RESEARCH & DISCOVERIES
Current Areas of Study
(Above) CT scan of pancreatic cyst and (Below) Adam Awe, a medical student and lead author, presenting the results at the Shapiro forum at the UW Medical School ​
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Machine Learning for Medical Imaging
The goal of this project was to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics.
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Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The goal of this project was to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics. This work used features extracted from CT images to assess the nature of pancreatic cysts, with the long term goal of using non-invasive CT to determine the potential health risks of such cysts and assist doctors on making choices about whether to perform surgery. Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model. Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis. This project was in collaboration with the visionary Machine Learning for Medical Imaging (ML4AI) program at UW. It involved 7 students across multiple departments on the UW campus. This work was published in: Awe, Adam M, Michael M Vanden Heuvel, Tianyuan Yuan, Victoria R Rendell, Mingren Shen, Agrima Kampani, Shanchao Liang, Dane D Morgan, Emily R Winslow, and Meghan G Lubner. 2022. “Machine Learning Principles Applied to CT Radiomics to Predict Mucinous Pancreatic Cysts.” Abdominal Radiology 47 (1): 221–31. https://doi.org/10.1007/s00261-021-03289-0.