论著摘要 |【CT】乳腺肿瘤微环境变化的乳房造影证据(双语版)

2017-08-15 14:38:06 admin 3

Mammographic evidence of microenvironment changes in tumorous breasts.

发表日期:2017.4.27    来源:Med Phys. 

作者:Marin Z1Batchelder KA1Toner BC1Guimond L1Gerasimova-Chechkina E2Harrow AR3Arneodo A4Khalil A1.

作者介绍

    1.CompuMAINE Laboratory, Department of Mathematics & Statistics, University of Maine, Orono, ME, 04469, USA.

    2.Laboratory of Physical Foundation of Strength, Institute of Continuous Media Mechanics UB RAS, Perm, Russia.

    3.Spectrum Medical Group, Eastern Maine Medical Center, Bangor, ME, 04401, USA.

    4.LOMA, Universite de Bordeaux, CNRS, UMR 5798, 33405, Talence, France.

目的

乳腺肿瘤的微环境在肿瘤发生中起关键作用。只要维持微环境的结构完整性,肿瘤就会受到抑制。如果组织结构在正常细胞周期中破坏而丧失,微环境可以成为肿瘤启动子。因此,区分健康和肿瘤组织的特征可能不仅仅在肿瘤特征中,而是在周围的非肿瘤组织中。本文的目的是展示乳腺组织微环境破坏和体内平衡丧失及和乳房双侧不对称的初步证据,可以通过对整个乳腺进行局部的基于小波分析,从乳房X射线成像进行定量和客观评估。

The microenvironment of breast tumors plays a critical role in tumorigenesis. As long as the structural integrity of the microenvironment is upheld, the tumor is suppressed. If tissue structure is lost through disruptions in the normal cell cycle, the microenvironment may act as a tumor promoter. Therefore, the properties that distinguish between healthy and tumorous tissues may not be solely in the tumor characteristics but rather in surrounding non-tumor tissue. The goal of this paper was to show preliminary evidence that tissue disruption and loss of homeostasis in breast tissue microenvironment and breast bilateral asymmetry can be quantitatively and objectively assessed from mammography via a localized, wavelet-based analysis of the whole breast.

方法

使用基于小波的多分形形式,称为2D小波变换模量最大值(WTMM)方法,通过赫斯特指数(H)来定量乳房X线摄影乳腺组织的密度波动。在重叠滑动窗口的网格化方案中,每个整个乳房X线照片被切割成数百个360×360像素的子区域,每个窗口边界由32个像素分开。将2D WTMM方法分别应用于每个子区域。建立了一种数据挖掘方法,以确定在正常与癌症病例之间最能区分哪些指标。然后使用这些相同的指标,无需修改,以区分正常与良性和良性与癌症病例。

A wavelet-based multifractal formalism called the 2D Wavelet Transform Modulus Maxima (WTMM) method was used to quantitate density fluctuations from mammographic breast tissue via the Hurst exponent (H). Each entire mammogram was cut in hundreds of 360 × 360 pixel subregions in a gridding scheme of overlapping sliding windows, with each window boundary separated by 32 pixels. The 2D WTMM method was applied to each subregion individually. A data mining approach was set up to determine which metrics best discriminated between normal vs. cancer cases. These same metrics were then used, without modification, to discriminate between normal vs. benign and benign vs. cancer cases.

结果

健康乳房X线照相乳房组织的密度波动是脂肪组织的单分形抗相关(H <1/2)或致密组织的单分形长程相关(H> 1/2)。然而,发现H1/2的组织区域,左侧和右侧乳房不对称,与正常乳房相比更偏向在肿瘤(良性或癌症)乳房中发现,P值约0.0006的组合度量进行量化。没有考虑的指标显示癌症与良性乳房之间的显着差异。

The density fluctuations in healthy mammographic breast tissue are either monofractal anti-correlated (H < 1/2) for fatty tissue or monofractal long-range correlated (H>1/2) for dense tissue. However, tissue regions with H~1/2, as well as left vs. right breast asymetries, were found preferably in tumorous (benign or cancer) breasts vs. normal breasts, as quantified via a combination metric yielding a P-value ~ 0.0006. No metric considered showed significant differences between cancer vs. benign breasts.

结论

由于与不相关的(H1/2)密度波动相关的乳房X光片组织区域主要在肿瘤乳房中,并且由于与H1/2特征相关的潜在物理过程有随机性,缺少空间相关性和自由扩散的特性,因此猜想这种特征也与组织破坏和组织内稳态的丧失有关。

Since mammographic tissue regions associated with uncorrelated (H~1/2) density fluctuations were predominantly in tumorous breasts, and since the underlying physical processes associated with a H~1/2 signature are those of randomness, lack of spatial correlation, and free diffusion, it is hypothesized that this signature is also associated with tissue disruption and loss of tissue homeostasis.

关键词

赫斯特指数;影像组学;组织破坏;组织内稳态;小波

Hurst exponent; radiomics; tissue disruption; tissue homeostasis; wavelets


阅读原文:10.1002/mp.12120


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