Abstract
Individualized prediction of a patient’s risk of premature ovarian insufficiency (POI) after chemotherapy is challenging. Currently, chemotherapy regimens are stratified as “minimally increased” risk of POI (<20% risk), “intermediate” (30-70%) or “high” risk (>80%). Given the wide range of possible effects, it is challenging to counsel a patient with “intermediate” risk. Our study sought to use individual patient and treatment factors to develop a model that predicts personalized risk of POI after chemotherapy for reproductive-aged women.
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