Suppr超能文献

用于鉴别乳腺良恶性肿瘤的血氧水平依赖性功能磁共振成像(MRI)、动态对比增强MRI及扩散加权MRI:初步经验

Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI, and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience.

作者信息

Fusco Roberta, Granata Vincenza, Mattace Raso Mauro, Vallone Paolo, De Rosa Alessandro Pasquale, Siani Claudio, Di Bonito Maurizio, Petrillo Antonella, Sansone Mario

机构信息

Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy

Department Electrical Engineering and Information Technologies, Universita’ Degli Studi DI Napoli Federico II, 80125 Naples, Italy

出版信息

Cancers (Basel). 2021 May 17;13(10):2421. doi: 10.3390/cancers13102421.

Abstract

PURPOSE

To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions.

METHODS

Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including , , , and were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (), perfusion fraction (), and tissue diffusivity ()). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered.

RESULTS

R* and D had a significant negative correlation (-0.57). The mean value, standard deviation, Skewness and Kurtosis values of R* did not show a statistical significance between benign and malignant lesions ( > 0.05) confirmed by the 'poor' diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of , Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (-value = 0.02). Significant results for the mean value of , mean value, standard deviation value and Skewness of , mean value, Skewness and Kurtosis of were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered.

CONCLUSIONS

Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R* and .

摘要

目的

结合血氧水平依赖性功能磁共振成像(BOLD-MRI)、动态对比增强磁共振成像(DCE-MRI)和扩散加权磁共振成像(DW-MRI)来鉴别乳腺良恶性病变。

方法

本回顾性初步研究纳入了37例经病理证实的乳腺病变(11例良性病变和21例恶性病变)。通过DCE-MRI提取药代动力学参数,包括 、 、 和 ;通过基础信号S0和弛豫率R*估计BOLD参数;通过DW-MRI得出扩散和灌注参数(伪扩散系数( )、灌注分数( )和组织扩散率( ))。计算相关系数、Wilcoxon-Mann-Whitney U检验和受试者工作特征(ROC)分析,并获得ROC曲线下面积(AUC)。此外,还考虑了采用平衡技术和留一法交叉验证方法的模式识别方法(线性判别分析和决策树)。

结果

R与D呈显著负相关(-0.57)。ROC分析的“较差”诊断价值证实,R的平均值、标准差、偏度和峰度值在良性和恶性病变之间未显示出统计学意义( > 0.05)。对于DW-MRI得出的参数,单变量分析中, 的标准差、D的偏度和峰度值在鉴别良性和恶性病变方面有显著结果,在鉴别良性和恶性病变中的单变量分析中,D的偏度获得了最佳结果,AUC为82.9%( -值 = 0.02)。 的平均值、 的平均值、标准差和偏度、 的平均值、偏度和峰度获得了显著结果,DCE-MRI提取参数中最佳AUC由 的平均值达到,等于80.0%。在仅考虑DCE-MRI参数的多变量分析中,采用平衡技术时,鉴别良性和恶性病变的最佳诊断性能AUC = 0.91。

结论

我们的结果表明,在乳腺病变分类中,与单独使用DCE-MRI特征相比,联合使用DCE-MRI、DW-MRI和/或BOLD-MRI并没有带来显著改善。然而,一个有趣的结果是R*与 之间的负相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4e5/8155852/e3cf7c2a2cc6/cancers-13-02421-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验