Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran.
Abdom Radiol (NY). 2022 Nov;47(11):3645-3659. doi: 10.1007/s00261-022-03625-y. Epub 2022 Aug 11.
The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients.
The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment.
The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively.
This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.
本研究旨在评估基于机器学习算法的不同去噪滤波器下直肠内超声（EUS）放射组学特征与局部晚期直肠癌（LARC）患者放射治疗反应的相关性。
本研究对 43 例 LARC 患者的 EUS 图像进行了分析，这些图像被用作预测新辅助放化疗（NCRT）治疗反应的预测生物标志物。为了去噪，EUS 图像首先经过传统滤波器（双边、维纳、李、弗罗斯特、中值和小波滤波器）预处理。两位读者分别对直肠肿瘤进行勾画，并提取放射组学特征。使用最小绝对值收缩和选择算子进行特征选择。使用分层五折交叉验证对逻辑回归（LR）、K 最近邻（KNN）、支持向量机（SVM）、随机森林、朴素贝叶斯和决策树等分类器进行训练，以开发模型。通过获得曲线下面积（AUC）、准确性、精度、敏感性和特异性来评估模型性能。
小波滤波器的 AUC 均值为 0.83，准确性为 77.41%，精度为 82.15%，敏感性为 79.41%，效果最好。LR 和 SVM 的 AUC 分别为 0.71 和 0.76，准确性分别为 70.0%和 71.5%，精度分别为 75.0%和 73.0%，敏感性分别为 69.8%和 80.2%，特异性分别为 70.0%和 60.9%，具有最高的模型性能。
本研究表明，EUS 基于放射组学模型可作为预测直肠癌病理特征的预处理生物标志物。小波滤波器和机器学习方法（LR 和 SVM）在直肠癌 EUS 图像上取得了较好的效果。
