Predicting personal risk of developing breast and prostate cancer from routine check-up data using survival analysis trees

Abstract The challenge of survival prediction is ubiquitous in industry and medicine. Few methods are available for survival prediction of time varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left truncated and right-censored data. We demonstrated the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyzed electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, Cox-regression model and a single survival tree. Our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development.