Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands.
Amsterdam UMC, University of Amsterdam, LEXOR, Laboratory for Experimental Oncology and Radiobiology, Cancer Center Amsterdam, Amsterdam, Netherlands.
PLoS One. 2018 Nov 15;13(11):e0207362. doi: 10.1371/journal.pone.0207362. eCollection 2018.
In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.
在这项研究中，我们探讨了一种 CT 放射组学方法，以预测胃食管交界癌（EGC）患者个体肝转移对化疗的反应。在 18 例接受化疗的转移性 EGC 患者中，所有肝转移均在治疗前和评估 CT 上进行了三维手动勾画。从预处理 CT 扫描中，每个病变提取了 370 个放射组学特征。随机森林（RF）模型用于区分部分缓解（PR，体积减少>65%，包括 100%体积减少）和完全缓解（CR，仅 100%体积减少）病变与其他病变。RF 模型使用留一法构建，其中单个患者的所有病变均从数据集中删除，并用于训练基于其余患者病变的模型的验证集。此过程对所有患者重复进行，为 PR 和 CR 数据集分别生成了 18 个训练模型和一个验证集。通过相应的曲线下面积（AUC）来评估接收器工作特征（ROC）的模型性能。总共在预处理 CT 上勾画了 196 个肝转移，其中 99 个（51%）病变的体积缩小超过 65%（PR）。在 PR 组中，总共 47 个（RL 的 47%，初始的 24%）病变在 CT 扫描 2 中不再被检测到（CR）。PR 病变的 RF 模型的平均训练 AUC 为 0.79（范围：0.74-0.83），组合验证集的 AUC 为 0.65（95%置信区间：0.57-0.73）。CR 病变的 RF 模型的平均训练 AUC 为 0.87（范围：0.83-0.90），验证集的 AUC 为 0.79（95%置信区间 0.72-0.87）。我们的研究结果表明，肝转移的个体反应在患者内和患者间差异很大。CT 放射组学方法基于治疗前 CT 扫描在区分反应性和非反应性肝转移方面显示出一定的潜力，但需要在独立的患者队列中进一步验证这些发现。
