Affiliations 

  • 1 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
  • 2 Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia
  • 3 Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Sunway City, Malaysia
  • 4 Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
  • 5 Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
  • 6 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia. shuai.li@unimelb.edu.au
Breast Cancer Res, 2023 Oct 25;25(1):127.
PMID: 37880807 DOI: 10.1186/s13058-023-01733-1

Abstract

BACKGROUND: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.

METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.

RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.