HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib

L. Beltrame, L. Mannarino, A. Sergi, A. Velle, I. Treilleux, S. Pignata, L. Paracchini, P. Harter, G. Scambia, F. Perrone, A. González-Martin, R. Berger, L. Arenare, S. Hietanen, D. Califano, S. Derio, T. Van Gorp, M.L. Dalessandro, K. Fujiwara, M. Provansal, D. Lorusso, P. Buderath, M. Masseroli, I. Ray-Coquard, E. Pujade-Lauraine, C. Romualdi, M. D’Incalci, S. Marchini

Open source

DOI: 10.1016/j.esmoop.2025.105060
Published: 2025-06
Container: ESMO Open
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.esmoop.2025.105060,
  title = {HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib},
  author = {L. Beltrame and L. Mannarino and A. Sergi and A. Velle and I. Treilleux and S. Pignata and L. Paracchini and P. Harter and G. Scambia and F. Perrone and A. González-Martin and R. Berger and L. Arenare and S. Hietanen and D. Califano and S. Derio and T. Van Gorp and M.L. Dalessandro and K. Fujiwara and M. Provansal and D. Lorusso and P. Buderath and M. Masseroli and I. Ray-Coquard and E. Pujade-Lauraine and C. Romualdi and M. D’Incalci and S. Marchini},
  year = {2025},
  journal = {ESMO Open},
  doi = {10.1016/j.esmoop.2025.105060},
  url = {https://doi.org/10.1016/j.esmoop.2025.105060}
}

RIS

TY  - JOUR
TI  - HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib
AU  - L. Beltrame
AU  - L. Mannarino
AU  - A. Sergi
AU  - A. Velle
AU  - I. Treilleux
AU  - S. Pignata
AU  - L. Paracchini
AU  - P. Harter
AU  - G. Scambia
AU  - F. Perrone
AU  - A. González-Martin
AU  - R. Berger
AU  - L. Arenare
AU  - S. Hietanen
AU  - D. Califano
AU  - S. Derio
AU  - T. Van Gorp
AU  - M.L. Dalessandro
AU  - K. Fujiwara
AU  - M. Provansal
AU  - D. Lorusso
AU  - P. Buderath
AU  - M. Masseroli
AU  - I. Ray-Coquard
AU  - E. Pujade-Lauraine
AU  - C. Romualdi
AU  - M. D’Incalci
AU  - S. Marchini
PY  - 2025
JO  - ESMO Open
DO  - 10.1016/j.esmoop.2025.105060
UR  - https://doi.org/10.1016/j.esmoop.2025.105060
ER  -

APA

Beltrame, L., Mannarino, L., Sergi, A., Velle, A., Treilleux, I., Pignata, S., Paracchini, L., Harter, P., Scambia, G., Perrone, F., González-Martin, A., Berger, R., Arenare, L., Hietanen, S., Califano, D., Derio, S., Gorp, T. V., Dalessandro, M., Fujiwara, K., Provansal, M., Lorusso, D., Buderath, P., Masseroli, M., Ray-Coquard, I., Pujade-Lauraine, E., Romualdi, C., D’Incalci, M., & Marchini, S. (2025). HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib. ESMO Open. https://doi.org/10.1016/j.esmoop.2025.105060

Source records

crossref · retrieved 2026-09-26T12:19:31.636Z