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.

<h4>Background</h4>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 mo...

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Main Authors: Nicole Hernández, Tomppa Pakarinen, Annukka Salminen, Santiago Laguna Castro, Ulla Karhunen-Enckell, Markus Hannula, Ritva Heljasvaara, Jari Hyttinen, Katriina Joensuu, Otto Jokelainen, Arja Jukkola, Sanna-Maria Karppinen, Auni Lindgren, Eero Lääperi, Emilia Peuhu, Taina Pihlajaniemi, Renata Prunskaite-Hyyryläinen, Kirsi Rilla, Pekka Ruusuvuori, Leena Latonen, Teemu Tolonen, Masi Valkonen, Mira Valkonen, Miska Vuorlaakso, Said Pertuz, Irina Rinta-Kiikka, Otso Arponen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0321658
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author Nicole Hernández
Tomppa Pakarinen
Annukka Salminen
Santiago Laguna Castro
Ulla Karhunen-Enckell
Markus Hannula
Ritva Heljasvaara
Jari Hyttinen
Katriina Joensuu
Otto Jokelainen
Arja Jukkola
Sanna-Maria Karppinen
Auni Lindgren
Eero Lääperi
Emilia Peuhu
Taina Pihlajaniemi
Renata Prunskaite-Hyyryläinen
Kirsi Rilla
Pekka Ruusuvuori
Leena Latonen
Teemu Tolonen
Masi Valkonen
Mira Valkonen
Miska Vuorlaakso
Said Pertuz
Irina Rinta-Kiikka
Otso Arponen
author_facet Nicole Hernández
Tomppa Pakarinen
Annukka Salminen
Santiago Laguna Castro
Ulla Karhunen-Enckell
Markus Hannula
Ritva Heljasvaara
Jari Hyttinen
Katriina Joensuu
Otto Jokelainen
Arja Jukkola
Sanna-Maria Karppinen
Auni Lindgren
Eero Lääperi
Emilia Peuhu
Taina Pihlajaniemi
Renata Prunskaite-Hyyryläinen
Kirsi Rilla
Pekka Ruusuvuori
Leena Latonen
Teemu Tolonen
Masi Valkonen
Mira Valkonen
Miska Vuorlaakso
Said Pertuz
Irina Rinta-Kiikka
Otso Arponen
author_sort Nicole Hernández
collection DOAJ
description <h4>Background</h4>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.<h4>Methodology</h4>This study will collect breast samples from 200 participants that will undergo breast resection as per clinical indications. Each sample will undergo the mammography imaging procedure several times to obtain mammograms with different combinations of imaging parameters. The resulting dataset will be used for the statistical analysis of the impact of imaging parameters in mammographic texture features and the computerized analysis of mammograms. Furthermore, biological information will be collected from the resected breast samples to study their relation to mammographic texture features.<h4>Discussion</h4>This study will add to the understanding of the effect of different sources of heterogeneity on mammography, ultimately aiding in the future development of robust computerized analysis models.
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spelling doaj-art-e4c6596b6f384629964512bec5fc5d212025-08-23T05:32:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032165810.1371/journal.pone.0321658A systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue: A protocol description.Nicole HernándezTomppa PakarinenAnnukka SalminenSantiago Laguna CastroUlla Karhunen-EnckellMarkus HannulaRitva HeljasvaaraJari HyttinenKatriina JoensuuOtto JokelainenArja JukkolaSanna-Maria KarppinenAuni LindgrenEero LääperiEmilia PeuhuTaina PihlajaniemiRenata Prunskaite-HyyryläinenKirsi RillaPekka RuusuvuoriLeena LatonenTeemu TolonenMasi ValkonenMira ValkonenMiska VuorlaaksoSaid PertuzIrina Rinta-KiikkaOtso Arponen<h4>Background</h4>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.<h4>Methodology</h4>This study will collect breast samples from 200 participants that will undergo breast resection as per clinical indications. Each sample will undergo the mammography imaging procedure several times to obtain mammograms with different combinations of imaging parameters. The resulting dataset will be used for the statistical analysis of the impact of imaging parameters in mammographic texture features and the computerized analysis of mammograms. Furthermore, biological information will be collected from the resected breast samples to study their relation to mammographic texture features.<h4>Discussion</h4>This study will add to the understanding of the effect of different sources of heterogeneity on mammography, ultimately aiding in the future development of robust computerized analysis models.https://doi.org/10.1371/journal.pone.0321658
spellingShingle Nicole Hernández
Tomppa Pakarinen
Annukka Salminen
Santiago Laguna Castro
Ulla Karhunen-Enckell
Markus Hannula
Ritva Heljasvaara
Jari Hyttinen
Katriina Joensuu
Otto Jokelainen
Arja Jukkola
Sanna-Maria Karppinen
Auni Lindgren
Eero Lääperi
Emilia Peuhu
Taina Pihlajaniemi
Renata Prunskaite-Hyyryläinen
Kirsi Rilla
Pekka Ruusuvuori
Leena Latonen
Teemu Tolonen
Masi Valkonen
Mira Valkonen
Miska Vuorlaakso
Said Pertuz
Irina Rinta-Kiikka
Otso Arponen
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.
PLoS ONE
title 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.
title_full 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.
title_fullStr 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.
title_full_unstemmed 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.
title_short 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.
title_sort systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue a protocol description
url https://doi.org/10.1371/journal.pone.0321658
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