Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database

被引:0
|
作者
Huang, Xiao [1 ,2 ]
Guo, Jian-Wei [1 ]
Han, Fei [1 ,3 ]
Zhang, Da-Wei [1 ]
机构
[1] Fourth Mil Med Univ, Xijing Hosp, Dept Orthoped, 169 Changle West Rd, Xian 710032, Peoples R China
[2] Lintong Rehabil & Convalescent Ctr Joint Logist Su, Xian, Peoples R China
[3] 990th Hosp Joint Logist Support Force, Dept Orthoped, Zhumadian, Peoples R China
关键词
Bone cancer; nomogram; Surveillance; Epidemiology; and End Results database (SEER database); limb; lung metastasis; CANCER STATISTICS;
D O I
10.21037/tcr-24-570
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. Methods: A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. Results: The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. Conclusions: This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.
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页数:13
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