A systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue: A protocol description.

Journal: PloS one
Published Date: Aug 18, 2025

Abstract

BACKGROUND: Mammography is the most common imaging modality for the detection of breast cancer. Artificial intelligence algorithms for mammography analysis have shown promising performance for breast cancer risk assessment and lesion detection and classification; however, these models often fail the test of external validation. The evidence points to variations in image acquisition-known as the batch effect-as a main contributing factor to the lack of the models generalization and robustness. However, studies on the effects of acquisition in the mammogram have been limited due to lack of appropriate datasets. This prospective, exploratory, non-randomized study aims to study how biological and non-biological sources of heterogeneity affect the mammogram and, in turn, the computerized models for mammography analysis.

Authors

  • Nicole Hernández
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Tomppa Pakarinen
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Annukka Salminen
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Santiago Laguna Castro
    Computational Biophysics and Imaging Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Ulla Karhunen-Enckell
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Markus Hannula
    Computational Biophysics and Imaging Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Ritva Heljasvaara
    Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
  • Jari Hyttinen
    Department of Electronics and Communications Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland.
  • Katriina Joensuu
    Department of Surgery, Tampere University Hospital, Tampere, Finland.
  • Otto Jokelainen
    Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland.
  • Arja Jukkola
    Tampere Cancer Center, Tampere University, Tampere, Finland.
  • Sanna-Maria Karppinen
    Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
  • Auni Lindgren
    Department of Gynecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland.
  • Eero Lääperi
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Emilia Peuhu
    Institute of Biomedicine, Cancer Laboratory FICAN West, University of Turku, Turku, Finland.
  • Taina Pihlajaniemi
    Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
  • Renata Prunskaite-Hyyryläinen
    Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
  • Kirsi Rilla
    Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
  • Pekka Ruusuvuori
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Leena Latonen
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Teemu Tolonen
  • Masi Valkonen
    Institute of Biomedicine, University of Turku, Turku, Finland.
  • Mira Valkonen
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Miska Vuorlaakso
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Said Pertuz
    Escuela de Ingenierías Eléctrica Electrónica y de Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga, Colombia.
  • Irina Rinta-Kiikka
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • Otso Arponen
    Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. otso.arponen@tuni.fi.

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