MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time Series Multi-phase Contrast-Enhanced CT Scans
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
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.
