Luo Zhendong, Ai Tao, Liu Zhiqiang, He Litong, Hou Yanzhen, Li Yulin, Zhou Ziyan, Shen Xinping
Department of Radiology, The University of Hong Kong - Shenzhen Hospital, Shenzhen, PR China.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
J Bone Oncol. 2025 May 9;52:100687. doi: 10.1016/j.jbo.2025.100687. eCollection 2025 Jun.
Osteosarcoma is a highly malignant bone tumor with a high incidence of lung metastases (LM), significantly impacting the 5-year survival rate of patients. This study aims to predict lung metastasis in osteosarcoma based on computed tomography angiography (CTA) signs.
A retrospective study was conducted involving 89 consecutive patients with osteosarcoma. Clinical features and CTA signs, including age, gender, laterality, primary site, type of bone destruction, T stage, periosteal reaction, tumor length, bone marrow involved length, vascular wrapping, and intratumoral vascular network, were evaluated. Univariate and multivariate logistic regression analyses were used to identify risk factors for LM, followed by receiver operating characteristic (ROC) curve analysis.
The vascular wrapping and intratumoral vascular network signs were more frequently observed in LM in patients with osteosarcoma ( < 0.05). The intratumoral vascular network remained an independent risk factor in multivariable regression analysis. ROC curve analysis demonstrated that the area under the curve (AUC) of the logistic regression model was 0.804, indicating good predictive accuracy.
Preliminary findings suggest that CTA signs, particularly vascular wrapping and the intratumoral vascular network, may have potential utility in predicting lung metastasis (LM) in osteosarcoma patients. The intratumoral vascular network, in particular, was identified as an independent risk factor.
骨肉瘤是一种高度恶性的骨肿瘤,肺转移(LM)发生率高,严重影响患者的5年生存率。本研究旨在基于计算机断层扫描血管造影(CTA)征象预测骨肉瘤的肺转移。
对89例连续的骨肉瘤患者进行回顾性研究。评估临床特征和CTA征象,包括年龄、性别、部位、原发部位、骨破坏类型、T分期、骨膜反应、肿瘤长度、骨髓受累长度、血管包绕和瘤内血管网络。采用单因素和多因素逻辑回归分析确定肺转移的危险因素,随后进行受试者工作特征(ROC)曲线分析。
骨肉瘤肺转移患者中血管包绕和瘤内血管网络征象更为常见(<0.05)。瘤内血管网络在多变量回归分析中仍是独立的危险因素。ROC曲线分析表明,逻辑回归模型的曲线下面积(AUC)为0.804,提示预测准确性良好。
初步研究结果表明,CTA征象,尤其是血管包绕和瘤内血管网络,可能在预测骨肉瘤患者肺转移(LM)方面具有潜在应用价值。特别是瘤内血管网络被确定为独立危险因素。