An AI Model Identifies Chemotherapy Benefit in Node-Negative HR+/HER2- Breast Cancer Patients from TAILORx, a Phase 3 Randomized Clinical Trial.

Chan N, Tang C, Biswas D, Park J, Zeng K, Cappadona J, Liu C, McMclean A, Berrevoets J, Bao L, Machura B, Gray RJ, Wang V, Witowski J, Geras KJ, Sparano JA, TAILORx Investigators

Open source

DOI
10.64898/2026.09.12.26362500
Published
2026 Sep 20
Container
medRxiv : the preprint server for health sciences
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.64898/2026.09.12.26362500,
  title = {An AI Model Identifies Chemotherapy Benefit in Node-Negative HR+/HER2- Breast Cancer Patients from TAILORx, a Phase 3 Randomized Clinical Trial.},
  author = {Chan N and Tang C and Biswas D and Park J and Zeng K and Cappadona J and Liu C and McMclean A and Berrevoets J and Bao L and Machura B and Gray RJ and Wang V and Witowski J and Geras KJ and Sparano JA and TAILORx Investigators},
  year = {2026},
  journal = {medRxiv : the preprint server for health sciences},
  doi = {10.64898/2026.09.12.26362500},
  url = {https://doi.org/10.64898/2026.09.12.26362500}
}

RIS

TY  - JOUR
TI  - An AI Model Identifies Chemotherapy Benefit in Node-Negative HR+/HER2- Breast Cancer Patients from TAILORx, a Phase 3 Randomized Clinical Trial.
AU  - Chan N
AU  - Tang C
AU  - Biswas D
AU  - Park J
AU  - Zeng K
AU  - Cappadona J
AU  - Liu C
AU  - McMclean A
AU  - Berrevoets J
AU  - Bao L
AU  - Machura B
AU  - Gray RJ
AU  - Wang V
AU  - Witowski J
AU  - Geras KJ
AU  - Sparano JA
AU  - TAILORx Investigators
PY  - 2026
JO  - medRxiv : the preprint server for health sciences
DO  - 10.64898/2026.09.12.26362500
UR  - https://doi.org/10.64898/2026.09.12.26362500
ER  - 

APA

N, C., C, T., D, B., J, P., K, Z., J, C., C, L., A, M., J, B., L, B., B, M., RJ, G., V, W., J, W., KJ, G., JA, S., & Investigators, T. (2026). An AI Model Identifies Chemotherapy Benefit in Node-Negative HR+/HER2- Breast Cancer Patients from TAILORx, a Phase 3 Randomized Clinical Trial.. medRxiv : the preprint server for health sciences. https://doi.org/10.64898/2026.09.12.26362500

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