Evidence map›Paper›PMID 37508772›Full record

ArticleBioengineering (Basel, Switzerland)2023

Evaluation of Whole-Tumor Texture Analysis Based on MRI Diffusion Kurtosis and Biparametric VI-RADS Model for Staging and Grading Bladder Cancer.

Xiaoyan Meng, Shichao Li, Kangwen He, Henglong Hu, Cui Feng, Zhen Li, Yanchun Wang

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaoyan MengDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Shichao LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Kangwen HeDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Henglong HuDepartment of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.ORCID 0000-0002-3618-7036
Cui FengDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.ORCID 0000-0001-9719-8436
Zhen LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.ORCID 0000-0001-8037-4245
Yanchun WangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.ORCID 0000-0002-8688-303X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundto evaluate the feasibility of texture analysis (TA) based on diffusion kurtosis imaging (DKI) in staging and grading bladder cancer (BC) and to compare it with apparent diffusion coefficient (ADC) and biparametric vesical imaging reporting and data system (VI-RADS). MATERIALS AND

methodsIn this retrospective study, 101 patients with pathologically confirmed BC underwent MRI with multiple-b values ranging from 0 to 2000 s/mm

resultsThe NMIBC and low-grade group had higher MDmean, MD5th, MD25th, MD50th, MD75th, MD90th, and ADC values than those of the MIBC and the high-grade group. The NMIBC and low-grade group yielded lower MKmean, MK25th, MK50th, MK75th, and MK90th than the MIBC and high-grade group. Among all histogram parameters, MD75th and MD90th yielded the highest AUC in differentiating MIBC from NMIBC (both AUCs were 0.87), while the AUC for ADC was 0.86. The MK75th and MK90th had the highest AUC (both 0.79) in differentiating low- from high-grade BC, while ADC had an AUC of 0.68. The AUC (0.92) of the combination of DKI histogram parameters (MD75th, MD90th, and MK90th) with biparametric VI-RADS in staging BC was higher than that of the biparametric VI-RADS (0.89).

conclusionsTexture-analysis-derived DKI is useful in evaluating both the staging and grading of bladder cancer; in addition, the histogram parameters of the DKI (MD75th, MD90th, and MK90th) can provide additional value to VI-RADS.

Indexed as

bladder cancerdiffusion kurtosis imaginggradingstagingtexture analysis

Identifiers

PMID37508772
PMCPMC10376391

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