Jawli Adel, Nabi Ghulam, Huang Zhihong, Alhusaini Abeer J, Wei Cheng, Tang Benjie
Biomedical Engineering, School of Science and Engineering, Fulton Building, University of Dundee, Dundee DD1 4HN, UK.
Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.
Cancers (Basel). 2025 Apr 18;17(8):1358. doi: 10.3390/cancers17081358.
: Artificial intelligence (AI) is increasingly utilized for texture analysis and the development of machine learning (ML) techniques to enhance diagnostic accuracy. ML algorithms are trained to differentiate between normal and malignant conditions based on provided data. Texture feature analysis, including first-order and second-order features, is a critical step in ML development. This study aimed to evaluate quantitative texture features of normal and prostate cancer tissues identified through ultrasound B-mode and shear-wave elastography (SWE) imaging and to develop and assess ML models for predicting and classifying normal versus malignant prostate tissues. : First-order and second-order texture features were extracted from B-mode and SWE imaging, including four reconstructed regions of interest (ROIs) from SWE images for normal and malignant tissues. A total of 94 texture features were derived, including features for intensity, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Dependence Length Matrix (GLDLM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Five ML models were developed and evaluated using 5-fold cross-validation to predict normal and malignant tissues. : Data from 62 patients were analyzed. All ROIs, except those derived from B-mode imaging, exhibited statistically significant differences in features between normal and malignant tissues. Among the developed models, Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) demonstrated the highest performance across all ROIs. These models consistently achieved strong predictive accuracy for classifying normal versus malignant tissues. Gray Pure SWE and Gray Reconstructed images Provided the highest sensitivity and specificity in PCa prediction by 82%, 90%, and 98%, 96%, respectively. : Texture analysis with machine learning on SWE-US and reconstructed images effectively differentiates malignant from benign prostate lesions, with features like contrast, entropy, and correlation playing a key role. Random Forest, SVM, and Naïve Bayes showed the highest classification performance, while grayscale reconstructions (GPSWE and GRRI) enhanced detection accuracy.
人工智能（AI）越来越多地用于纹理分析和机器学习（ML）技术的开发，以提高诊断准确性。ML算法通过所提供的数据进行训练，以区分正常和恶性情况。纹理特征分析，包括一阶和二阶特征，是ML开发中的关键步骤。本研究旨在评估通过超声B模式和剪切波弹性成像（SWE）成像识别的正常和前列腺癌组织的定量纹理特征，并开发和评估用于预测和分类正常与恶性前列腺组织的ML模型。
从B模式和SWE成像中提取一阶和二阶纹理特征，包括来自SWE图像的正常和恶性组织的四个重建感兴趣区域（ROI）。总共得出94个纹理特征，包括强度、灰度共生矩阵（GLCM）、灰度依赖长度矩阵（GLDLM）、灰度游程长度矩阵（GLRLM）和灰度大小区域矩阵（GLSZM）的特征。使用五折交叉验证开发并评估了五个ML模型，以预测正常和恶性组织。
分析了62名患者的数据。除了从B模式成像得出的ROI外，所有ROI在正常和恶性组织之间的特征上均表现出统计学上的显著差异。在开发的模型中，支持向量机（SVM）、随机森林（RF）和朴素贝叶斯（NB）在所有ROI中表现出最高的性能。这些模型在分类正常与恶性组织方面始终实现了强大的预测准确性。灰度纯SWE和灰度重建图像在前列腺癌预测中分别提供了82%、90%和98%、96%的最高敏感性和特异性。
对SWE-US和重建图像进行机器学习的纹理分析有效地将恶性前列腺病变与良性病变区分开来，对比度、熵和相关性等特征起着关键作用。随机森林、SVM和朴素贝叶斯表现出最高的分类性能，而灰度重建（GPSWE和GRRI）提高了检测准确性。
