A Multi-phase CT Dataset for Automated Differential Diagnosis of Liver Tumors

Liver tumors exhibit significant heterogeneity in etiology, pathology, and treatment response, making accurate differential diagnosis critical for diagnosis and management. While multi-phase contrast-enhanced computed tomography (CT) provides valuable imaging patterns for differentiation, visual assessment alone is often limited by overlapping features. To address this, we present MCT-LTDiag, a comprehensive Multi-phase CT dataset for Liver Tumor Diagnosis, comprising 517 cases with four-phase contrast-enhanced CT scans (non-contrast, arterial, portal venous, and delayed phases) and five tumor subtypes: hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), colorectal liver metastasis (CRLM), breast cancer liver metastasis (BCLM), and hepatic hemangioma (HH). The dataset features standardized preprocessing, rigorous quality control, and expert-annotated tumor masks. Baseline experiments using radiomics-based machine learning and deep learning models demonstrate the dataset's utility, with multi-phase integration significantly improving diagnostic performance. MCT-LTDiag serves as a benchmark for advancing automated liver tumor subtype classification and is publicly available to support future research.