HR-SC-an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib.
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, Van Gorp T, Dalessandro ML, Fujiwara K, Provansal M, Lorusso D, Buderath P, Masseroli M, Ray-Coquard I, Pujade-Lauraine E, Romualdi C, D'Incalci M, Marchini S.
Beltrame L, et al. Among authors: derio s.
ESMO Open. 2025 Jun;10(6):105060. doi: 10.1016/j.esmoop.2025.105060. Epub 2025 May 19.
ESMO Open. 2025.
PMID: 40393377
Free PMC article.
CONCLUSIONS: The study demonstrates that HR-SC is a novel, clinically feasible solution with a low failure rate for predicting HRR status in OC patients and underscores the importance of leveraging ML approaches for advancing precision oncology in the era of personalized m …
CONCLUSIONS: The study demonstrates that HR-SC is a novel, clinically feasible solution with a low failure rate for predicting HRR status in …