ArticleTranslational cancer research2024
Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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4 authors.
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Abstract
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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