HETEROGENEOUS TISSUE CHARACTERIZATION USING ULTRASOUND: A COMPARISON OF FRACTAL ANALYSIS BACKSCATTER MODELS ON LIVER TUMORS
HETEROGENEOUS TISSUE CHARACTERIZATION USING ULTRASOUND: A COMPARISON OF FRACTAL ANALYSIS BACKSCATTER MODELS ON LIVER TUMORS
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DOI：
10.1016/j.ultrasmedbio.2016.02.007
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发表时间：
2016-07-01
影响因子：
2.9
通讯作者：
Noble, J. Alison
中科院分区：
文献类型：
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作者：
Al-Kadi, Omar S.;Chung, Daniel Y. F.;Noble, J. Alison
通过超声评估肿瘤组织异质性最近被认为是预测早期治疗反应的一种方法。超声后向散射特性通过突出组织散射体的局部浓度和空间排列，有助于更好地了解肿瘤的质地。然而，量化肿瘤纹理中从细到粗的各种组织异质性是一项挑战。利用极大似然估计方法从5个知名统计模型族中提取局部参数分形特征，对超声组织表征进行评价。用分形维数（自相似度量）表征散射体的空间分布，用空洞度（稀疏度量）确定散射体数密度。根据608张肝脏肿瘤的临床超声射频图像（分别为230张和378张，分别代表应答病例和非应答病例）评估其性能。交叉验证通过留一个肿瘤和不同的k-fold方法使用贝叶斯分类器进行验证。基于Nakagami模型（Nkg）及其扩展的四参数Nakagami-广义逆高斯分布（NIG）的后向散射回波分形特性在表征肝脏肿瘤组织方面取得了最好的结果，性能接近。Nkg/NIG的准确性、敏感性和特异性分别为85.6%/86.3%、94.0%/96.0%和73.0%/71.0%。其他统计模型，如医师分布、瑞利分布和k分布，被发现在描述组织结构的细微变化方面不如对治疗反应的指示有效。采用最相关和实用的统计模型可能对设计早期和有效的临床治疗有潜在的影响。（E-mail: omar.al-kadi@eng.ox.ac.uk或o.alkadi@ju.edu.jo） (C) 2016年世界超声医学与生物学联合会。
Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results-with nearly similar performance-in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy. (E-mail: omar.al-kadi@eng.ox.ac.uk or o.alkadi@ju.edu.jo) (C) 2016 World Federation for Ultrasound in Medicine & Biology.