我们提出了基于时空特征的ConvLSTM协调纵向Transformer（LCTformer）用于肿瘤生长预测。我们设计了自适应边缘增强模块（AEEM）来学习不同大小肿瘤在时间序列下的静态空间特征，并使得深度模型更加关注肿瘤边缘区域。此外，我们提出了生长预测模块（GPM）来描述肿瘤的未来生长趋势。它由纵向Transformer和ConvLSTM组成。基于当前肿瘤的自适应抽象特征，纵向Transformer探索时空CT序列之间的动态生长模式，并在并行计算中学习肿瘤未来的形态特征，同时考虑残差信息和序列运动关系。ConvLSTM可以更好地学习目标肿瘤的位置信息，并与纵向Transformer相辅相成，共同预测肿瘤的未来成像，以减少生长信息的损失。最后，通道增强融合模块（CEFM）在通道和空间维度上密集融合生成的肿瘤特征图像，实现对整个肿瘤生长过程的精确量化。我们的模型严格训练和测试在NLST数据集上。平均预测准确率达到88.52％（Dice得分），89.64％（召回率）和11.06（RMSE），可以提高医生的工作效率。版权所有©2023 Elsevier Ltd.版权所有。

Accurate quantification of tumor growth patterns can indicate the development process of the disease. According to the important features of tumor growth rate and expansion, physicians can intervene and diagnose patients more efficiently to improve the cure rate. However, the existing longitudinal growth model can not well analyze the dependence between tumor growth pixels in the long space-time, and fail to effectively fit the nonlinear growth law of tumors. So, we propose the ConvLSTM coordinated longitudinal Transformer (LCTformer) under spatiotemporal features for tumor growth prediction. We design the Adaptive Edge Enhancement Module (AEEM) to learn static spatial features of different size tumors under time series and make the depth model more focused on tumor edge regions. In addition, we propose the Growth Prediction Module (GPM) to characterize the future growth trend of tumors. It consists of a Longitudinal Transformer and ConvLSTM. Based on the adaptive abstract features of current tumors, Longitudinal Transformer explores the dynamic growth patterns between spatiotemporal CT sequences and learns the future morphological features of tumors under the dual views of residual information and sequence motion relationship in parallel. ConvLSTM can better learn the location information of target tumors, and it complements Longitudinal Transformer to jointly predict future imaging of tumors to reduce the loss of growth information. Finally, Channel Enhancement Fusion Module (CEFM) performs the dense fusion of the generated tumor feature images in the channel and spatial dimensions and realizes accurate quantification of the whole tumor growth process. Our model has been strictly trained and tested on the NLST dataset. The average prediction accuracy can reach 88.52% (Dice score), 89.64% (Recall), and 11.06 (RMSE), which can improve the work efficiency of doctors.Copyright © 2023 Elsevier Ltd. All rights reserved.