From Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Zhejiang Province 317000, China.
Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Radiother Oncol. 2023 Jun;183:109637. doi: 10.1016/j.radonc.2023.109637. Epub 2023 Mar 22.
Although adaptive radiotherapy (ART) has many advantages, ART is not universal in the clinical appliance due to the consumption of a lot of labor, and economic burden. It is necessary to explore a CT stimulation-based radiomics model for screening who can get more benefits from ART in locally advanced non-small cell lung cancer (NSCLC) patients.
183 cases of NSCLC patients receiving concurrent chemoradiotherapy with an adaptive approach were enrolled as a primary cohort, while 28 cases from another hospital served as an independent external validation cohort. Tumor regression assessment was conducted based on GTV reduction (Criteria A) or according to RECIST Version 1.1(Criteria B). The radiomics features were extracted by the "PyRadiomics" package and further screened by the LASSO method. Then, logistic regression was used to establish the model. Bootstrap and external validation were applied to verify the stability of the model. The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the radiomics model. Dose-volume histograms were quantitatively compared between the initial and composite ART plans. Clinical endpoints included overall survival (OS) and progression-free survival (PFS).
There were no significant differences in clinical features between tumor regression-resistant (RR) and tumor regression-sensitivity (RS) groups. The AUC values of the Criteria A model and Criteria B model were 0.767 and 0.771, respectively. Bootstrapping validation and external validation confirmed the stability of models. In all patients, there was a significant benefit of ART in the lung, heart, cord, and esophagus compared to non-ART, particularly in RS patients. Furthermore, PFS and OS from ART were significantly longer in RS as defined by Criterion B than in RR patients with the same ART application.
CT-based radiomics can screen out the patients who can gain more benefits from ART, which contribute to guiding and popularizing the application of ART strategy in the clinic within economic benefits and feasibility.
尽管自适应放疗（ART）有许多优点，但由于其耗费大量劳动力和经济负担，ART 在临床应用中并不普遍。有必要探索一种基于 CT 刺激的放射组学模型，以筛选出在局部晚期非小细胞肺癌（NSCLC）患者中谁能从 ART 中获益更多。
183 例接受自适应同步放化疗的 NSCLC 患者被纳入主要队列，另一家医院的 28 例患者作为独立的外部验证队列。根据 GTV 减少（标准 A）或根据 RECIST 版本 1.1（标准 B）进行肿瘤退缩评估。使用“PyRadiomics”包提取放射组学特征，并进一步通过 LASSO 方法进行筛选。然后，使用逻辑回归建立模型。应用 bootstrap 和外部验证来验证模型的稳定性。绘制受试者工作特征（ROC）曲线评估放射组学模型的预测效能。比较初始和综合 ART 计划的剂量-体积直方图。临床终点包括总生存期（OS）和无进展生存期（PFS）。
在肿瘤退缩抵抗（RR）和肿瘤退缩敏感（RS）组之间，临床特征无显著差异。标准 A 模型和标准 B 模型的 AUC 值分别为 0.767 和 0.771。Bootstrap 验证和外部验证证实了模型的稳定性。在所有患者中，与非 ART 相比，ART 在肺、心脏、脊髓和食管中均有显著获益，尤其是在 RS 患者中。此外，与 RR 患者相比，RS 患者根据标准 B 定义接受 ART 后 PFS 和 OS 明显更长。
基于 CT 的放射组学可以筛选出能从 ART 中获益更多的患者，有助于在经济效益和可行性的基础上指导和推广 ART 策略在临床中的应用。
