Interactive Web Application for Plotting Personalized Prognosis Prediction Curves in Allogeneic Hematopoietic Cell Transplantation Using Machine Learning.

Background: Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative treatment option for malignant hematological disorders. Transplant clinicians estimate patient-specific prognosis empirically in clinical practice based on previous studies on similar patients. However, this approach does not provide objective data. The present study primarily aimed to develop a tool capable of providing accurate personalized prognosis prediction after allo-HCT in an objective manner.

Methods: We developed an interactive web application tool with a graphical user interface capable of plotting the personalized survival and cumulative incidence prediction curves after allo-HCT adjusted by 8 patient-specific factors, which are known as prognostic predictors, and assessed their predictive performances. A random survival forest model using the data of patients who underwent allo-HCT at our institution was applied to develop this application.

Results: We succeeded in showing the personalized prognosis prediction curves of 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality (NRM) interactively using our web application (https://predicted-os-after-transplantation.shinyapps.io/RSF_model/). To assess its predictive performance, the entire cohort (363 cases) was split into a training cohort (70%) and a test cohort (30%) time-sequentially based on the patients' transplant dates. The areas under the receiver-operating characteristic curves for 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality in test cohort were 0.70, 0.72, 0.73, and 0.77, respectively.

Conclusions: The new web application could allow transplant clinicians to inform a new allo-HCT candidate of the objective personalized prognosis prediction and facilitate decision-making.