MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens
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 Adri...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01579-1 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Chengyue Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7ab5eeb4990f42929d7faf65df8c8c1c |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-7ab5eeb4990f42929d7faf65df8c8c1c2025-08-20T02:28:07ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111310.1038/s41746-025-01579-1MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimensChengyue Wu0Ernesto A. B. F. Lima1Casey E. Stowers2Zhan Xu3Clinton Yam4Jong Bum Son5Jingfei Ma6Gaiane M. Rauch7Thomas E. Yankeelov8 Department of Imaging Physics, The University of Texas MD Anderson Cancer CenterOden Institute for Computational Engineering and Sciences, The University of Texas at AustinOden Institute for Computational Engineering and Sciences, The University of Texas at Austin Department of Imaging Physics, The University of Texas MD Anderson Cancer CenterDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center Department of Imaging Physics, The University of Texas MD Anderson Cancer Center Department of Imaging Physics, The University of Texas MD Anderson Cancer CenterDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center Department of Imaging Physics, The University of Texas MD Anderson Cancer CenterAbstract 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.https://doi.org/10.1038/s41746-025-01579-1 |
| spellingShingle | 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 MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens npj Digital Medicine |
| title | MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| title_full | MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| title_fullStr | MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| title_full_unstemmed | MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| title_short | MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| title_sort | mri based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens |
| url | https://doi.org/10.1038/s41746-025-01579-1 |
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