MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 40195521.
- Also identified by DOI 10.1038/s41746-025-01579-1 and PMC identifier 11976917.
- Licence recorded as CC BY-NC-ND.
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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.