{"title":"Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.","authors":"Xiao Huang, Jian-Wei Guo, Fei Han, Da-Wei Zhang","doi":"10.21037/tcr-24-570","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4763-4774"},"PeriodicalIF":1.5000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483498/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-24-570","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/21 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.