Introduction: 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.Methodology: 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.Results: 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.Conclusions: 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.
引言：人工智能在纹理分析及机器学习技术开发中的应用日益广泛，旨在提升诊断准确性。机器学习算法通过训练数据学习区分正常与恶性病变。纹理特征分析（包括一阶和二阶特征）是机器学习开发的关键环节。本研究旨在评估通过超声B模式及剪切波弹性成像识别的正常与前列腺癌组织的定量纹理特征，并开发评估用于预测和分类正常与恶性前列腺组织的机器学习模型。 方法：从B模式及剪切波弹性成像中提取一阶和二阶纹理特征，包括从正常与恶性组织的弹性成像中重建的四个感兴趣区域。共提取94个纹理特征，涵盖强度特征、灰度共生矩阵、灰度依赖长度矩阵、灰度游程矩阵及灰度区域大小矩阵。采用五折交叉验证法开发并评估了五种用于预测正常与恶性组织的机器学习模型。 结果：共分析62例患者数据。除B模式成像外，所有感兴趣区域在正常与恶性组织间的纹理特征均呈现统计学显著差异。在开发的模型中，支持向量机、随机森林和朴素贝叶斯在所有感兴趣区域中均表现出最优性能，这些模型在区分正常与恶性组织时均展现出稳定的高预测准确性。灰度纯弹性成像与灰度重建图像在前列腺癌预测中分别达到82%、90%和98%、96%的最高敏感性与特异性。 结论：基于剪切波弹性超声及重建图像的机器学习纹理分析能有效区分前列腺良恶性病变，其中对比度、熵和相关性等特征发挥关键作用。随机森林、支持向量机和朴素贝叶斯展现出最佳分类性能，而灰度重建图像（纯弹性成像与重建图像）进一步提升了检测准确度。
肿瘤(癌症)患者之家