Prinzi Francesco, Militello Carmelo, Conti Vincenzo, Vitabile Salvatore
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy.
Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), 90146 Palermo, Italy.
J Imaging. 2023 Jan 30;9(2):32. doi: 10.3390/jimaging9020032.
Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that , , , and kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.
放射组学分析能够检测出支持临床环境中从诊断到预后等决策过程的影像生物标志物。通常，通过考虑诸如小波变换等高级特征来扩充原始的放射组学特征集。然而，有几个小波族（即所谓的核）能够生成原始图像的不同多分辨率表示，而其中哪一个能产生更显著的图像尚不清楚。在本研究中，通过比较不同的小波核并评估它们对放射组学模型预测能力的影响进行了深入分析。作为案例研究，使用了一个由1589张胸部X光图像组成的数据集来预测COVID-19的预后。在经过严格的特征选择策略以构建预测模型后，对随机森林、支持向量机和XGBoost（在1103张图像的子集上）进行了训练。接下来，为了评估模型对未见数据的泛化能力，进行了测试阶段（在486张图像的子集上）。实验结果表明，对于所考虑的所有三种机器学习模型， 、 、 和 核保证了更好且相似的性能。支持向量机和随机森林表现出可比的性能，并且它们优于XGBoost。此外，随机森林被证明是最稳定的模型，确保了敏感性和特异性之间的适当平衡。
