A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

作者：Shixin Huang, Xixi Nie, Kexue Pu, Xiaoyu Wan, Jiawei Luo · 发表于：Journal of Cancer Research and Clinical Oncology · 年份：2024 · DOI：10.1007/s00432-024-05977-y · 被引用次数：14 · 研究领域：Advanced X-ray and CT Imaging、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

BACKGROUND: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals. METHODS: We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model's robustness. RESULTS: The H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9. CONCLUSION: The proposed H-LSTM model can be...