Home CONTACT Neoplasma Ahead of print Neoplasma Vol.69, No.3, p.708–722, 2022

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Neoplasma Vol.69, No.3, p.708–722, 2022

Title: Development of a model for predicting risk from breast interval cancer in the female population in the Republic of Croatia
Author: Romana Tandara Haček, Nataša Antoljak, Marijan Erceg

Abstract: There are several risk prediction models for screen-detected breast cancer but to the best of our knowledge, none for predicting risk from the interval cancer in breast cancer screening. The challenge for developing such a model was that the risk factors for both cancers appear to be similar, but the effects of interval cancer on women‘s health are more severe due to its higher biological aggressiveness. Our model is based on risk factors identified in the female population in the Republic of Croatia. Anonymized data from 472,395 women who participated in the National Program for Early Detection of Breast Cancer during the first three cycles of the program (October 2006-May 2014) were used. Cancer data from the Breast Cancer Screening Registry were linked by the data linkage method with data from the Cancer Registry of the Republic of Croatia. A total of 789 women with interval cancer and 3,530 women with screen-detected cancer were identified. Multivariate logistic regression in R was used to model the difference between participants with screen-detected cancer and those with interval cancer, using the general linear model (glm) function. The variables used for the analysis were selected using the all subset regression analysis method. The criterion of the least complexity parameter, the Cp-Mallows index, was chosen. Three variables were found to be statistically significant in the model: breast tissue density (p = 0.038), hormone replacement therapy (p = 0.034), and a first-degree family history of breast cancer (p

Keywords: cancer breast screening; interval breast cancer; risk prediction model
Published online: 10-Mar-2022
Year: 2022, Volume: 69, Issue: 3 Page From: 708, Page To: 722
doi:10.4149/neo_2022_211118N1642


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