PDF(Sci-hub)
Abstract：
UNASSIGNED: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.
UNASSIGNED: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.
UNASSIGNED: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.
UNASSIGNED: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.
UNASSIGNED: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.
UNASSIGNED: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.
UNASSIGNED: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.
摘要：
■确定基于术前计算机断层扫描（CT）的影像组学分析是否可以预测脊柱骨巨细胞瘤（GCTB）的术后早期复发。
■在回顾性审查中，2008年3月至2018年2月，62例经病理证实为脊柱GCTB，最少随访24个月。已确定。平均随访73.7个月(范围，28.7-152.1个月）。临床信息包括年龄,性别,病变位置,多椎体受累，和手术方法，已获得。检索手术前获得的CT图像以进行影像组学分析。对于每种情况，手动勾勒出感兴趣的肿瘤区域(ROI)，共提取了107个影像组学特征。通过使用支持向量机(SVM)的顺序选择过程来选择特征，然后用高斯核构建分类模型。通过ROC分析评估复发和未复发组之间的区别，使用10倍交叉验证。
■在62名患者中，17例复发，复发率为27.4%。两组之间的临床信息均无明显差异。与接受TES（6/26=23.1％）或病灶内脊椎切除术（5/20=25％）的患者相比，接受刮宫的患者的复发率更高（6/16=37.5％）。最终的影像组学模型是使用10个选定的特征建立的，其准确度为89%，AUC为0.78。
■基于术前CT开发的影像组学模型可以实现较高的准确性，以预测脊柱GCTB的复发。早期复发风险高的患者应更积极地治疗，以尽量减少复发。
■在回顾性审查中，2008年3月至2018年2月，62例经病理证实为脊柱GCTB，最少随访24个月。已确定。平均随访73.7个月(范围，28.7-152.1个月）。临床信息包括年龄,性别,病变位置,多椎体受累，和手术方法，已获得。检索手术前获得的CT图像以进行影像组学分析。对于每种情况，手动勾勒出感兴趣的肿瘤区域(ROI)，共提取了107个影像组学特征。通过使用支持向量机(SVM)的顺序选择过程来选择特征，然后用高斯核构建分类模型。通过ROC分析评估复发和未复发组之间的区别，使用10倍交叉验证。
■在62名患者中，17例复发，复发率为27.4%。两组之间的临床信息均无明显差异。与接受TES（6/26=23.1％）或病灶内脊椎切除术（5/20=25％）的患者相比，接受刮宫的患者的复发率更高（6/16=37.5％）。最终的影像组学模型是使用10个选定的特征建立的，其准确度为89%，AUC为0.78。
■基于术前CT开发的影像组学模型可以实现较高的准确性，以预测脊柱GCTB的复发。早期复发风险高的患者应更积极地治疗，以尽量减少复发。
