A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation

Abstract

Background Forecasting acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (HSCT) is highly challenging with conventional statistical techniques due to complex parameters and their interactions. The primary object of this study was to establish a convolutional neural network (CNN)-based prediction model for aGVHD. Method We analyzed adult patients who underwent allogeneic HSCT between 2008 and 2018, using the Japanese nationwide registry database. The CNN algorithm, equipped with a natural language processing technique and an interpretable explanation algorithm, was applied to develop and validate prediction models. Results Here, we evaluate 18,763 patients between 16 and 80 years of age (median, 50 years). In total, grade II–IV and grade III–IV aGVHD is observed among 42.0% and 15.6%. The CNN-based model eventually allows us to calculate a prediction score of aGVHD for an individual case, which is validated to distinguish the high-risk group of aGVHD in the test cohort: cumulative incidence of grade III–IV aGVHD at Day 100 after HSCT is 28.8% for patients assigned to a high-risk group by the CNN model, compared to 8.4% among low-risk patients (hazard ratio, 4.02; 95% confidence interval, 2.70–5.97; p < 0.01), suggesting high generalizability. Furthermore, our CNN-based model succeeds in visualizing the learning process. Moreover, contributions of pre-transplant parameters other than HLA information to the risk of aGVHD are determined. Conclusions Our results suggest that CNN-based prediction provides a faithful prediction model for aGVHD, and can serve as a valuable tool for decision-making in clinical practice.

Data-driven grading of acute graft-versus-host disease

“…More recently, promising efforts have been undertaken employing machine learning methods to predict mortality 20,21 and aGVHD [22][23][24] after HCT. Given these recent insights, prevailing discrepancies in conventional aGVHD classification practice, and substantially varying survival outcomes of patients with the same aGVHD severity grade, we hypothesized that a data-driven approach could shed light on the strengths and limitations of aGVHD classifications and respond to ongoing issues such as multiorgan involvement, heterogeneous phenotypes and their relation to outcomes.…”