Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients

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.

本研究旨在探究CT放射组学（CT radiomics）方法，以预测食管胃交界癌（esophagogastric cancer, EGC）患者单发肝转移灶的化疗应答情况。针对18例接受化疗的转移性EGC患者，于治疗前及疗效评估期的CT影像中，所有肝转移灶均经人工三维勾画。从患者治疗前的CT扫描影像中，为每个转移灶提取370项放射组学特征。构建随机森林（Random Forest, RF）模型，以区分部分缓解（partial responding, PR，定义为体积缩小>65%，含体积缩小100%）、完全缓解（complete remission, CR，仅指体积缩小100%）的转移灶与其余转移灶。RF模型采用留一法构建策略：将单例患者的所有转移灶从数据集中剔除，将其作为验证集，用以验证基于其余患者转移灶训练得到的模型。该流程针对所有患者重复执行，最终针对PR及CR数据集各得到18个训练完成的模型与对应的验证集。模型性能通过受试者工作特征曲线（receiver operating characteristics, ROC）及其曲线下面积（area under the curve, AUC）进行评估。本研究共在治疗前CT影像中共勾画196个肝转移灶，其中99个（占比51%）转移灶体积缩小超过65%，归类为PR。在PR组转移灶中，共有47个（占PR组的47%，占初始总转移灶的24%）在第二次CT复查中未被检出，归类为CR。针对PR转移灶的RF模型，其训练集平均AUC均值为0.79（范围：0.74~0.83），联合验证集的AUC均值为0.65（95%置信区间：0.57~0.73）。针对CR转移灶的RF模型，其训练集平均AUC为0.87（范围：0.83~0.90），验证集AUC均值为0.79（95%置信区间：0.72~0.87）。本研究结果表明，肝转移灶的个体应答情况在患者内部及患者之间均存在显著差异。基于治疗前CT影像的CT放射组学方法，在区分应答与非应答肝转移灶方面展现出应用潜力，但仍需在独立患者队列中开展进一步验证，以确认本研究结果的可靠性。