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Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

An interpretable machine-learning signature predicts early triple-negative breast cancer recurrence but adds no prognostic value beyond clinicopathological factors in external validation

  • Haocheng Nan
  • Rong Di
  • Xiaorong Kou
  • Gang Ma
Open at sourcePDFDOICite

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Cite

BibTeXRIS
BibTeX
@article{nan2026interpretable,
  title = {{An interpretable machine-learning signature predicts early triple-negative breast cancer recurrence but adds no prognostic value beyond clinicopathological factors in external validation}},
  author = {Haocheng Nan and Rong Di and Xiaorong Kou and Gang Ma},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-73601-9},
  url = {https://www.nature.com/articles/s41598-026-73601-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - An interpretable machine-learning signature predicts early triple-negative breast cancer recurrence but adds no prognostic value beyond clinicopathological factors in external validation
AU  - Haocheng Nan
AU  - Rong Di
AU  - Xiaorong Kou
AU  - Gang Ma
PY  - 2026
DA  - 2026/10/12
JO  - Scientific Reports
DO  - 10.1038/s41598-026-73601-9
UR  - https://www.nature.com/articles/s41598-026-73601-9
SN  - 2045-2322
ER  -