用多期增强CT时序数据预测肠癌肝转移，模型准确率达AUC 0.79
MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time Series Multi-phase Contrast-Enhanced CT Scans
- 设计五维时序模型MPBD-LSTM，融合多平面与多期增强信息
- 在真实临床数据上实现AUC 0.79，优于现有四维模型
- 为肠癌随访中的早期肝转移筛查提供可落地的深度学习方案
结直肠癌是常见癌症类型，许多患者会发展为结直肠癌肝转移（CRLM）。早期检测对提高生存率至关重要。放射科医生通常依赖随访期间获取的多期增强计算机断层扫描（CECT）进行早期识别，这些扫描构成独特的五维数据（时间、分期及轴向、矢状、冠状三个平面的3D CT）。现有深度学习模型多能处理四维数据（如时序3D CT），但其扩展至第五维（分期）的效果尚不明确。本文构建了用于早期诊断CRLM的时间序列CECT数据集，并基于先进深度学习技术评估不同方法的预测效果。实验表明，基于3D双向LSTM的多平面架构——即MPBD-LSTM表现最佳，达到AUC 0.79。结果分析显示，仍有显著提升空间。
原文摘要 · Abstract (English)
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.
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