Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning

To develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.

[1]  Yi-Jen Chen,et al.  Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging? , 2021, Gynecologic oncology.

[2]  J. Kwon,et al.  Factors associated with an increased risk of recurrence in patients diagnosed with high-grade endometrial cancer undergoing minimally invasive surgery: A study of the society of gynecologic oncology (GOC) community of practice (CoP). , 2021, Gynecologic oncology.

[3]  A. Jemal,et al.  Cancer Statistics, 2021 , 2021, CA: a cancer journal for clinicians.

[4]  Arcot Sowmya,et al.  A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction , 2020, Scientific Reports.

[5]  C. Hur,et al.  Using machine learning to create prognostic systems for endometrial cancer. , 2020, Gynecologic oncology.

[6]  J. Kwon,et al.  Evaluating the diagnostic performance of preoperative endometrial biopsies in patients diagnosed with high grade endometrial cancer: A study of the Society of Gynecologic Oncology (GOC) Community of Practice (CoP). , 2020, Gynecologic oncology.

[7]  H. Putter,et al.  Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): patterns of recurrence and post-hoc survival analysis of a randomised phase 3 trial , 2019, The Lancet. Oncology.

[8]  F. Ghezzi,et al.  Tumor Size, an Additional Risk Factor of Local Recurrence in Low-Risk Endometrial Cancer: A Large Multicentric Retrospective Study , 2018, International Journal of Gynecologic Cancer.

[9]  A. Talhouk,et al.  Confirmation of ProMisE: A simple, genomics‐based clinical classifier for endometrial cancer , 2017, Cancer.

[10]  C. Coutant,et al.  Predicting poor prognosis recurrence in women with endometrial cancer: a nomogram developed by the FRANCOGYN study group , 2016, British Journal of Cancer.

[11]  M. Köbel,et al.  Treatment related outcomes in high-risk endometrial carcinoma: Canadian high risk endometrial cancer consortium (CHREC). , 2016, Gynecologic oncology.

[12]  M. Köbel,et al.  Canadian high risk endometrial cancer (CHREC) consortium: analyzing the clinical behavior of high risk endometrial cancers. , 2015, Gynecologic oncology.

[13]  A. Talhouk,et al.  A clinically applicable molecular-based classification for endometrial cancers , 2015, British Journal of Cancer.

[14]  Shuhei Kaneko,et al.  Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data , 2015, Comput. Math. Methods Medicine.

[15]  E. Swisher,et al.  Does size matter? Tumor size and morphology as predictors of nodal status and recurrence in endometrial cancer. , 2005, Gynecologic oncology.

[16]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[17]  M. Maiman,et al.  A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study , 2004 .