Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jan 2, 2023
Open Peer Review Period: Jan 2, 2023 - Jan 16, 2023
Date Accepted: May 8, 2023
Date Submitted to PubMed: May 11, 2023
(closed for review but you can still tweet)
Epidemiology and a predictive model of prognosis index based on machine learning in primary breast lymphoma: Population-Based Study
ABSTRACT
Background:
Primary breast lymphoma (PBL) is a rare disease whose epidemiological features, treatment principles, and factors used for the patients’ prognosis remain controversial. The aim of this study was to explore the epidemiology of PBL and to develop a better model to predict prognosis for PBL patients.
Methods:
The annual incidence of PBL was extracted from the SEER database between the years 1975 and 2019 to examine disease occurrence trends using Joinpoint software (version 4.9). We enrolled data from 1251 female PBL patients from the SEER database for survival analysis. Univariable and multivariable analyses were performed to explore independent prognostic factors for overall survival (OS) and disease-specific survival (DSS) of PBL patients. Eight machine learning algorithms were developed to predict the five-year survival of PBL patients.
Results:
The overall incidence of PBL increased drastically between 1975 and 2004 followed by a significant downward trend in incidence around 2004 with an average annual percent change (AAPC) of -0.8 (-1.1, -0.6). Disparities in trends of PBL exist by age and race. We also identified that the risk of death from PBL is multifactorial and includes patient factors and treatment factors. The patients diagnosed between 2007-2015 had a significant risk reduction of mortality compared to those diagnosed between 1983-1990. The Gradient booster model outperforms other models, with 0.752 for sensitivity and 0.817 for AUC. Conclusion: The incidence of PBL started demonstrating a tendency to decrease after 2004. In recent years, the prognosis of PBL patients has been remarkable improved. The Gradient booster model had a promising performance. This model can help clinicians identify the prognosis of PBL patients early and therefore improve clinical outcome by changing management strategies and patient healthcare.
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Copyright
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