MRI-Based Digital Twins to Improve Treatment Response of Breast Cancer by Optimizing Neoadjuvant Chemotherapy Regimens

Authors

Chengyue Wu
Ernesto A B F Lima
Casey E Stowers
Zhan Xu
Clinton Yam
Jong Bum Son
Jingfei Ma
Gaiane M Rauch
Thomas E Yankeelov

Publication Date

4-7-2025

Journal

NPJ Digital Medicine

Abstract

We developed a practical framework to construct digital twins for predicting and optimizing triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276443, registered on 10/21/2014) who received Adriamycin/Cytoxan (A/C)-Taxol (T). Digital twins were established by calibrating a biology-based mathematical model to patient-specific MRI data, which accurately predicted pathological complete response (pCR) with an AUC of 0.82. We then used each patient's twin to theoretically optimize outcome by identifying their optimal A/C-T schedule from 128 options. The patient-specifically optimized treatment yielded a significant improvement in pCR rate of 20.95-24.76%. Retrospective validation was conducted by virtually treating the twins with AC-T schedules from historical trials and obtaining identical observations on outcomes: bi-weekly A/C-T outperforms tri-weekly A/C-T, and weekly/bi-weekly T outperforms tri-weekly T. This proof-of-principle study demonstrates that our digital twin framework provides a practical methodology to identify patient-specific TNBC treatment schedules.

Keywords

Breast cancer, Computational science

DOI

10.1038/s41746-025-01579-1

PMID

40195521

PMCID

PMC11976917

PubMedCentral® Posted Date

4-7-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes