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.
AI-assisted analysis of CT perfusion to predict response in patients with pancreatic adenocarcinoma
T. Perik, J. Hermans and H. Huisman
European Congress of Radiology 2022.