Kobe Adrian, Zgraggen Juliana, Messmer Florian, Puippe Gilbert, Sartoretti Thomas, Alkadhi Hatem, Pfammatter Thomas, Mannil Manoj
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Clinic of Radiology, University Hospital Münster, University of Münster, Münster, Germany.
Eur J Radiol Open. 2021 Aug 30;8:100375. doi: 10.1016/j.ejro.2021.100375. eCollection 2021.
To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases.
In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed.
The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85.
Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy.
探讨纹理分析和机器学习在预测肝转移瘤患者经动脉放射性栓塞（TARE）治疗前锥形束计算机断层扫描（CBCT）图像上的治疗反应方面的潜力。
在这项经机构审查委员会批准的回顾性单中心研究中，36例共有104个肝转移瘤的患者（56%为男性，平均年龄61.1±13岁）在TARE治疗前接受了CBCT检查，并在治疗后6个月进行了随访成像。根据实体瘤疗效评价标准（RECIST）1.1版评估治疗反应，并将其分为疾病控制（部分缓解/疾病稳定）与疾病进展（疾病进展）。在目标病灶分割后，使用pyRadiomics软件包提取了与七种不同特征类别相对应的104个影像组学特征。在降维后，在定制的人工神经网络（ANN）上进行机器学习分类。对一个之前未见过的测试数据集进行十折交叉验证。
TARE的平均给药累积活度为1.6GBq（±0.5GBq）。在平均随访5.9±0.8个月时，82%的转移瘤实现了疾病控制。降维后，104个（15%）纹理分析特征中的15个保留用于进一步分析。在一组之前未见过的肝转移瘤上，多层感知器人工神经网络的敏感性为94.2%，特异性为67.7%，受试者操作特征曲线下面积为0.85。
我们的研究表明，基于纹理分析的机器学习可能有潜力使用肝转移瘤患者治疗前的CBCT图像高精度地预测TARE的治疗反应。
