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基于MRI扩散峰度成像和双参数VI-RADS模型的全肿瘤纹理分析在膀胱癌分期和分级中的应用评估

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

作者信息

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

机构信息

Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

出版信息

Bioengineering (Basel). 2023 Jun 21;10(7):745. doi: 10.3390/bioengineering10070745.

DOI:10.3390/bioengineering10070745
PMID:37508772
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10376391/
Abstract

BACKGROUND

to 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 METHODS

In this retrospective study, 101 patients with pathologically confirmed BC underwent MRI with multiple-b values ranging from 0 to 2000 s/mm. ADC- and DKI-derived parameters, including mean kurtosis (MK) and mean diffusivity (MD), were obtained. First-order texture histogram parameters of MK and MD, including the mean; 5th, 25th, 50th, 75th, and 90th percentiles; inhomogeneity; skewness: kurtosis; and entropy; were extracted. The VI-RADS score was evaluated based on the T2WI and DWI. The Mann-Whitney U-test was used to compare the texture parameters and ADC values between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), as well as between low and high grades. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of each significant parameter and their combinations.

RESULTS

The 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).

CONCLUSIONS

Texture-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.

摘要

背景

评估基于扩散峰度成像(DKI)的纹理分析(TA)在膀胱癌(BC)分期和分级中的可行性,并将其与表观扩散系数(ADC)和双参数膀胱影像报告和数据系统(VI-RADS)进行比较。

材料与方法

在这项回顾性研究中,101例经病理证实的BC患者接受了多b值(范围从0至2000 s/mm²)的MRI检查。获得了ADC和DKI衍生参数,包括平均峰度(MK)和平均扩散率(MD)。提取了MK和MD的一阶纹理直方图参数,包括均值、第5、25、50、75和90百分位数、不均匀性、偏度、峰度和熵。基于T2WI和DWI评估VI-RADS评分。采用Mann-Whitney U检验比较非肌层浸润性膀胱癌(NMIBC)与肌层浸润性膀胱癌(MIBC)之间以及低级别与高级别之间的纹理参数和ADC值。采用受试者操作特征分析评估每个显著参数及其组合的诊断性能。

结果

NMIBC组和低级别组的MD均值、MD第5百分位数、MD第25百分位数、MD第50百分位数、MD第75百分位数、MD第90百分位数和ADC值均高于MIBC组和高级别组。NMIBC组和低级别组的MK均值、MK第25百分位数、MK第50百分位数、MK第75百分位数和MK第90百分位数均低于MIBC组和高级别组。在所有直方图参数中,MD第75百分位数和MD第90百分位数在区分MIBC与NMIBC时的曲线下面积(AUC)最高(均为0.87),而ADC的AUC为0.86。MK第75百分位数和MK第90百分位数在区分低级别与高级别BC时的AUC最高(均为0.79),而ADC的AUC为0.68。DKI直方图参数(MD第75百分位数、MD第90百分位数和MK第90百分位数)与双参数VI-RADS联合用于BC分期时的AUC(0.92)高于双参数VI-RADS的AUC(0.89)。

结论

基于纹理分析的DKI有助于评估膀胱癌的分期和分级;此外,DKI的直方图参数(MD第75百分位数、MD第90百分位数和MK第90百分位数)可为VI-RADS提供额外价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/3847f1bf9ef6/bioengineering-10-00745-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/8730a18e905e/bioengineering-10-00745-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/6b1138281f9a/bioengineering-10-00745-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/e0e331c9a5f3/bioengineering-10-00745-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/aa79dc4cb292/bioengineering-10-00745-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/a9cac7a925b6/bioengineering-10-00745-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/3847f1bf9ef6/bioengineering-10-00745-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/8730a18e905e/bioengineering-10-00745-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/6b1138281f9a/bioengineering-10-00745-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/e0e331c9a5f3/bioengineering-10-00745-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/aa79dc4cb292/bioengineering-10-00745-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/a9cac7a925b6/bioengineering-10-00745-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/919b/10376391/3847f1bf9ef6/bioengineering-10-00745-g006.jpg

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