Alanezi Saleh T, Sullivan Frank, Kleefeld Christoph, Greally John F, Kraśny Marcin J, Woulfe Peter, Sheppard Declan, Colgan Niall
Physics Department, Faculty of Science, Northern Border University, Arar 1321, Saudi Arabia.
School of Physics, College of Science and Engineering, National University of Ireland Galway, H91 CF50 Galway, Ireland.
Cancers (Basel). 2022 Mar 23;14(7):1631. doi: 10.3390/cancers14071631.
(1) Background: Multiparametric MRI (mp-MRI) is used to manage patients with PCa. Tumor identification via irregular sampling or biopsy is problematic and does not allow the comprehensive detection of the phenotypic and genetic alterations in a tumor. A non-invasive technique to clinically assess tumor heterogeneity is also in demand. We aimed to identify tumor heterogeneity from multiparametric magnetic resonance images using texture analysis (TA). (2) Methods: Eighteen patients with prostate cancer underwent mp-MRI scans before prostatectomy. A single radiologist matched the histopathology report to single axial slices that best depicted tumor and non-tumor regions to generate regions of interest (ROIs). First-order statistics based on the histogram analysis, including skewness, kurtosis, and entropy, were used to quantify tumor heterogeneity. We compared non-tumor regions with significant tumors, employing the two-tailed Mann-Whitney U test. Analysis of the area under the receiver operating characteristic curve (ROC-AUC) was used to determine diagnostic accuracy. (3) Results: ADC skewness for a 6 × 6 px filter was significantly lower with an ROC-AUC of 0.82 ( = 0.001). The skewness of the ADC for a 9 × 9 px filter had the second-highest result, with an ROC-AUC of 0.66; however, this was not statistically significant ( = 0.08). Furthermore, there were no substantial distinctions between pixel filter size groups from the histogram analysis, including entropy and kurtosis. (4) Conclusions: For all filter sizes, there was poor performance in terms of entropy and kurtosis histogram analyses for cancer diagnosis. Significant prostate cancer may be distinguished using a textural feature derived from ADC skewness with a 6 × 6 px filter size.
(1) 背景：多参数磁共振成像（mp-MRI）用于前列腺癌（PCa）患者的管理。通过不规则采样或活检进行肿瘤识别存在问题，无法全面检测肿瘤中的表型和基因改变。临床上也需要一种非侵入性技术来评估肿瘤异质性。我们旨在使用纹理分析（TA）从多参数磁共振图像中识别肿瘤异质性。(2) 方法：18例前列腺癌患者在前列腺切除术前接受了mp-MRI扫描。一名放射科医生将组织病理学报告与最能描绘肿瘤和非肿瘤区域的单个轴向切片进行匹配，以生成感兴趣区域（ROI）。基于直方图分析的一阶统计量，包括偏度、峰度和熵，用于量化肿瘤异质性。我们使用双尾曼-惠特尼U检验比较非肿瘤区域和显著肿瘤区域。采用受试者操作特征曲线下面积（ROC-AUC）分析来确定诊断准确性。(3) 结果：6×6像素滤波器的ADC偏度显著较低，ROC-AUC为=0.82（=0.001）。9×9像素滤波器的ADC偏度结果次之，ROC-AUC为0.66；然而，这在统计学上不显著（=0.08）。此外，从包括熵和峰度的直方图分析来看，像素滤波器大小组之间没有实质性差异。(4) 结论：对于所有滤波器大小，在熵和峰度直方图分析用于癌症诊断方面表现不佳。使用6×6像素滤波器大小的ADC偏度得出的纹理特征可区分显著前列腺癌。
