Evidence map›Paper›PMID 40348741›Full record

ArticleScientific data2025

Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer.

Jiawei Luo, Xiaoyu Wan, Jinchao Du, Li Liu, Ling Zhao, Xin Peng, Min Wu, Shixin Huang, Xixi Nie

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jiawei LuoWest China Biomedical Big Data Center, West China Hospital; Med-X Center for Informatics, Sichuan University, Chengdu, 610044, China.
Xiaoyu WanSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Jinchao DuDepartment of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400000, China.
Li LiuDepartment of Radiology, The People's Hospital of Yubei District of Chongqing city, Chongqing, 401120, China.
Ling ZhaoDepartment of Radiology, The People's Hospital of Yubei District of Chongqing city, Chongqing, 401120, China.
Xin PengDepartment of Radiology, The People's Hospital of Yubei District of Chongqing city, Chongqing, 401120, China.
Min WuCollege of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
Shixin HuangDepartment of Scientific Research, The People's Hospital of Yubei District of Chongqing city, Chongqing, 401120, China. d200101011@stu.cqupt.edu.cn.
Xixi NieSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. niexx@cqupt.edu.cn.

Funding

Sichuan Provincial Department of Science and Technology | Sichuan Province Science and Technology Support Program 2023YFS0200
6 · The paper itself

Abstract

Primary liver cancer is a significant global health issue with high incidence and mortality rates worldwide. Accurate diagnosis and classification of its subtypes are crucial for choosing the right treatment options and improving patient outcomes. Contrast-enhanced computed tomography (CECT) is known for its high sensitivity and specificity in diagnosing liver cancer. However, publicly available datasets of liver cancer CECT scans are limited and often do not fully cover all subtypes or include complete CT scan phases. We hypothesize that using 3D CECT images with complete scan phases can help develop and validate diagnostic and segmentation models for primary liver cancer. Therefore, we created a CECT dataset with annotated liver and lesion areas. This dataset includes 278 cases of liver cancer, featuring hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma, along with CECT images from 83 non-liver cancer subjects. It contains over 50,000 layers of liver cancer lesion images. We believe this dataset can offer valuable support for developing and validating models for classifying and segmenting primary liver cancer.

Indexed as

Imaging, Three-DimensionalLiver NeoplasmsTomography, X-Ray ComputedCarcinoma, HepatocellularCholangiocarcinomaContrast MediaHumansContrast Media

Identifiers

PMID40348741
PMCPMC12065907

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.