Department of Thoracic Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, P.R. China.
China Telecom Hanshan Research Institute, Ma'anshan 238105, P.R. China.
Comput Methods Programs Biomed. 2021 Sep;208:106263. doi: 10.1016/j.cmpb.2021.106263. Epub 2021 Jul 3.
Identifying benign and malignant pulmonary nodules is essential for the early diagnosis of lung cancer and targeted surgical resection. This study aimed to differentiate benign from malignant pulmonary nodules based on computed tomography (CT) plain scan texture analysis technique.
A total of 47 pulmonary nodules use the improved window adaptive gray level co-occurrence matrix (GLCM) algorithm to extract the texture characteristics of the area of interest. The Fisher coefficient (Fisher), classification error probability joint average correlation coefficient (POE+ACC), mutual information (MI), and the combination of above three methods joint (FPM) were used to select the best texture parameters set. After that, the analysis of the screened texture parameters was adopted. The B11 module provides four analytical methods, including raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA), and nonlinear discriminant analysis (NDA). The results were expressed in the form of misclassification rate (MCR). Region of curve (ROC) analysis was also performed on the selected optimal texture parameters.
The MCR of all the three texture feature extraction methods, Fisher, POE+ACC, and MI, were lower in differentiating benign from malignant pulmonary nodules. FPM method could further reduce the MCR. The NDA analysis had the lowest MCR for both of these three feature extraction methods. The MCR can be further reduced to 2.13% by the combination of NDA and FPM. The ROC curve showed that Perc.01% parameter had the highest AUC value and the most discriminative efficacy.
The lowest MCR values were calculated by the FPM dimensionality reduction method and the NDA analysis method. The improved GLCM algorithm has a discriminative role in CT texture analysis of benign and malignant pulmonary nodules.
识别肺部良、恶性结节对于肺癌的早期诊断和靶向手术切除至关重要。本研究旨在基于 CT 平扫纹理分析技术区分良、恶性肺部结节。
共 47 个肺部结节使用改进的窗口自适应灰度共生矩阵（GLCM）算法提取感兴趣区的纹理特征。使用费歇尔系数（Fisher）、分类错误概率联合平均相关系数（POE+ACC）、互信息（MI）以及这三种方法的组合（FPM）来选择最佳纹理参数集。然后，对筛选出的纹理参数进行分析。B11 模块提供了四种分析方法，包括原始数据分析（RDA）、主成分分析（PCA）、线性判别分析（LDA）和非线性判别分析（NDA）。结果以错误分类率（MCR）的形式表示。对选定的最佳纹理参数进行了 ROC 曲线分析。
Fisher、POE+ACC 和 MI 这三种纹理特征提取方法的 MCR 在区分良、恶性肺部结节时均较低，FPM 方法可进一步降低 MCR。NDA 分析对这三种特征提取方法的 MCR 均最低。通过 NDA 和 FPM 的组合，MCR 可进一步降低至 2.13%。ROC 曲线显示 Perc.01%参数的 AUC 值最高，具有最强的鉴别效果。
FPM 降维法和 NDA 分析方法计算出的最低 MCR 值。改进的 GLCM 算法在 CT 纹理分析中对良、恶性肺部结节具有鉴别作用。
