Evidence map›Paper›PMID 40636134›Full record

ReviewiLIVER2022

Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma.

Jingwei Wei, Meng Niu, Ouyang Yabo, Yu Zhou, Xiaoke Ma, Xue Yang, Hanyu Jiang, Hui Hui, Hongyi Cao, Binwei Duan and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iLIVER, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Jingwei WeiKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Meng NiuDepartment of Interventional Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110000, China.
Ouyang YaboBeijing YouAn Hospital, Capital Medical University, Beijing Institute of Hepatology, Beijing, 100069, China.
Yu ZhouKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Xiaoke MaSchool of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Xue YangDepartment of Radiology, Beijing Youan Hospital, Capital Medical Universtiy, Beijing, 100069, China.
Hanyu JiangDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Hui HuiKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Hongyi CaoDepartment of Pathology, College of Basic Medical Science, China Medical University, Shenyang, Liaoning, 110000, China.
Binwei DuanThe Department of General Surgery Center, Beijing YouAn Hospital, Capital Medical University, China.
Hongjun LiDepartment of Radiology, Beijing Youan Hospital, Capital Medical Universtiy, Beijing, 100069, China.
Dawei DingSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Jie TianKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) remains the most common malignancy to threaten public health globally. With advances in artificial intelligence techniques, radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings, and reveals pixel-level radiological information for medical imaging big data, correlating the radiological phenotype with targeted clinical issues. Conventional radiomics pipelines depend on handcrafted engineering features, and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies. During the past decade, radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation, as well as in prognosis prediction. In this review, we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.

Indexed as

Artificial intelligenceDiagnosisHepatocellular carcinomaPrognosisRadiomics

Identifiers

PMID40636134
PMCPMC12212591

What OpenQuestion holds

Textmetadata
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.