Machine learning algorithm for mortality prediction in patients with advanced penile cancer

Penile cancer remains a rare cancer with an annual incidence of 1 in 100,000 men in the United States, accounting for 0.4-0.6% of all malignancies. Furthermore, to date there are no predictive models of early mortality in penile cancer. Meanwhile, machine learning has potential to serve as a prognostic tool for patients with advanced disease. We developed a machine learning model for predicting early mortality in penile cancer (survival less than 11 months after initial diagnosis. A cohort of 88 patients with advanced penile cancer was extracted from the Surveillance, Epidemiology and End Results (SEER) program. In the cohort, patients with advanced penile cancer exhibited a median overall survival of 21 months, with the 25th percentile of overall survival being 11 months. We constructed predictive features based on patient demographics, staging, metastasis, lymph node biopsy criteria, and metastatic sites. We trained a multivariate logistic regression model, tuning parameters with respect to regularization, and feature selection criteria. Upon evaluation with 5-fold cross validation, our model achieved 68.2% accuracy with AUC 0.696. Criteria for advanced staging (T4, group stage IV), as well as higher age, white race and squamous cell histology, were the most predictive of early mortality. Tumor size was the strongest negative predictor of early mortality. Our study showcases the first known predictive model for early mortality in patients with advanced penile cancer and should serve as a framework for approaching the clinical problem in future studies. Future work should aim to incorporate other data sources such as genomic and metabolomic data, increase patient counts, incorporate clinical characteristics such as ECOG and RECIST criteria, and assess the performance of the model in a prospective fashion.