Annukka M. Pasanen

Explainable Machine Learning for Preoperative Relapse Prediction in Molecularly Stratified Endometrial Cancer: A Single-Center Finnish Cohort Study

This paper develops explainable machine learning models to predict relapse timing in endometrial cancer using preoperative data, finding high performance with F1 scores up to 0.895.

Interpretable prognostic modeling of endometrial cancer

This study compares Cox proportional hazards and optimal survival tree models for prognostic modeling of endometrial cancer using clinical and molecular data, finding linear models better for clinical features alone and tree models better with molecular data.

ESR1, WT1, WNT4, ATM and TERT loci are major contributors to uterine leiomyoma predisposition

This study identifies five new genetic loci associated with uterine leiomyoma risk and links them to estrogen receptor alpha and genetic stability.