Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.
Breast neoplasms
Deep learning
Mammography
Neovascularization
Pathologic
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
17
08
2021
accepted:
21
12
2021
revised:
20
12
2021
pubmed:
31
1
2022
medline:
24
6
2022
entrez:
30
1
2022
Statut:
ppublish
Résumé
To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM). This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour. In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower. Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status. • A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography. • The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors. • Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.
Identifiants
pubmed: 35094119
doi: 10.1007/s00330-022-08538-4
pii: 10.1007/s00330-022-08538-4
pmc: PMC8800426
doi:
Substances chimiques
Contrast Media
0
Receptors, Estrogen
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4834-4844Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
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