MPBD-LSTM：一种基于时间序列多期增强计算机断层扫描的结直肠癌肝转移预测模型
MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time Series Multi-phase Contrast-Enhanced CT Scans
AI总结：
针对结直肠癌肝转移（CRLM）早期检测需求，构建时间序列多期增强CT数据集，提出基于3D双向LSTM的多平面架构MPBD-LSTM，实现0.79的AUC，为CRLM预测提供有效模型。
摘要：
结直肠癌是一种常见的癌症类型，许多患者会因此发展为结直肠癌肝转移（CRLM）。CRLM的早期检测对于提高生存率至关重要。放射科医生通常依靠随访期间进行的一系列多期增强计算机断层扫描（CECT）来早期检测潜在的CRLM。这些扫描形成独特的五维数据（时间、期相以及3D CT的轴位、矢状位和冠状位平面）。大多数现有深度学习模型可轻松处理四维数据（例如时间序列3D CT图像），但尚不清楚它们扩展处理额外期相维度的效果如何。在本文中，我们构建了一个时间序列CECT扫描数据集以辅助CRLM的早期诊断，并基于最先进的深度学习技术评估如何最佳预测CRLM。我们的实验结果表明，基于3D双向LSTM的多平面架构（我们称之为MPBD-LSTM）效果最佳，实现了0.79的曲线下面积（AUC）。另一方面，结果分析显示仍有很大的进一步改进空间。
英文摘要：
Colorectal cancer is a prevalent form of cancer, and many patients develop colorectal cancer liver metastasis (CRLM) as a result. Early detection of CRLM is critical for improving survival rates. Radiologists usually rely on a series of multi-phase contrast-enhanced computed tomography (CECT) scans done during follow-up visits to perform early detection of the potential CRLM. These scans form unique five-dimensional data (time, phase, and axial, sagittal, and coronal planes in 3D CT). Most of the existing deep learning models can readily handle four-dimensional data (e.g., time-series 3D CT images) and it is not clear how well they can be extended to handle the additional dimension of phase. In this paper, we build a dataset of time-series CECT scans to aid in the early diagnosis of CRLM, and build upon state-of-the-art deep learning techniques to evaluate how to best predict CRLM. Our experimental results show that a multi-plane architecture based on 3D bi-directional LSTM, which we call MPBD-LSTM, works best, achieving an area under curve (AUC) of 0.79. On the other hand, analysis of the results shows that there is still great room for further improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
