Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China.
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China.
J Orthop Surg (Hong Kong). 2023 May-Aug;31(2):10225536231177102. doi: 10.1177/10225536231177102.
Metastasis is one of the most significant prognostic factors in osteosarcoma (OS). The goal of this study was to construct a clinical prediction model for OS patients in a population cohort and to evaluate the factors influencing the occurrence of pulmonary metastasis.
We collected data from 612 patients with osteosarcoma (OS), and 103 clinical indicators were collected. After the data were filtered, the patients were randomly divided into training and validation cohorts by using random sampling. The training cohort included 191 patients with pulmonary metastasis in OS and 126 patients with non-pulmonary metastasis, and the validation cohort included 50 patients with pulmonary metastasis in OS and 57 patients with non-pulmonary metastasis. Univariate logistics regression analysis, LASSO regression analysis and multivariate logistic regression analysis were performed to identify potential risk factors for pulmonary metastasis in patients with osteosarcoma. A nomogram was developed that included risk influencing variables selected by multivariable analysis, and used the concordance index (C-index) and calibration curve to validate the model. Receiver operating characteristic curve (ROC), decision analysis curve (DCA) and clinical impact curve (CIC) were employed to assess the model. In addition, we used a predictive model on the validation cohort.
Logistic regression analysis was used to identify independent predictors [N Stage + Alkaline phosphatase (ALP)+Thyroid stimulating hormone (TSH)+Free triiodothyronine (FT3)]. A nomogram was constructed to predict the risk of pulmonary metastasis in patients with osteosarcoma. The performance was evaluated by the concordance index (C-index) and calibration curve. The ROC curve provides the predictive power of the nomogram (AUC = 0.701 in the training cohort, AUC = 0.786 in the training cohort). Decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the clinical value of the nomogram and higher overall net benefits.
Our study can help clinicians effectively predict the risk of lung metastases in osteosarcoma with more readily available clinical indicators, provide more personalized diagnosis and treatment guidance, and improve the prognosis of patients.
A new risk model was constructed to predict the pulmonary metastasis in patients with osteosarcoma based on multiple machine learning.
转移是骨肉瘤（OS）最重要的预后因素之一。本研究的目的是构建人群队列中 OS 患者的临床预测模型，并评估影响肺转移发生的因素。
我们收集了 612 例骨肉瘤（OS）患者的数据，收集了 103 个临床指标。数据过滤后，采用随机抽样法将患者随机分为训练集和验证集。训练集包括 191 例 OS 伴肺转移患者和 126 例无肺转移患者，验证集包括 50 例 OS 伴肺转移患者和 57 例无肺转移患者。采用单因素逻辑回归分析、LASSO 回归分析和多因素逻辑回归分析，筛选出骨肉瘤患者肺转移的潜在危险因素。建立了一个包含多变量分析选择的风险影响因素的列线图，并使用一致性指数（C-index）和校准曲线对模型进行验证。采用受试者工作特征曲线（ROC）、决策分析曲线（DCA）和临床影响曲线（CIC）评估模型。此外，我们还在验证队列中使用了一个预测模型。
采用逻辑回归分析筛选出独立预测因子[N 期+碱性磷酸酶（ALP）+促甲状腺激素（TSH）+游离三碘甲状腺原氨酸（FT3）]。构建了一个列线图来预测骨肉瘤患者肺转移的风险。通过一致性指数（C-index）和校准曲线评估了该列线图的性能。ROC 曲线提供了列线图的预测能力（训练集 AUC=0.701，验证集 AUC=0.786）。决策曲线分析（DCA）和临床影响曲线（CIC）表明了列线图的临床价值和更高的总体净收益。
本研究可以帮助临床医生利用更易获得的临床指标有效预测骨肉瘤肺转移的风险，提供更具个性化的诊断和治疗指导，改善患者的预后。
本研究构建了一个基于多机器学习的新风险模型，用于预测骨肉瘤患者的肺转移。
该模型使用了易于获得的临床指标，具有良好的预测性能。
该模型可以帮助临床医生更准确地预测骨肉瘤患者的肺转移风险，提供更个性化的诊断和治疗建议。
