A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment

Gerson Africano, Otso Arponen, Antti Sassi, Mirja Karivaara-Mäkelä, Kirsi Holli-Helenius, Irina Rinta-Kiikka, Anna-Leena Lääperi, Said Pertuz

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem. In this work we perform a comparison between five different methods suggested in the literature for automated ROI selection, including the whole breast (WB), the maximum squared (MS), the retro-areolar region (RA), the lattice-based (LB), and the polar-based (PB) selection methods. For the experiments, we built a retrospective dataset of 896 screening mammograms from 224 women (112 cases and 112 healthy controls). The performance of each ROI selection method was measured in terms of the area under the curve (AUC) values. The AUC values varied between 0.55 and 0.79 depending on the method and experimental settings. The best performance on an independent test set was achieved by the MS method (AUC of 0.59, 95% CI: 0.55-0.64). This method is fully-automated and does not require adjusting hyper-parameters. Based on our results, we prompt the use of the MS method for ROI selection in the computerized parenchymal analysis for breast cancer risk assessment.
Original languageEnglish
Title of host publication2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
PublisherIEEE
Pages1136-1139
Number of pages4
ISBN (Electronic)978-1-7281-1990-8
ISBN (Print)978-1-7281-1991-5
DOIs
Publication statusPublished - 2020
Publication typeA4 Article in conference proceedings
EventAnnual International Conference of the IEEE Engineering in Medicine and Biology Society - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42nd

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
ISSN (Print)2375-7477
ISSN (Electronic)2694-0604

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/TerritoryCanada
CityMontreal
Period20/07/2024/07/20

Keywords

  • cancer
  • feature extraction
  • gynaecology
  • learning (artificial intelligence)
  • mammography
  • medical image processing
  • AUC
  • area under the curve values
  • RA
  • maximum squared
  • machine learning-based models
  • computerized measures
  • digital mammograms
  • breast cancer risk assessment
  • computerized parenchymal analysis
  • MS method
  • ROI selection method
  • screening mammograms
  • polar-based selection methods
  • lattice-based
  • retro-areolar region
  • automated ROI selection
  • Feature extraction
  • Breast cancer
  • Tuning
  • Risk management
  • Mammography
  • Computational modeling
  • Area Under Curve
  • Breast Neoplasms
  • Female
  • Humans
  • Retrospective Studies
  • Risk Assessment

Publication forum classification

  • Publication forum level 1

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