Evidence map›Paper›PMID 31455432›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019

Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the best model?

Ming Ni, Xiaoming Zhou, Qian Lv, Zhiming Li, Yuanxiang Gao, Yongqi Tan, Jihua Liu, Fang Liu, Haiyang Yu, Linlin Jiao and 1 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 6 pooled it
–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

48 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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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

11 authors.

Ming NiDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Xiaoming ZhouDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China. zhouxm@qduhospital.cn.
Qian LvDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Zhiming LiDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Yuanxiang GaoDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Yongqi TanCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China.
Jihua LiuDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Fang LiuDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Haiyang YuDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Linlin JiaoIntervention Medical Center, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
Gang WangDepartment of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China. 313682216@qq.com.ORCID http://orcid.org/0000-0002-1503-919X

Funding

Natural Science Foundation of Shandong Province (CN) ZR2017BH04 4
6 · The paper itself

Abstract

objectivesTo explore the feasibility of diagnosing microvascular invasion (MVI) with radiomics, to compare the diagnostic performance of different models established by each method, and to determine the best diagnostic model based on radiomics.

methodsA retrospective analysis was conducted with 206 cases of hepatocellular carcinoma (HCC) confirmed through surgery and pathology in our hospital from June 2015 to September 2018. Among the samples, 88 were MVI-positive, and 118 were MVI-negative. The radiomics analysis process included tumor segmentation, feature extraction, data preprocessing, dimensionality reduction, modeling and model evaluation.

resultsA total of 1044 sets of texture feature parameters were extracted, and 21 methods were used for the radiomics analysis. All research methods could be used to diagnose MVI. Of all the methods, the LASSO+GBDT method had the highest accuracy, the LASSO+RF method had the highest sensitivity, the LASSO+BPNet method had the highest specificity, and the LASSO+GBDT method had the highest AUC. Through Z-tests of the AUCs, LASSO+GBDT, LASSO+K-NN, LASSO+RF, PCA + DT, and PCA + RF had Z-values greater than 1.96 (p<0.05). The DCA results showed that the LASSO + GBDT method was better than the other methods when the threshold probability was greater than 0.22.

conclusionsRadiomics can be used for the preoperative, noninvasive diagnosis of MVI, but different dimensionality reduction and modeling methods will affect the diagnostic performance of the final model. The model established with the LASSO+GBDT method had the optimal diagnostic performance and the greatest diagnostic value for MVI.

Indexed as

Carcinoma, HepatocellularFemaleHumansImage Processing, Computer-AssistedLiver NeoplasmsMaleMiddle AgedNeoplasm InvasivenessPositron-Emission TomographyHepatocellular carcinomaMicrovesselNeoplasm invasivenessRadiomics

Identifiers

PMID31455432
PMCPMC6712704

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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.