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用于膀胱癌诊断的非高斯扩散度量与全肿瘤直方图分析:肌肉浸润和组织学分级

Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade.

作者信息

Fan Zhichang, Guo Junting, Zhang Xiaoyue, Chen Zeke, Wang Bin, Jiang Yueluan, Li Yan, Wang Yongfang, Yang Guoqiang, Wang Xiaochun

机构信息

Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.

Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China.

出版信息

Insights Imaging. 2024 Jun 9;15(1):138. doi: 10.1186/s13244-024-01701-z.

Abstract

PURPOSE

To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa).

METHODS

Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWI) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWI and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC).

RESULTS

MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46-82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31-82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWI's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWI's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWI (p > 0.05).

CONCLUSION

CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively.

CRITICAL RELEVANCE STATEMENT

Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures.

KEY POINTS

Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWI in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa.

摘要

目的

探讨非高斯扩散指标的直方图特征在诊断膀胱癌(BCa)肌肉浸润和组织学分级中的性能。

方法

前瞻性地将患者分配到MR扫描仪1(训练队列)或MR2(测试队列)进行常规扩散加权成像(DWI)和多b值DWI检查。使用多b值DWI同时计算连续时间随机游走(CTRW)、扩散峰度成像(DKI)、分数阶微积分(FROC)、体素内不相干运动(IVIM)和拉伸指数模型(SEM)的指标。从DWI和非高斯扩散指标中提取全肿瘤直方图特征进行逻辑回归分析,以建立诊断肌肉浸润和组织学分级的扩散模型。通过受试者操作特征曲线(AUC)下的面积对模型性能进行量化。

结果

MR1纳入了267例经病理证实的BCa患者(中位年龄67岁[四分位间距,46 - 82岁],男性222例),MR2纳入了83例(中位年龄65岁[四分位间距,31 - 82岁],男性73例)。对于鉴别肌肉浸润,CTRW的测试AUC最高,为0.915,高于DWI的0.805(p = 0.014),与联合扩散模型的AUC 0.885相似(p = 0.076)。对于区分非肌肉浸润性膀胱癌的组织学分级,IVIM的测试AUC表现出色,为0.897,高于DWI的0.694(p = 0.020),与联合扩散模型的AUC 0.917相似(p = 0.650)。在这两项任务中,DKI、FROC和SEM均未显示出优于DWI的诊断优势(p > 0.05)。

结论

CTRW和IVIM分别是两种潜在的非高斯扩散模型,可改善MRI在评估BCa肌肉浸润和组织学分级中的应用。

关键相关性声明

我们的研究验证了非高斯扩散成像作为一种可靠的非侵入性技术,可用于早期评估BCa的肌肉浸润和组织学分级,提高诊断准确性并改善MRI在BCa诊断程序中的应用。

要点

肌肉浸润在很大程度上决定了膀胱癌患者的膀胱保留率。在BCa肌肉浸润和组织学分级诊断中,评估的非高斯扩散指标优于DWI。非高斯扩散成像改善了MRI在BCa术前诊断中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2712/11162990/3c735975fce5/13244_2024_1701_Fig1_HTML.jpg

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