Department of Radiology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China.
Eur Radiol. 2022 Mar;32(3):1538-1547. doi: 10.1007/s00330-021-08277-y. Epub 2021 Sep 26.
The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both.
This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.
The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846).
Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future.
The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
本研究旨在评估基于肺部治疗前 CT 图像的放射组学特征预测接受一线化疗、靶向治疗或两者联合治疗的非小细胞肺癌（NSCLC）患者肿瘤反应的有效性。
本回顾性研究纳入了 322 例接受一线化疗、靶向治疗或两者联合治疗的 NSCLC 患者。其中 224 例患者被随机分配到一个队列中以帮助建立放射组学特征。从每位患者的 CT 扫描中获得了总共 1946 个放射组学特征。使用最小冗余最大相关性（MRMR）特征排序方法选择排名最高的特征，并使用随机森林（RF）分类器构建一个轻量级放射组学特征。从排名最高的特征中进一步确定独立预测（IP）特征（AUC＞0.6，p 值＜0.05），并使用 RF 分类器构建一个经优化的放射组学特征。在由其余 98 例患者组成的验证队列中对其预测性能进行了测试。
由 15 个排名最高的特征构建的初始轻量级放射组学特征的 AUC 为 0.721（95%CI，0.619-0.823）。进一步确定了 6 个 IP 特征并构建了一个经过优化的放射组学特征后，其 AUC 为 0.746（95%CI，0.646-0.846）。
基于治疗前 CT 扫描的放射组学特征可准确预测接受一线化疗或靶向治疗的 NSCLC 患者的肿瘤反应。放射组学特征可能成为未来有前途的预后成像生物标志物。
从 NSCLC 患者的基线 CT 图像中提取的放射组学特征可以预测一线化疗、靶向治疗或两种治疗方法的反应，AUC=0.746（95%CI，0.646-0.846）。放射组学特征可作为放射学中定量分析的新生物标志物，这可能为癌症患者的决策和个体化治疗提供价值。
