Li Bing, Zheng Xiaoli, Zhang Jiang, Lam Saikit, Guo Wei, Wang Yunhan, Cui Sunan, Teng Xinzhi, Zhang Yuanpeng, Ma Zongrui, Zhou Ta, Lou Zhaoyang, Meng Lingguang, Ge Hong, Cai Jing
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
Cancers (Basel). 2022 Oct 6;14(19):4889. doi: 10.3390/cancers14194889.
Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson’s correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features.
评估从我们提出的基于增量剂量间隔的肺子区域分割（IDLSS）中获得的特征，用于预测肺癌患者在调强放疗（IMRT）后≥2级急性放射性肺炎（ARP）的有效性。（1）材料与方法：回顾性分析126例接受IMRT治疗的非小细胞肺癌患者。通过全肺（WL）与接受增量剂量间隔的五个子区域的交集生成五个肺子区域（SR）。从治疗前计划计算机断层扫描（CT）中提取了总共4610个放射组学特征（RF）和213个剂量组学特征（DF）。生成了六个特征组，包括WL-RF、WL-DF、SR-RF、SR-DF以及WL-RDF和SR-RDF的组合特征集。使用方差阈值进行特征选择，随后进行学生t检验。应用Pearson相关检验去除冗余特征。随后，采用岭回归使用六个特征组开发六个ARP模型。实施30次重采样迭代，通过使用受试者操作特征曲线（AUC）下的面积、准确性、精确性、召回率和F1分数来评估整体模型性能。（2）结果：SR-RDF模型在训练队列（平均AUC：0.98±0.01对0.90±0.02，p<0.001）和测试队列（平均AUC：0.88±0.05对0.80±0.04，p<0.001）中实现了最佳分类性能，并且提供了比WL-RDF模型显著更好的预测能力。同样，SR-DF模型在训练队列（平均AUC：0.88±0.03对0.70±0.030，p<0.001）和测试队列（平均AUC：0.74±0.08对0.65±0.06，p<0.001）中的预测能力明显强于WL-DF模型。相比之下，SR-RF模型仅在训练集中显著优于WL-RF模型（平均AUC：0.93±0.02对0.85±0.03，p<0.001），但在测试集中没有（平均AUC：0.79±0.05对0.77±0.07，p = 0.13）。（3）结论：我们的结果表明，当使用剂量组学或联合放射组学-剂量组学特征时，IDLSS方法提高了对≥2级ARP进行分类的模型性能。
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