Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
BMC Med Imaging. 2022 Jul 26;22(1):131. doi: 10.1186/s12880-022-00859-6.
To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma.
A retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma who received the chemotherapy regimen of epirubicin combined with ifosfamide was performed, and efficacy was evaluated by Recist1.1. ROIs (1 or 2) were selected for each patient. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a non-progressive group (n = 57); the latter included a stable group (n = 34) and a partial response group (n = 23). Information on lung metastases was extracted from CECT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better performance in all radiomics models. A receiver operating characteristic curve was plotted to evaluate the predictive performance of the radiomics model.
In total, 851 CECT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were then used to construct a radiomics model. Areas under the curves of the model for predicting lesion progression were 0.917 and 0.856 in training and testing groups, respectively.
The model established based on the radiomics features of CECT before treatment has certain predictive value for assessing the efficacy of chemotherapy for patients with soft tissue sarcoma lung metastases.
探讨对比增强 CT(CECT)放射组学特征在预测蒽环类药物联合异环磷酰胺治疗软组织肉瘤肺转移患者疗效中的价值。
回顾性分析 51 例接受蒽环类药物联合异环磷酰胺化疗方案的软组织肉瘤肺转移患者,采用 RECIST1.1 标准评估疗效。为每位患者选择 1 或 2 个 ROI。将肺转移灶作为靶病灶(共 86 个靶病灶),将患者分为进展组(n=29)和非进展组(n=57);后者包括稳定组(n=34)和部分缓解组(n=23)。化疗前从 CECT 图像中提取肺转移灶信息,所有病灶均由 ITK-SNAP 软件手动或半自动勾画。决策树分类器在所有放射组学模型中表现出更好的性能。绘制受试者工作特征曲线评估放射组学模型的预测性能。
共提取每个靶病灶的 851 个 CECT 放射组学特征,最终简化为 2 个放射组学特征,然后用于构建放射组学模型。在训练组和测试组中,模型预测病灶进展的曲线下面积分别为 0.917 和 0.856。
基于治疗前 CECT 放射组学特征建立的模型对评估软组织肉瘤肺转移患者化疗疗效具有一定的预测价值。