AI-assisted analysis of CT perfusion to predict response in patients with pancreatic adenocarcinoma

CT perfusion (CTP) shows potential for treatment response in patients pancreatic ductal adenocarcinoma (PDAC). However, current pharmacokinetic models are difficult to use in clinical decision-making as they do not always accurately reflect changes in perfusion. Visual changes in the time-intensity curve (TIC) are not translated in perfusion parameters. We developed a kinetic model-independent method to analyze time-intensity curves, based on experiences in dynamic-contrast-enhanced MRI in prostate cancer. Methods or Background: Initial data (n=12) from a prospective study evaluating chemotherapy response in patients with PDAC. CTP was performed at baseline and after 3 months. A bolus-timing optimized scan protocol with 23 perfusion images was used. The tumor section with the largest diameter was free-hand annotated at baseline and matching follow-up section to create TICs. Our method used a trilinear fit, separating the static phase, enhancement phase, and wash-out phase. Linear discriminate analysis (LDA) was trained to predict response based on the curve changes after treatment. One-sided T-test was used to test the statistical differences between groups. Results or Findings: Using this method we could discriminate responders (n=4) from non-responders (n=8), classified with RECIST. Our curve fit showed that after treatment maximum enhancement increased by 42% in responders and 7% in non-responders (p=0.02). Enhancement slope increased with 140% in responders and 3% in non-responders (p=0.06). Changes in static enhancement and TTP did not significantly differ. Linear discriminant analysis with all four features classified treatment response with 92% accuracy. Conclusion: We developed an AI-assisted tool with a robust, kinetic model-independent method for TIC analysis in CTP resulting in perfusion features that distinguish between responders and non-responders in PDAC after chemotherapy. Limitations:Early results of a feasibility study, future work will include more patients and comparison with pharmacokinetic models.