Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, People's Republic of China.
United Imaging Intelligence (Beijing) Co., Ltd, Yongteng North Road, Haidian District, Beijing, 100094, People's Republic of China.
BMC Med Imaging. 2023 Oct 2;23(1):147. doi: 10.1186/s12880-023-01077-4.
This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis.
A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models.
Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models.
In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.
本研究旨在基于 CT 和临床特征开发并验证放射组学模型，以预测诊断后 2 年内尤文肉瘤（ES）患者的肺部转移（MT）。
本研究共纳入 143 例经组织病理学诊断为 ES 的患者（训练队列 114 例，验证队列 29 例）。在 CT 和 CT 增强（CTE）图像上沿每个肿瘤的边界手工绘制感兴趣区（ROI），提取放射组学特征。共构建了 6 种不同的模型，包括 3 种放射组学模型（CT、CTE 和 ComB 模型）和 3 种临床-放射组学模型（CT_clinical、CTE_clinical 和 ComB_clinical 模型）。计算受试者工作特征曲线（ROC）下面积（AUC）和准确率来评估不同的模型，并使用 DeLong 检验比较模型的 AUC。
在临床危险因素中，治疗方法在 MT 组和非 MT 组之间存在显著差异（P＜0.01）。6 种模型在预测 ES 患者肺部转移方面表现良好，ComB 模型（训练队列 AUC：0.866/0.852，验证队列 AUC：0.852/0.844）在 6 种模型中获得了最高 AUC。然而，模型的 AUC 之间没有统计学上的显著差异。
在 ES 患者中，基于放射组学特征和临床特征构建的临床-放射组学模型对肺部转移的预测具有良好的效能和准确率。
