Predicting erectile dysfunction after treatment for localized prostate cancer
It addresses a quality-of-life issue for prostate cancer patients by enabling personalized treatment choices, but it is incremental as it applies existing ML methods to a specific clinical prediction task.
This study tackled the problem of predicting erectile dysfunction (ED) after localized prostate cancer treatment by developing a machine learning model using patient data at diagnosis, achieving predictions at 1-year and 2-year post-diagnosis to aid in treatment decision-making.
While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve accuracy of prediction and quality of care. Predicting ED may help aid shared decision making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis.