Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.
Authors
Affiliations (9)
Affiliations (9)
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA.
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA.
- Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA.
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA.
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, USA.
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
- Department of Pathology, The Ohio State University, Columbus, OH, USA.
- Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, Utah, USA.
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA. Electronic address: [email protected].
Abstract
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well understood, which can complicate decisions about donor-lung acceptance. Previously, we developed a machine learning model to predict grade 3 PGD using donor and recipient electronic health record data, but it lacked granular information from donor-lung computed tomography (CT) scans, which are routinely assessed during offer review. In this study, we used a gated approach to determine optimal methods for analyzing donor-lung CT scans among patients receiving first-time, bilateral lung transplants at a single center over 10 years. We assessed 4 computer vision approaches and fused the best with electronic health record data at 3 points in the machine learning process. A total of 160 patients had donor-lung CT scans for analysis. The best imaging-only approach employed a 3D ResNet model, yielding median (interquartile range) areas under the receiver operating characteristic and precision-recall curves of 0.63 (0.49-0.72) and 0.48 (0.35-0.6), respectively. Combining imaging with clinical data using late fusion provided the highest performance, with median areas under the receiver operating characteristic and precision-recall curves of 0.74 (0.59-0.85) and 0.61 (0.47-0.72), respectively.