Department of Diagnostic Imaging, Institute of Mother and Child, Warsaw, 01-211, Poland.
WIT Academy, Warsaw, 01-447, Poland.
BMC Pediatr. 2024 Jun 3;24(1):382. doi: 10.1186/s12887-024-04858-0.
Osteosarcoma is the most common primary malignant bone tumour in children and adolescents. Lungs are the most frequent and often the only site of metastatic disease. The presence of pulmonary metastases is a significant unfavourable prognostic factor. Thoracotomy is strongly recommended in these patients, while computed tomography (CT) remains the gold imaging standard. The purpose of our study was to create tools for the CT-based qualification for thoracotomy in osteosarcoma patients in order to reduce the rate of useless thoracotomies.
Sixty-four osteosarcoma paediatric patients suspected of lung metastases on CT and their first-time thoracotomies (n = 100) were included in this retrospective analysis. All CT scans were analysed using a compartmental evaluation method based on the number and size of nodules. Calcification and location of lung lesions were also analysed. Inter-observer reliability between two experienced radiologists was assessed. The CT findings were then correlated with the histopathological results of thoracotomies. Various multivariate predictive models (logistic regression, classification tree and random forest) were built and predictors of lung metastases were identified.
All applied models proved that calcified nodules on the preoperative CT scan best predict the presence of pulmonary metastases. The rating of the operated lung on the preoperative CT scan, dependent on the number and size of nodules, and the total number of nodules on this scan were also found to be important predictors. All three models achieved a relatively high sensitivity (72-92%), positive predictive value (81-90%) and accuracy (74-79%). The positive predictive value of each model was higher than of the qualification for thoracotomy performed at the time of treatment. Inter-observer reliability was at least substantial for qualitative variables and excellent for quantitative variables.
The multivariate models built and tested in our study may be useful in the qualification of osteosarcoma patients for metastasectomy through thoracotomy and may contribute to reducing the rate of unnecessary invasive procedures in the future.
骨肉瘤是儿童和青少年中最常见的原发性恶性骨肿瘤。肺部是最常见的转移部位，也是最常发生转移的部位。肺部转移的存在是一个显著的预后不良因素。在这些患者中，强烈推荐进行开胸手术，而计算机断层扫描（CT）仍然是金成像标准。我们的研究目的是创建基于 CT 的工具，以对骨肉瘤患者进行开胸手术资格评估，从而降低不必要开胸手术的比例。
本回顾性分析纳入了 64 名疑似 CT 显示肺部转移的骨肉瘤患儿及其首次开胸手术（n=100）。所有 CT 扫描均采用基于结节数量和大小的分区评估方法进行分析。同时还分析了钙化和肺部病变的位置。评估了两位经验丰富的放射科医生之间的观察者间可靠性。然后将 CT 结果与开胸手术的组织病理学结果进行相关性分析。构建了各种多变量预测模型（逻辑回归、分类树和随机森林），并确定了肺转移的预测因子。
所有应用的模型都证明，术前 CT 扫描上的钙化结节最能预测肺部转移的存在。术前 CT 扫描上的肺部病变评分、依赖于结节数量和大小的评分以及该扫描上的总结节数量也被发现是重要的预测因子。所有三种模型的敏感性（72-92%）、阳性预测值（81-90%）和准确性（74-79%）都相对较高。每个模型的阳性预测值都高于治疗时进行的开胸手术资格评估。定性变量的观察者间可靠性至少为中等，定量变量的观察者间可靠性为优秀。
我们研究中构建和测试的多变量模型可能有助于骨肉瘤患者开胸手术资格评估，并有助于未来降低不必要的侵入性操作率。
