Evidence map›Paper›PMID 38274720›Full record

ArticleWorld journal of oncology2024

Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma.

Wan Min Liu, Xing Yu Zhao, Meng Ting Gu, Kai Rong Song, Wei Zheng, Hui Yu, Hui Lin Chen, Xiao Wen Xu, Xiang Zhou, Ai E Liu and 2 more

Abstract read
In one paragraph

Article in World journal of oncology, 2024. 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. Review
  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

12 authors.

Wan Min LiuDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Xing Yu ZhaoDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Meng Ting GuDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Kai Rong SongDepartment of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China.
Wei ZhengDepartment of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China.
Hui YuDepartment of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China.
Hui Lin ChenSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Xiao Wen XuDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Xiang ZhouDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Ai E LiuDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
Ning Yang JiaDepartment of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China.
Pei Jun WangDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results: The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions: Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.

Indexed as

Hepatocellular carcinomaMagnetic resonance imagingMicrovascular invasionPredictive performanceRadiomics

Identifiers

PMID38274720
PMCPMC10807913

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.