Yan Meng, Zhang Zhen, Tian Jia, Yu Jiaqi, Dekker Andre, Ruysscher Dirk de, Wee Leonard, Zhao Lujun
Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Radiat Oncol. 2025 Jan 17;20(1):9. doi: 10.1186/s13014-025-02583-1.
Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.
A total of 661 patients with LA-NSCLC treated with definitive radiotherapy in combination with chemotherapy were enrolled in this study, with 292 patients in the training set, 57 patients from the same hospital from January to December 2017 as an independent test set (test-set-1), 83 patients from a multi-institutional prospective clinical trial data set (RTOG0617) as test-set-2, and 229 patients from a Dutch radiotherapy center as test-set-3. Tumor-based radiomic features and whole lung-based radiomic features were extracted from primary tumor and whole lungs (excluding the primary tumor) delineations in planning CT images. Feature selection of radiomic features was done by the least absolute shrinkage (LASSO) method embedded with a Cox proportional hazards (CPH) model with 5-fold cross-internal validation, with 1000 bootstrap samples. Radiomics prognostic scores (RS) were calculated by CPH regression based on selected features. Three models based on a tumor RS, and a lung RS separately and their combinations were constructed. The Harrell concordance index (C-index) and calibration curves were used to evaluate the discrimination and calibration performance. Patients were stratified into high and low risk groups based on median RS, and a log-rank test was performed.
The discrimination ability of lung- and tumor-based radiomics model was similar in terms of C-index, 0.69 vs. 0.68 in training set, 0.68 vs. 0.66 in test-set-1, 0.61 vs. 0.62 in test-set-2, 0.65 vs. 0.64 in test-set-3. The combination of tumor- and lung-based radiomics model performed best, with C-index of 0.71 in training set, 0.70 in test-set-1, 0.69 in test-set-2, and 0.68 in test-set-3. The calibration curve showed good agreement between predicted values and actual values. Patients were well stratified in training set, test-set-1 and test-set-3. In test-set-2, it was only whole lung-based RS that could stratify patients well and tumor-based RS performed bad.
Lung- and tumor-based radiomic features have the power to predict OS in LA-NSCLC. The combination of tumor- and lung-based radiomic features can achieve optimal performance.
多项研究表明，肺癌患者的肺组织异质性与总生存期（OS）相关。然而，两者之间的定量关系尚不清楚。本研究旨在探讨基于全肺和基于肿瘤的放射组学对接受根治性放疗的局部晚期非小细胞肺癌（LA-NSCLC）患者总生存期的预后价值。
本研究共纳入661例接受根治性放疗联合化疗的LA-NSCLC患者，其中292例患者纳入训练集，2017年1月至12月来自同一医院的57例患者作为独立测试集（测试集-1），来自多机构前瞻性临床试验数据集（RTOG0617）的83例患者作为测试集-2，来自荷兰放疗中心的229例患者作为测试集-3。在计划CT图像中，从原发肿瘤和全肺（不包括原发肿瘤）的轮廓中提取基于肿瘤的放射组学特征和基于全肺的放射组学特征。采用嵌入Cox比例风险（CPH）模型的最小绝对收缩（LASSO）方法进行放射组学特征选择，并进行5倍交叉内部验证，共1000个自助抽样样本。基于选定特征，通过CPH回归计算放射组学预后评分（RS）。分别构建基于肿瘤RS、肺RS及其组合的三个模型。采用Harrell一致性指数（C-index）和校准曲线评估判别性能和校准性能。根据RS中位数将患者分为高风险组和低风险组，并进行对数秩检验。
基于肺和基于肿瘤的放射组学模型在C-index方面的判别能力相似，训练集中分别为0.69和0.68，测试集-1中为0.68和0.66，测试集-2中为0.61和0.62，测试集-3中为0.65和0.64。基于肿瘤和基于肺的放射组学模型的组合表现最佳，训练集中C-index为0.71，测试集-1中为0.70，测试集-2中为0.69，测试集-3中为0.68。校准曲线显示预测值与实际值之间具有良好的一致性。患者在训练集、测试集-1和测试集-3中分层良好。在测试集-2中，只有基于全肺的RS能够很好地对患者进行分层，而基于肿瘤的RS表现不佳。
基于肺和基于肿瘤的放射组学特征能够预测LA-NSCLC患者的总生存期。基于肿瘤和基于肺的放射组学特征的组合可以实现最佳性能。
