DOI: 10.57760/sciencedb.12207
title: Primary Liver Cancer CECT Imaging Dataset
title: Primary Liver Cancer CECT Imaging Dataset

description: Primary liver cancer is a significant global health issue, characterized by high incidence and mortality rates worldwide. Accurate diagnosis and classification of subtypes are essential for selecting appropriate treatment options and enhancing patient outcomes. Contrast-enhanced computed tomography (CECT) has proven highly sensitive and specific in diagnosing liver cancer. Currently, publicly available datasets of liver cancer CECT scans are limited and often do not comprehensively cover liver cancer subtypes or include complete phasing of CT scans. We hypothesize that utilizing full-phase 3D CECT images, including the Plain, Arterial, Venous, and Delayed phases, can improve the diagnostic classification performance for liver cancer. To test this hypothesis, we have collected a large dataset from a single medical institution that includes 278 cases of liver cancer, featuring Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (ICC), and Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA), as well as CECT images from 83 non-liver cancer subjects. For each patient, we annotated the liver and lesion regions. This dataset, rich in liver cancer types and complete in CT phasing, facilitates the development and validation of diagnostic classification models and lesion segmentation models tailored to liver cancer CT imaging.The median age of participants was 59 years [51, 67] (IQR), with 185 males (67.3% of the liver cancer group) . Each patient had complete 3D contrast-enhanced CT (CECT) data across the Plain, Arterial, Venous, and Delayed phases, stored as NIFTI files. A total of 50,560 slices containing lesions were collected, with a median lesion volume of 75.37 cm³ [26.70, 239.24] . The Python code for loading and processing the data can be found on GitHub (https://github.com/ljwa2323/PLC_CECT).
description: Primary liver cancer is a significant global health issue, characterized by high incidence and mortality rates worldwide. Accurate diagnosis and classification of subtypes are essential for selecting appropriate treatment options and enhancing patient outcomes. Contrast-enhanced computed tomography (CECT) has proven highly sensitive and specific in diagnosing liver cancer. Currently, publicly available datasets of liver cancer CECT scans are limited and often do not comprehensively cover liver cancer subtypes or include complete phasing of CT scans. We hypothesize that utilizing full-phase 3D CECT images, including the Plain, Arterial, Venous, and Delayed phases, can improve the diagnostic classification performance for liver cancer. To test this hypothesis, we have collected a large dataset from a single medical institution that includes 278 cases of liver cancer, featuring Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (ICC), and Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA), as well as CECT images from 83 non-liver cancer subjects. For each patient, we annotated the liver and lesion regions. This dataset, rich in liver cancer types and complete in CT phasing, facilitates the development and validation of diagnostic classification models and lesion segmentation models tailored to liver cancer CT imaging.The median age of participants was 59 years [51, 67] (IQR), with 185 males (67.3% of the liver cancer group) . Each patient had complete 3D contrast-enhanced CT (CECT) data across the Plain, Arterial, Venous, and Delayed phases, stored as NIFTI files. A total of 50,560 slices containing lesions were collected, with a median lesion volume of 75.37 cm³ [26.70, 239.24] . The Python code for loading and processing the data can be found on GitHub (https://github.com/ljwa2323/PLC_CECT).