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Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach

2024Journal article Open access
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dc.contributor.authorMota, Ana M.
dc.contributor.authorMendes, João
dc.contributor.authorMatela, Nuno
dc.date.accessioned2025-03-13T17:56:31Z
dc.date.available2025-03-13T17:56:31Z
dc.date.issued2024
dc.description.abstractBreast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/biomedicines12061371pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/99316
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationFundação para a Ciência e a Tecnologia (FCT) grant number UIDB/00645/2020pt_PT
dc.relationFundação para a Ciência e a Tecnologia (FCT) grant number 2022.12271.BDpt_PT
dc.subjectbreast cancerpt_PT
dc.subjectmolecular subtypespt_PT
dc.subjectmammographypt_PT
dc.subjectartificial intelligencept_PT
dc.subjectdeep learningpt_PT
dc.subjectpersonalized medicinept_PT
dc.titleBreast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue6pt_PT
oaire.citation.startPage1371pt_PT
oaire.citation.titleBiomedicinespt_PT
oaire.citation.volume12pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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