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关于扩散加权乳腺成像中的分数阶微积分模型,以区分在X线筛查乳腺摄影中检测到的恶性和良性乳腺病变。

On a fractional order calculus model in diffusion weighted breast imaging to differentiate between malignant and benign breast lesions detected on X-ray screening mammography.

作者信息

Bickelhaupt Sebastian, Steudle Franziska, Paech Daniel, Mlynarska Anna, Kuder Tristan Anselm, Lederer Wolfgang, Daniel Heidi, Freitag Martin, Delorme Stefan, Schlemmer Heinz-Peter, Laun Frederik Bernd

机构信息

German Cancer Research Center (dkfz), Department of Radiology, Heidelberg, Im Neuenheimer Feld 280, Heidelberg, Germany.

German Cancer Research Center (dkfz), Medical Physics in Radiology, Heidelberg, Im Neuenheimer Feld 280, Heidelberg, Germany.

出版信息

PLoS One. 2017 Apr 28;12(4):e0176077. doi: 10.1371/journal.pone.0176077. eCollection 2017.

DOI:10.1371/journal.pone.0176077
PMID:28453516
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5409173/
Abstract

OBJECTIVE

To evaluate a fractional order calculus (FROC) model in diffusion weighted imaging to differentiate between malignant and benign breast lesions in breast cancer screening work-up using recently introduced parameters (βFROC, DFROC and μFROC).

MATERIALS AND METHODS

This retrospective analysis within a prospective IRB-approved study included 51 participants (mean 58.4 years) after written informed consent. All patients had suspicious screening mammograms and indication for biopsy. Prior to biopsy, full diagnostic contrast-enhanced MRI examination was acquired including diffusion-weighted-imaging (DWI, b = 0,100,750,1500 s/mm2). Conventional apparent diffusion coefficient Dapp and FROC parameters (βFROC, DFROC and μFROC) as suggested further indicators of diffusivity components were measured in benign and malignant lesions. Receiver operating characteristics (ROC) were calculated to evaluate the diagnostic performance of the parameters.

RESULTS

29/51 patients histopathologically revealed malignant lesions. The analysis revealed an AUC for Dapp of 0.89 (95% CI 0.80-0.98). For FROC derived parameters, AUC was 0.75 (0.60-0.89) for DFROC, 0.59 (0.43-0.75) for βFROC and 0.59 (0.42-0.77) for μFROC. Comparison of the AUC curves revealed a significantly higher AUC of Dapp compared to the FROC parameters DFROC (p = 0.009), βFROC (p = 0.003) and μFROC (p = 0.001).

CONCLUSION

In contrast to recent description in brain tumors, the apparent diffusion coefficient Dapp showed a significantly higher AUC than the recently proposed FROC parameters βFROC, DFROC and μFROC for differentiating between malignant and benign breast lesions. This might be related to the intrinsic high heterogeneity within breast tissue or to the lower maximal b-value used in our study.

摘要

目的

在扩散加权成像中评估分数阶微积分(FROC)模型,以利用最近引入的参数(βFROC、DFROC和μFROC)在乳腺癌筛查工作中区分乳腺良恶性病变。

材料与方法

这项在一项经IRB批准的前瞻性研究中的回顾性分析纳入了51名参与者(平均年龄58.4岁),所有参与者均签署了书面知情同意书。所有患者的乳腺钼靶筛查结果可疑且有活检指征。在活检前,进行了包括扩散加权成像(DWI,b = 0、100、750、1500 s/mm2)在内的全诊断性对比增强MRI检查。在良性和恶性病变中测量了传统的表观扩散系数Dapp以及作为扩散率成分进一步指标的FROC参数(βFROC、DFROC和μFROC)。计算受试者工作特征(ROC)曲线以评估这些参数的诊断性能。

结果

51例患者中29例经组织病理学检查显示为恶性病变。分析显示Dapp的曲线下面积(AUC)为0.89(95%置信区间0.80 - 0.98)。对于FROC衍生参数,DFROC的AUC为0.75(0.60 - 0.89),βFROC为0.59(0.43 - 0.75),μFROC为0.59(0.42 - 0.77)。AUC曲线比较显示,Dapp的AUC显著高于FROC参数DFROC(p = 0.009)、βFROC(p = 0.003)和μFROC(p = 0.001)。

结论

与最近关于脑肿瘤的描述不同,在区分乳腺良恶性病变方面,表观扩散系数Dapp的AUC显著高于最近提出 的FROC参数βFROC、DFROC和μFROC。这可能与乳腺组织内固有的高度异质性或本研究中使用的较低最大b值有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/54fb26b55b0c/pone.0176077.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/524444998b54/pone.0176077.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/b8e1e6ed6432/pone.0176077.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/54fb26b55b0c/pone.0176077.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/524444998b54/pone.0176077.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/b8e1e6ed6432/pone.0176077.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0e/5409173/54fb26b55b0c/pone.0176077.g003.jpg

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