On the Impact of Smoothing and Noise on Robustness of CT and CBCT Radiomics Features for Patients with Head and Neck Cancers.
发表日期： 2017.04.17 来源： Medical Physics, 2017,44,(5): 1755–1770.
Hassan Bagher-Ebadian1,2, Farzan Siddiqui1, Chang Liu1, Benjamin Movsas1, Indrin J. Chetty1
1. Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
2. Department of Physics, Oakland University, Rochester, MI, USA
We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets.
从肿瘤体积的pCT和CBCT图像中中提取纹理特征，根据以下八个不同类别进行轮廓分析：基于强度的直方图特征（IBHF），灰度运行长度（GLRL），劳氏纹理信息（LAWS），离散正交S变换（DOST），局部二元模式（LBP），二维小波变换（2DWT），二维加博滤波（2DGF）和灰度共生矩阵（GLCM）。总共提取了165个影像组学特征。使用具有不同信噪比（SNR = 5,10,15,20,25,35,50,75,100和150）的高斯噪声模型在特征提取之前对图像进行后处理。图像数据集中使用具有不同截止频率（从0.0458到0.7321个周期-mm-1变化的）的高斯滤波器。噪声和滤波对每个提取特征的影响使用后处理和基线图像上各个值之间的平均绝对百分比变化（MAPC）进行量化。Fisher方法联合韦尔奇P值用于显著性检验。对比3处研究：（a）基线pCT对比改进的pCT（使用给定的滤波器）；（b）基线CBCT对比改进的CBCT；（c）基线和改进的pCT对比基线和改进的CBCT。
Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following eight different classes: intensity based histogram features (IBHF), gray level run length (GLRL), law's textural information (LAWS), discrete orthonormal stockwell transform (DOST), local binary pattern (LBP), two-dimensional wavelet transform (2DWT), Two dimensional Gabor filter (2DGF), and gray level co-occurrence matrix (GLCM). A total of 165 radiomics features were extracted. Images were post-processed prior to feature extraction using a Gaussian noise model with different signal-to-noise-ratios (SNR = 5, 10, 15, 20, 25, 35, 50, 75, 100, and 150). Gaussian filters with different cut off frequencies (varied discreetly from 0.0458 to 0.7321 cycles-mm−1) were applied to image datasets. Effect of noise and smoothing on each extracted feature was quantified using mean absolute percent change (MAPC) between the respective values on post-processed and baseline images. The Fisher method for combining Welch P-values was used for tests of significance. Three comparisons were investigated: (a) Baseline pCT versus modified pCT (with given filter applied); (b) Baseline CBCT versus modified CBCT, and (c) Baseline and modified pCT versus baseline and modified CBCT.
从CT和CBCT图像数据集提取的特征对低通滤波（MAPC = 17.5％，
Features extracted from CT and CBCT image datasets were robust to low-pass filtering (MAPC = 17.5%, = 0.93 for CBCT and MAPC = 7.5%,
Radiomics features extracted from planning CT and daily CBCT image datasets for head/neck cancer patients were robust to low-power Gaussian noise and low-pass filtering, but were impacted by high-pass filtering. Textural features extracted from CBCT and pCT image datasets were similar, suggesting interchangeability of pCT and CBCT for investigating radiomics features as possible biomarkers for outcome.