Khajetash Benyamin, Hajianfar Ghasem, Talebi Amin, Ghavidel Beth, Mahdavi Seied Rabi, Lei Yang, Tavakoli Meysam
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
Biomed Phys Eng Express. 2025 Feb 6;11(2). doi: 10.1088/2057-1976/adafac.
. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stages HNC patients using CT and MRI image features along with demographics and dosimetric information.A cohort of 85 HNC patients who underwent radiation treatment was evaluated. We built different ML-based classifiers to build a multi-objective, multimodal radiomics model by extracting 346 different features from patient data. The models were trained and tested for prediction, utilizing Relief feature selection method and eight classifiers consisting eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic Regression (LR), and Decision Tree (DT). The performance of the models was evaluated using sensitivity, specificity, area under the curve (AUC), and accuracy metrics.Using a combination of demographics, dosimetric, and image features, the SVM model obtained the best performance with AUC of 0.77 and 0.81 for predicting early sticky saliva and xerostomia, respectively. Also, SVM and MLP classifiers achieved a noteworthy AUC of 0.85 and 0.64 for predicting late sticky saliva and xerostomia, respectively.. This study highlights the potential of baseline CT and MRI image features, combined with dosimetric data and patient demographics, to predict radiation-induced xerostomia and sticky saliva. The use of ML techniques provides valuable insights for personalized treatment planning to mitigate toxicity effects during radiation therapy for HNC patients.
尽管放射治疗技术是头颈癌（HNC）的主要治疗方法，但它们仍然伴随着相当大的毒性和副作用。基于机器学习（ML）的放射组学模型在预测毒性方面大多依赖于从治疗前成像数据中提取的特征。本研究旨在使用CT和MRI图像特征以及人口统计学和剂量学信息，比较不同模型在预测早期和晚期HNC患者放射性口干和唾液黏稠方面的效果。对85名接受放射治疗的HNC患者进行了队列评估。我们通过从患者数据中提取346种不同特征，构建了不同的基于ML的分类器，以建立一个多目标、多模态放射组学模型。利用Relief特征选择方法和由极端梯度提升（XGBoost）、多层感知器（MLP）、支持向量机（SVM）、随机森林（RF）、K近邻（KNN）、朴素贝叶斯（NB）、逻辑回归（LR）和决策树（DT）组成的八个分类器对模型进行训练和测试以进行预测。使用敏感性、特异性、曲线下面积（AUC）和准确性指标对模型的性能进行评估。结合人口统计学、剂量学和图像特征，SVM模型在预测早期唾液黏稠和口干方面分别获得了最佳性能，AUC分别为0.77和0.81。此外，SVM和MLP分类器在预测晚期唾液黏稠和口干方面分别获得了显著的AUC，分别为0.85和0.64。本研究强调了基线CT和MRI图像特征，结合剂量学数据和患者人口统计学，在预测放射性口干和唾液黏稠方面的潜力。ML技术的使用为个性化治疗计划提供了有价值的见解，以减轻HNC患者放射治疗期间的毒性作用。
