Jiang Yong, Zhu Yapeng, Ding Yongli, Lu Xinchang
Orthopaedic Department, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Department of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Front Oncol. 2024 Aug 20;14:1393990. doi: 10.3389/fonc.2024.1393990. eCollection 2024.
To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis and predicting cancer-specific survival (CSS) in the lung metastasis subgroup.
A total of 1,298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing sarcoma, and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3- and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms.
Histologic type, grade, lymph node involvement, tumor size, tumor extension, and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827, respectively. Age, histologic type, surgery at primary site, and grade were identified as the prognostic factors for the CSS. The AUC for the 3- and 5-year CSS were 0.790 and 0.740, respectively. Calibration curves revealed good agreements, and the Hosmer and Lemeshow test identified the models to be well fitted. DCA curves demonstrated that nomograms were clinically useful.
The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis and make a personalized CSS evaluation for the lung metastasis subgroup.
构建并验证列线图，用于预测恶性原发性脊柱骨肿瘤（MPOSN）患者初诊时发生肺转移的概率，以及预测肺转移亚组患者的癌症特异性生存（CSS）情况。
回顾性收集了1298例脊柱原发性骨肉瘤、软骨肉瘤、尤因肉瘤和弦瘤患者。采用最小绝对收缩和选择算子（LASSO）及多因素逻辑回归分析来确定肺转移的预测因素。采用LASSO和多因素Cox分析来确定肺转移亚组患者3年和5年CSS的预后因素。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）来评估列线图的准确性和净效益。
组织学类型、分级、淋巴结受累情况、肿瘤大小、肿瘤侵犯范围及其他部位转移被确定为肺转移的预测因素。训练队列和验证队列的曲线下面积（AUC）分别为0.825和0.827。年龄、组织学类型、原发部位手术情况及分级被确定为CSS的预后因素。CSS 3年和5年的AUC分别为0.790和0.740。校准曲线显示一致性良好，Hosmer和Lemeshow检验表明模型拟合良好。DCA曲线表明列线图具有临床实用性。
我们构建并验证的列线图可为临床医生提供一种快速且用户友好的工具，用于预测初诊MPOSN患者发生肺转移概率，并对肺转移亚组患者进行个性化的CSS评估。
