Gevaert and colleagues trained LungNet using imaging data from four independent cohorts of more than 700 patients with non-small cell lung cancer, which accounts for 85% of all such malignancies. The machine learning tool was able to predict overall survival rates across all four cohorts at four different hospitals, classify benign versus malignant nodules, and stratify based on cancer progression.
“This is an outstanding example of how machine learning technology can be a cost-effective approach to advance disease detection, diagnosis and treatment,” added Qi Duan, PhD, director of the NIBIB Program in Image Processing, Visual Perception and Display.
Their work was fueled by a $444,000 grant from the National Institutes of Health and was recently highlighted in the journal Nature Machine Intelligence.
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