Zhang Rihui, Zhu Haiming, Chen Minbin, Sang Weiwei, Lu Ke, Li Zhen, Wang Chunhao, Zhang Lei, Yin Fang-Fang, Yang Zhenyu
Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China.
Department of Radiotherapy & Oncology, The First People's Hospital of Kunshan, Kunshan, Jiangsu, China.
Front Oncol. 2024 Aug 14;14:1419621. doi: 10.3389/fonc.2024.1419621. eCollection 2024.
Radiation therapy (RT) is one of the primary treatment options for early-stage non-small cell lung cancer (ES-NSCLC). Therefore, accurately predicting the overall survival (OS) rate following radiotherapy is crucial for implementing personalized treatment strategies. This work aims to develop a dual-radiomics (DR) model to (1) predict 3-year OS in ES-NSCLC patients receiving RT using pre-treatment CT images, and (2) provide explanations between feature importanceand model prediction performance.
The publicly available TCIA Lung1 dataset with 132 ES-NSCLC patients received RT were studied: 89/43 patients in the under/over 3-year OS group. For each patient, two types of radiomic features were examined: 56 handcrafted radiomic features (HRFs) extracted within gross tumor volume, and 512 image deep features (IDFs) extracted using a pre-trained U-Net encoder. They were combined as inputs to an explainable boosting machine (EBM) model for OS prediction. The EBM's mean absolute scores for HRFs and IDFs were used as feature importance explanations. To evaluate identified feature importance, the DR model was compared with EBM using either (1) key or (2) non-key feature type only. Comparison studies with other models, including supporting vector machine (SVM) and random forest (RF), were also included. The performance was evaluated by the area under the receiver operating characteristic curve (AUCROC), accuracy, sensitivity, and specificity with a 100-fold Monte Carlo cross-validation.
The DR model showed highestperformance in predicting 3-year OS (AUCROC=0.81 ± 0.04), and EBM scores suggested that IDFs showed significantly greater importance (normalized mean score=0.0019) than HRFs (score=0.0008). The comparison studies showed that EBM with key feature type (IDFs-only demonstrated comparable AUCROC results (0.81 ± 0.04), while EBM with non-key feature type (HRFs-only) showed limited AUCROC (0.64 ± 0.10). The results suggested that feature importance score identified by EBM is highly correlated with OS prediction performance. Both SVM and RF models were unable to explain key feature type while showing limited overall AUCROC=0.66 ± 0.07 and 0.77 ± 0.06, respectively. Accuracy, sensitivity, and specificity showed a similar trend.
In conclusion, a DR model was successfully developed to predict ES-NSCLC OS based on pre-treatment CT images. The results suggested that the feature importance from DR model is highly correlated to the model prediction power.
放射治疗（RT）是早期非小细胞肺癌（ES-NSCLC）的主要治疗选择之一。因此，准确预测放疗后的总生存率（OS）对于实施个性化治疗策略至关重要。本研究旨在开发一种双放射组学（DR）模型，以（1）使用治疗前CT图像预测接受放疗的ES-NSCLC患者的3年总生存率，以及（2）解释特征重要性与模型预测性能之间的关系。
研究了公开可用的TCIA Lung1数据集，其中132例接受放疗的ES-NSCLC患者：3年总生存率低于/高于3年的患者分别为89/43例。对于每位患者，检查了两种类型的放射组学特征：在大体肿瘤体积内提取的56个手工放射组学特征（HRF），以及使用预训练的U-Net编码器提取的512个图像深度特征（IDF）。将它们作为输入，输入到一个可解释的增强机器（EBM）模型中进行总生存率预测。EBM对HRF和IDF的平均绝对分数用作特征重要性解释。为了评估确定的特征重要性，仅使用（1）关键或（2）非关键特征类型将DR模型与EBM进行比较。还包括与其他模型的比较研究，包括支持向量机（SVM）和随机森林（RF）。通过接收器操作特征曲线下面积（AUCROC）、准确性、敏感性和特异性进行100倍蒙特卡洛交叉验证来评估性能。
DR模型在预测3年总生存率方面表现出最高性能（AUCROC=0.81±0.04），EBM分数表明IDF的重要性（标准化平均分数=0.0019）明显高于HRF（分数=0.0008）。比较研究表明，具有关键特征类型（仅IDF）的EBM显示出可比的AUCROC结果（0.81±0.04），而具有非关键特征类型（仅HRF）的EBM显示出有限的AUCROC（0.64±0.10）。结果表明，EBM确定的特征重要性分数与总生存率预测性能高度相关。SVM和RF模型都无法解释关键特征类型，同时总体AUCROC分别显示有限的值，分别为0.66±0.07和0.77±0.06。准确性、敏感性和特异性显示出类似的趋势。
总之，成功开发了一种基于治疗前CT图像预测ES-NSCLC总生存率的DR模型。结果表明，DR模型的特征重要性与模型预测能力高度相关。
