Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review

Abstract Background Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Multimodal deep learning (DL), integrating diverse data sources (radiology, pathology, omics, clinical), holds promise for improving pCR predicti...

Full description

Saved in:
Bibliographic Details
Main Authors: Eriseld Krasniqi, Lorena Filomeno, Teresa Arcuri, Gianluigi Ferretti, Simona Gasparro, Alberto Fulvi, Arianna Roselli, Loretta D’Onofrio, Laura Pizzuti, Maddalena Barba, Marcello Maugeri-Saccà, Claudio Botti, Franco Graziano, Ilaria Puccica, Sonia Cappelli, Fabio Pelle, Flavia Cavicchi, Amedeo Villanucci, Ida Paris, Fabio Calabrò, Sandra Rea, Maurizio Costantini, Letizia Perracchio, Giuseppe Sanguineti, Silvia Takanen, Laura Marucci, Laura Greco, Rami Kayal, Luca Moscetti, Elisa Marchesini, Nicola Calonaci, Giovanni Blandino, Giulio Caravagna, Patrizia Vici
Format: Article
Language:English
Published: BMC 2025-06-01
Series:Biology Direct
Subjects:
Breast cancer
Neoadjuvant treatment
Deep learning
Multimodal prediction
Online Access:https://doi.org/10.1186/s13062-025-00661-8
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items