Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.
The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA.
Med Phys. 2018 Nov;45(11):5317-5324. doi: 10.1002/mp.13150. Epub 2018 Sep 24.
The purpose of this study was to explore gains in predictive model performance for radiation pneumonitis (RP) using pretreatment CT radiomics features extracted from the normal lung volume.
A total of 192 patients treated for nonsmall cell lung cancer with definitive radiotherapy were considered in the current study. In addition to clinical and dosimetric data, CT radiomics features were extracted from the total lung volume defined using the treatment planning scan. A total of 6851 features (15 clinical, 298 total lung and heart dosimetric, and 6538 image features) were gathered and considered candidate predictors for modeling of RP grade ≥3. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors with 50 iterations of tenfold nested cross-validation.
In the current cohort, 30 of 192 patients (15.6%) presented with RP grade ≥3. Average cross-validated AUC (CV-AUC) using only the clinical and dosimetric parameters was 0.51. CV-AUC was 0.68 when total lung CT radiomics features were added. Analysis with the entire set of available predictors revealed seven different image features selected in at least 40% of the model fits.
We have successfully incorporated CT radiomics features into a framework for building predictive RP models via LASSO logistic regression. Addition of normal lung image features produced superior model performance relative to traditional dosimetric and clinical predictors of RP, suggesting that pretreatment CT radiomics features should be considered in the context of RP prediction.
本研究旨在探讨使用预处理 CT 放射组学特征从正常肺体积中预测放射性肺炎 (RP) 的模型性能的提高。
本研究共纳入 192 例接受非小细胞肺癌根治性放疗的患者。除临床和剂量学数据外，还从治疗计划扫描定义的全肺体积中提取 CT 放射组学特征。共提取了 6851 个特征（15 个临床特征、298 个全肺和心脏剂量学特征以及 6538 个图像特征），并将其作为建模 RP 等级≥3 的候选预测因子。使用最小绝对值收缩和选择算子 (LASSO) 逻辑回归建立模型，并将模型应用于候选预测因子集，进行 50 次 10 倍嵌套交叉验证。
在本队列中，192 例患者中有 30 例（15.6%）出现 RP 等级≥3。仅使用临床和剂量学参数的平均交叉验证 AUC（CV-AUC）为 0.51。当添加全肺 CT 放射组学特征时，CV-AUC 为 0.68。使用所有可用预测因子进行分析，发现有 7 个不同的图像特征在至少 40%的模型拟合中被选中。
我们已成功地将 CT 放射组学特征纳入 LASSO 逻辑回归构建预测 RP 模型的框架中。与传统的 RP 预测剂量学和临床预测因子相比，添加正常肺图像特征可提高模型性能，这表明在预测 RP 时应考虑预处理 CT 放射组学特征。
