Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data

• Main Authors: Ruitian Gao, Shuai Zhao, Kedeerya Aishanjiang, Hao Cai, Ting Wei, Yichi Zhang, Zhikun Liu, Jie Zhou, Bing Han, Jian Wang, Han Ding, Yingbin Liu, Xiao Xu, Zhangsheng Yu, Jinyang Gu
• Format: Article
• Language: English
• Published: BMC 2021-09-01
• Series: Journal of Hematology & Oncology
• Subjects: Artificial intelligence; Liver cancer; Contrast-enhanced CT; Computer-assisted diagnosis; Multimodal data
• Online Access: https://doi.org/10.1186/s13045-021-01167-2

Abstract Background Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients’ multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Methods Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model . Results The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors’ consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors’ consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model’s generalization ability. Conclusions We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.