Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives.
artificial intelligence
breast cancer
imaging
machine learning
neoadjuvant chemotherapy
nuclear medicine
radiomics
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
14 Jul 2021
14 Jul 2021
Historique:
received:
16
06
2021
accepted:
30
06
2021
entrez:
24
7
2021
pubmed:
25
7
2021
medline:
25
7
2021
Statut:
epublish
Résumé
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.
Identifiants
pubmed: 34298733
pii: cancers13143521
doi: 10.3390/cancers13143521
pmc: PMC8303777
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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