ArticleScientific reports2022
Establishment of the diagnostic and prognostic nomograms for pancreatic cancer with bone metastasis.
Article in Scientific reports, 2022. 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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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
- Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis.Journal of cancer research and clinical oncology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bone metastasis (BM) is rare in patients with pancreatic cancer (PC), but often neglected at the initial diagnosis and treatment. Bone metastasis is associated with a worse prognosis. This study was aimed to perform a large data analysis to determine the predictors and prognostic factors of BM in PC patients and to develop two nomograms to quantify the risks of BM and the prognosis of PC patients with BM. In the present study, we reviewed and collected the data of patients who were diagnosed as PC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used together to screen and validate the risk factors for BM in PC patients. The independent prognostic factors for PC patients with BM were identified by Cox regression analysis. Finally, two nomograms were established via calibration curves, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). This study included 16,474 PC patients from the SEER database, and 226 of them were diagnosed with BM. The risk factors of BM for PC patients covered age, grade, T stage, N stage, tumor size, and primary site. The independent prognostic factors for PC patients with BM included age, race, grade, surgery, and lung metastasis. The AUC of the diagnostic nomogram was 0.728 in the training set and 0.690 in the testing set. In the prognostic nomogram, the AUC values of 6/12/18 month were 0.781/0.833/0.849 in the training set and 0.738/0.781/0.772 in the testing set. The calibration curve and DCA furtherly indicated the satisfactory clinical consistency of the nomograms. These nomograms could be accurate and personalized tools to predict the incidence of BM in PC patients and the prognosis of PC patients with BM. The nomograms can help clinicians make more personalized and effective treatment choices.
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