Abstract：
OBJECTIVE: This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients.
METHODS: The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value.
RESULTS: The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05).
CONCLUSIONS: Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients.
CONCLUSIONS: Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
METHODS: The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value.
RESULTS: The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05).
CONCLUSIONS: Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients.
CONCLUSIONS: Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
摘要：
目的：本研究旨在研究基于深度学习（DL）的影像学特征对局部晚期非小细胞肺癌（LA-NSCLC）患者放射性肺炎（RP）的预测能力。
方法：研究对象包括复旦大学附属上海肿瘤防治中心90例患者和江南大学附属医院59例患者。RP的发生被用作终点事件。从放射治疗前计划CT上描绘的两个感兴趣区域（肺PTV和PTV-GTV）中总共提取了512个3DDL衍生特征。使用LASSO回归进行特征选择，并使用多层感知器方法建立分类模型。通过接收器工作特性曲线分析评估了开发模型的性能。此外,所开发的模型补充了相关的临床变量和剂量-体积指标,以寻找更高的预测价值.
结果：使用从肺PTV导出的DL特征的预测模型优于基于从PTV-GTV提取的特征的预测模型，AUC分别为0.921和0.892，在内部测试数据集中。此外，使用肺PTV的特征将剂量-体积度量V30Gy纳入预测模型，导致训练数据的AUC从0.835提高到0.881，验证数据从0.690提高到0.746。分别(DeLongp<0.05)。
结论：使用3DDL网络从放疗前计划CT中提取的成像特征可以预测放射性肺炎，并且可能对LA-NSCLC患者的风险分层和毒性处理具有临床价值。
结论：将DL衍生特征与剂量-体积指标相结合，为预测LA-NSCLC肺癌放疗中放射性肺炎提供了一种有希望的无创性方法，从而改善个性化治疗和患者预后。
方法：研究对象包括复旦大学附属上海肿瘤防治中心90例患者和江南大学附属医院59例患者。RP的发生被用作终点事件。从放射治疗前计划CT上描绘的两个感兴趣区域（肺PTV和PTV-GTV）中总共提取了512个3DDL衍生特征。使用LASSO回归进行特征选择，并使用多层感知器方法建立分类模型。通过接收器工作特性曲线分析评估了开发模型的性能。此外,所开发的模型补充了相关的临床变量和剂量-体积指标,以寻找更高的预测价值.
结果：使用从肺PTV导出的DL特征的预测模型优于基于从PTV-GTV提取的特征的预测模型，AUC分别为0.921和0.892，在内部测试数据集中。此外，使用肺PTV的特征将剂量-体积度量V30Gy纳入预测模型，导致训练数据的AUC从0.835提高到0.881，验证数据从0.690提高到0.746。分别(DeLongp<0.05)。
结论：使用3DDL网络从放疗前计划CT中提取的成像特征可以预测放射性肺炎，并且可能对LA-NSCLC患者的风险分层和毒性处理具有临床价值。
结论：将DL衍生特征与剂量-体积指标相结合，为预测LA-NSCLC肺癌放疗中放射性肺炎提供了一种有希望的无创性方法，从而改善个性化治疗和患者预后。
