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Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■研究纹理分析和机器学习在肝转移患者介入前锥形束计算机断层扫描（CBCT）图像上预测经动脉放射栓塞（TARE）治疗反应的潜力。
■在这项IRB批准的回顾性单中心研究中，36例患者共104例肝转移（男性占56％，平均年龄61.1±13岁）在TARE之前接受CBCT检查，并在治疗后6个月进行随访成像。根据RECIST版本1.1评估治疗反应，并将其分为疾病控制（部分反应/稳定的疾病）与疾病进展（进行性疾病）。目标病变分割后，使用pyRadiomics软件包提取了对应于七个不同特征类的104个影像组学特征。在降维之后,在定制人工神经网络(ANN)上执行机器学习分类。对先前未看到的测试数据集进行10倍交叉验证。
■来自TARE的平均施用累积活性为1.6Gbq(±0.5Gbq)。在平均5.9±0.8个月的随访中，82％的转移灶实现了疾病控制。降维后，104个(15%)纹理分析特征中的15个仍用于进一步分析。在以前看不见的一组肝转移瘤中，多层感知器ANN的灵敏度为94.2％，特异性为67.7%，受试者工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具有使用肝转移患者的治疗前CBCT图像来预测对TARE的治疗反应的潜力。
■在这项IRB批准的回顾性单中心研究中，36例患者共104例肝转移（男性占56％，平均年龄61.1±13岁）在TARE之前接受CBCT检查，并在治疗后6个月进行随访成像。根据RECIST版本1.1评估治疗反应，并将其分为疾病控制（部分反应/稳定的疾病）与疾病进展（进行性疾病）。目标病变分割后，使用pyRadiomics软件包提取了对应于七个不同特征类的104个影像组学特征。在降维之后,在定制人工神经网络(ANN)上执行机器学习分类。对先前未看到的测试数据集进行10倍交叉验证。
■来自TARE的平均施用累积活性为1.6Gbq(±0.5Gbq)。在平均5.9±0.8个月的随访中，82％的转移灶实现了疾病控制。降维后，104个(15%)纹理分析特征中的15个仍用于进一步分析。在以前看不见的一组肝转移瘤中，多层感知器ANN的灵敏度为94.2％，特异性为67.7%，受试者工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具有使用肝转移患者的治疗前CBCT图像来预测对TARE的治疗反应的潜力。
