Department of Orthopaedic Surgery, Sir Run Run Shaw Hospital, Medical College of Zhejiang University, Hangzhou, China.
Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, China.
Sci Rep. 2021 Mar 10;11(1):5542. doi: 10.1038/s41598-021-85223-4.
Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.
骨肉瘤是最常见的骨恶性肿瘤，在儿童和青少年中的发病率最高。生存率预测对于改善预后和制定治疗计划很重要。然而，目前仍然没有用于骨肉瘤的高准确率预测模型。因此，我们旨在使用极端梯度增强（XGBoost）这一大型机器学习算法，构建一个用于预测骨肉瘤患者 5 年生存率的人工智能（AI）模型。我们从监测、流行病学和最终结果（SEER）研究数据库中确定了骨肉瘤病例，并排除了不合格的样本。研究人群为 835 例，分为训练集（n=668）和验证集（n=167）。通过生存分析选择特征来构建模型。通过接收者操作特征（ROC）曲线和决策曲线分析来评估预测。该预测模型在训练集（AUC=0.977）和验证集（AUC=0.911）中的预测准确性都非常出色。决策曲线分析证明该模型可用于支持临床决策。XGBoost 是一种用于预测骨肉瘤患者 5 年生存率的有效算法。我们的预测模型具有出色的准确性，因此在临床环境中很有用。
