School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Med Phys. 2024 Jan;51(1):650-661. doi: 10.1002/mp.16829. Epub 2023 Nov 14.
To develop and validate a dosiomics and radiomics model based on three-dimensional (3D) dose distribution map and computed tomography (CT) images for the prediction of the post-radiotherapy (post-RT) neutrophil-to-lymphocyte ratio (NLR).
This work retrospectively collected 242 locally advanced non-small cell lung cancer (LA-NSCLC) patients who were treated with definitive radiotherapy from 2012 to 2016. The NLR collected one month after the completion of RT was defined as the primary outcome. Clinical characteristics and two-dimensional dosimetric factors calculated from the dose-volume histogram (DVH) were included. A total of 4165 dosiomics and radiomics features were extracted from the 3D dose maps and CT images within five different anatomical regions of interest (ROIs), respectively. Then, a three-step feature selection method was proposed to progressively filter features from coarse to fine: (i) model-based ranking according to individual feature's performance, (ii) maximum relevance and minimum redundancy (mRMR), (iii) select from model based on feature importance calculated with an ensemble of several decision trees. The selected feature subsets were utilized to develop the prediction model with GBDT. All patients were divided into a development set and an independent testing set (2:1). Five-fold cross-validation was applied to the development set for both feature selection and model training procedure. Finally, a fusion model combining dosiomics, radiomics and clinical features was constructed to further improve the prediction results. The area under receiver operating characteristic curve (ROC) were used to evaluate the model performance.
The clinical-based and DVH-based models showed limited predictive power with AUCs of 0.632 (95% CI: 0.490-0.773) and 0.634 (95% CI: 0.497-0.771), respectively, in the independent testing set. The 9 feature-based dosiomics and 3 feature-based radiomics models showed improved AUCs of 0.738 (95% CI: 0.628-0.849) and 0.689 (95% CI: 0.566-0.813), respectively. The dosiomics & radiomics & clinical fusion model further improved the model's generalization ability with an AUC of 0.765 (95% CI: 0.656-0.874).
Dosiomics and radiomics can benefit the prediction of post-RT NLR of LA-NSCLC patients. This can provide a reference for evaluating radiotherapy-related inflammation.
基于三维（3D）剂量分布图和计算机断层扫描（CT）图像，开发和验证一种适用于预测放疗后中性粒细胞与淋巴细胞比值（NLR）的剂量组学和放射组学模型。
本研究回顾性收集了 2012 年至 2016 年间接受根治性放疗的 242 例局部晚期非小细胞肺癌（LA-NSCLC）患者。放疗结束后一个月收集的 NLR 被定义为主要结局。纳入的临床特征和从剂量-体积直方图（DVH）计算的二维剂量学因素。分别从 3D 剂量图和 CT 图像的 5 个不同感兴趣区（ROI）中提取了总共 4165 个剂量组学和放射组学特征。然后，提出了一种三步骤特征选择方法，从粗到细逐步筛选特征：（i）基于个体特征性能的基于模型的排序，（ii）最大相关性和最小冗余度（mRMR），（iii）基于使用多个决策树的集成计算的特征重要性从模型中选择。利用 GBDT 开发预测模型。所有患者分为开发集和独立测试集（2：1）。在开发集中应用五折交叉验证进行特征选择和模型训练过程。最后，构建了一个融合剂量组学、放射组学和临床特征的融合模型，以进一步提高预测结果。接受者操作特征曲线（ROC）下的面积用于评估模型性能。
临床和 DVH 模型在独立测试集中的 AUC 分别为 0.632（95%CI：0.490-0.773）和 0.634（95%CI：0.497-0.771），预测能力有限。基于 9 个特征的剂量组学和 3 个特征的放射组学模型的 AUC 分别提高到 0.738（95%CI：0.628-0.849）和 0.689（95%CI：0.566-0.813）。剂量组学和放射组学与临床融合模型进一步提高了模型的泛化能力，AUC 为 0.765（95%CI：0.656-0.874）。
剂量组学和放射组学可以有助于预测 LA-NSCLC 患者放疗后的 NLR。这可以为评估放疗相关炎症提供参考。
