Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh IT, Ravdin P, Bugarini R, Baehner FL, Davidson NE et al (2010) Prognostic and predictive value of the 21-gene recurrence score assay in a randomized trial of chemotherapy for postmenopausal, node-positive, estrogen receptor-positive breast cancer. Lancet Oncol 11(1):55

Article  CAS  PubMed  Google Scholar 

Albanell J, Svedman C, Gligorov J, Holt SD, Bertelli G, Blohmer JU, Rouzier R, Lluch A, Eiermann W (2016) Pooled analysis of prospective European studies assessing the impact of using the 21-gene recurrence score assay on clinical decision making in women with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative early-stage breast cancer. Eur J Cancer 66:104–113

Article  PubMed  Google Scholar 

Andre F, Ismaila N, Henry NL, Somerfield MR, Bast RC, Barlow W, Collyar DE, Hammond ME, Kuderer NM, Liu MC et al (2019) Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: Asco clinical practice guideline update-integration of results from tailorx. J Clin Oncol 37(22):1956–1964

Article  CAS  PubMed  Google Scholar 

Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann Stat 47(2):1148–1178. https://doi.org/10.1214/18-AOS1709

Article  Google Scholar 

Baltres A, Al Masry Z, Zemouri R, Valmary-Degano S, Arnould L, Zerhouni N, Devalland C (2020) Prediction of oncotype dx recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, her2-negative breast cancer. Breast Cancer 27(5):1007–1016

Article  PubMed  Google Scholar 

Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

Article  Google Scholar 

Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

Article  Google Scholar 

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. The Wadsworth Statistics/Probability Series. Belmont, California: Wadsworth International Group, a Division of Wadsworth, Inc., p 368

Google Scholar 

Chen G, Li Q, Shi F, Rekik I, Pan Z (2020) Rfdcr: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields. Neuroimage 211:116620

Article  PubMed  Google Scholar 

Ćevid D, Michel L, Näf J, Meinshausen N, Bühlmann P (2021) Distributional random forests: heterogeneity adjustment and multivariate distributional regression. https://arxiv.org/abs/2005.14458

Fernandez-Lozano C, Hervella P, Mato-Abad V, Rodríguez-Yáñez M, Suárez-Garaboa S, López-Dequidt I, Estany-Gestal A, Sobrino T, Campos F, Castillo J et al (2021) Random forest-based prediction of stroke outcome. Sci Rep 11(1):1–12

Article  Google Scholar 

Flanagan MB, Dabbs DJ, Brufsky AM, Beriwal S, Bhargava R (2008) Histopathologic variables predict oncotype dx™recurrence score. Mod Pathol 21(10):1255–1261

Article  CAS  PubMed  Google Scholar 

Giuliano AE, Connolly JL, Edge SB, Mittendorf EA, Rugo HS, Solin LJ, Weaver DL, Winchester DJ, Hortobagyi GN (2017) Breast cancer-major changes in the American joint committee on cancer eighth edition cancer staging manual. CA: A Cancer J Clin 67(4):290–303

Gneiting T, Katzfuss M (2014) Probabilistic forecasting. Annu Rev Stat Appl 1(1):125–151. https://doi.org/10.1146/annurev-statistics-062713-085831

Article  Google Scholar 

Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Amer Stat Assoc 102(477):359–378. https://doi.org/10.1198/016214506000001437

Article  CAS  Google Scholar 

Hou Y, Tozbikian G, Zynger DL, Li Z (2017) Using the modified Magee equation to identify patients unlikely to benefit from the 21-gene recurrence score assay (oncotype dx assay). Am J Clin Pathol 147(6):541–548

Article  CAS  PubMed  Google Scholar 

Kalinsky K, Barlow WE, Gralow JR, Meric-Bernstam F, Albain KS, Hayes DF, Lin NU, Perez EA, Goldstein LJ, Chia SK et al (2021) 21-gene assay to inform chemotherapy benefit in node-positive breast cancer. N Engl J Med 385(25):2336–2347

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kim I, Choi HJ, Ryu JM, Lee SK, Yu JH, Kim SW, Nam SJ, Lee JE (2019) A predictive model for high/low risk group according to oncotype dx recurrence score using machine learning. Eur J Surg Oncol 45(2):134–140

Article  PubMed  Google Scholar 

Klein ME, Dabbs DJ, Shuai Y, Brufsky AM, Jankowitz R, Puhalla SL, Bhargava R (2013) Prediction of the oncotype dx recurrence score: use of pathology-generated equations derived by linear regression analysis. Mod Pathol 26(5):658–664

Article  PubMed  PubMed Central  Google Scholar 

Lin Y, Jeon Y (2006) Random forests and adaptive nearest neighbors. J Am Stat Assoc 101(474):578–590. https://doi.org/10.1198/016214505000001230

Article  CAS  Google Scholar 

Matheson JE, Winkler RL (1976) Scoring rules for continuous probability distributions. Manag Sci 22. https://doi.org/10.2307/2629907

Article  Google Scholar 

Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 983–999

Orucevic A, Bell JL, King M, McNabb AP, Heidel RE (2019) Nomogram update based on tailorx clinical trial results-oncotype dx breast cancer recurrence score can be predicted using clinicopathologic data. Breast 46:116–125

Article  PubMed  Google Scholar 

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826

Article  CAS  PubMed  Google Scholar 

Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23):3726–3734

Article  CAS  PubMed  Google Scholar 

Pawloski KR, Gonen M, Wen HY, Tadros AB, Thompson D, Abbate K, Morrow M, El-Tamer M (2022) Supervised machine learning model to predict oncotype dx risk category in patients over age 50. Breast Cancer Res Treat 191(2):423–430

Article  CAS  PubMed  Google Scholar 

Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, Geyer Jr CE, Dees EC, Perez EA, Olson Jr JA et al (2015) Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med 373(21):2005–2014

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, Geyer Jr CE, Dees EC, Goetz MP, Olson Jr JA et al (2018) Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med 379(2):111–121

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sughayer M, Alaaraj R, Alsughayer A (2018) Applying new magee equations for predicting the oncotype dx recurrence score. Breast Cancer 25(5):597–604

Article  PubMed  Google Scholar 

Wolff AC, Hammond ME, Allison KH, Harvey BE, Mangu PB, Bartlett JM, Bilous M, Ellis IO, Fitzgibbons P, Hanna W et al (2018) Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/college of American pathologists clinical practice guideline focused update. Archiv pathology Labor Med 142(11):1364–1382

Article  Google Scholar 

Yeo B, Zabaglo L, Hills M, Dodson A, Smith I, Dowsett M (2015) Clinical utility of the ihc4+ c score in oestrogen receptor-positive early breast cancer: a prospective decision impact study. Br J Cancer 113(3):390–395

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zare A, Postovit L-M, Githaka JM (2021) Robust inflammatory breast cancer gene signature using nonparametric random forest analysis. Breast Cancer Res 23(1):1–6

Article  Google Scholar