Wang Xun, Zhang Aiping, Yang Huipeng, Zhang Guqing, Ma Junli, Ye Shucheng, Ge Shuang
Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, 272000, Shandong, China.
Department of Radiation Oncology, Tumor Hospital of Jining, Jianshe North Road, Jining, 272123, Shandong, China.
Sci Rep. 2025 May 16;15(1):17106. doi: 10.1038/s41598-025-02045-4.
Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dosimetry images combining with clinical parameters to improve the predictive ability of ≥ 2 grade RP (RP2) in patients with non-small cell lung cancer (NSCLC). This study retrospectively collected 245 patients with NSCLC who received radiotherapy from three hospitals. 162 patients from Hospital I were randomly divided into training cohort and internal validation cohort according to 7:3. 83 patients from two other hospitals served as an external validation cohort. Multivariate analysis was used to screen independent clinical predictors and establish clinical model (CM). The radiomic and dosiomics (RD) features and DL features were extracted from simulated location CT and dosimetry images based on the region of interest (ROI) of total lung-PTV (TL-PTV). The features screened by the t-test and least absolute shrinkage and selection operator (LASSO) were used to construct the RD and DL model, and RD-score and DL-score were calculated. RD-score, DL-score and independent clinical features were combined to establish deep learning radiomics and dosiomics nomogram (DLRDN). The model performance was evaluated by area under the curve (AUC). Three clinical factors, including V20, V30, and mean lung dose (MLD), were used to establish the CM. 7 RD features including 4 radiomics features and 3 dosiomics features were selected to establish RD model. 10 DL features were selected to establish DL model. Among the different models, DLRDN showed the best predictions, with the AUCs of 0.891 (0.826-0.957), 0.825 (0.693-0.957), and 0.801 (0.698-0.904) in the training cohort, internal validation cohort and external validation cohort, respectively. DCA showed that DLRDN had a higher overall net benefit than other models. The calibration curve showed that the predicted value of DLRDN was in good agreement with the actual value. Overall, radiomics, dosiomics, and DL features based on simulated location CT and dosimetry images have the potential to help predict RP2. The combination of multi-dimensional data produced the optimal predictive model, which could provide guidance for clinicians.
放射性肺炎（RP）是胸部放疗最常见的副作用，会影响患者的生活质量。本研究旨在基于模拟定位CT和剂量学图像，结合临床参数，建立一个包含放射组学、剂量组学和深度学习（DL）的联合模型，以提高非小细胞肺癌（NSCLC）患者≥2级RP（RP2）的预测能力。本研究回顾性收集了来自三家医院接受放疗的245例NSCLC患者。将医院I的162例患者按照7:3随机分为训练队列和内部验证队列。另外两家医院的83例患者作为外部验证队列。采用多因素分析筛选独立临床预测因素并建立临床模型（CM）。基于全肺-计划靶区（TL-PTV）的感兴趣区（ROI），从模拟定位CT和剂量学图像中提取放射组学和剂量组学（RD）特征以及DL特征。采用t检验和最小绝对收缩和选择算子（LASSO）筛选出的特征用于构建RD和DL模型，并计算RD评分和DL评分。将RD评分、DL评分和独立临床特征相结合，建立深度学习放射组学和剂量组学列线图（DLRDN）。通过曲线下面积（AUC）评估模型性能。采用包括V20、V30和平均肺剂量（MLD）在内的三个临床因素建立CM。选择包括4个放射组学特征和3个剂量组学特征在内的7个RD特征建立RD模型。选择10个DL特征建立DL模型。在不同模型中，DLRDN显示出最佳预测效果，在训练队列、内部验证队列和外部验证队列中的AUC分别为0.891（0.826 - 0.957）、0.825（0.693 - 0.957）和0.801（0.698 - 0.904）。决策曲线分析（DCA）表明，DLRDN的总体净效益高于其他模型。校准曲线显示，DLRDN的预测值与实际值吻合良好。总体而言，基于模拟定位CT和剂量学图像的放射组学、剂量组学和DL特征有潜力帮助预测RP2。多维度数据的组合产生了最优预测模型，可为临床医生提供指导。
