Early detection of prostate gland and breast cancer risk based on routine check-up data using survival analysis trees for left-truncated and right-censored data

ABSTRACT Objective To predict breast cancer (BC) and prostate gland cancer (PGC) risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. Materials and Methods We analyzed electronic medical records of 20,317 healthy individuals who underwent routine checkups, encompassing more than 600 parameters per visit, and identified those who later developed cancer. We developed a novel ensemble method for risk prediction of multivariate time series data using a random forest model of survival trees for left truncated and right-censored data. Results Using cross-validation, our method predicted future PGC and BC 6 months before diagnosis, achieving an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, better than standard random forest, Cox-regression model and a single survival tree. Our method can complement existing screening tests such as clinical breast examination and mammography for BC, and help in detection of subjects that were missed by these tests. Discussion Computational analysis of results of routine checkups of healthy individuals can improve the detection of those at risk of cancer development. Conclusion Our method may assist in early detection of breast and prostate gland cancer.