Revista de informática médica y de salud

Pilot Study: Two-Stage Hybrid Model to Correlate Single Energy CT and PET in Pancreatic Adenocarcinoma

Abstract

Min Zhang ,Jorge Oldan ,Miao He ,Teresa Wu *,Alvin Silva ,Jing Li ,J Ross Mitchell ,William M Pavlicek ,Michael C Roarke ,Amy K Hara

Pancreas adenocarcinoma is one of the most common malignant tumors and the fourth leading cause of cancerrelated mortality. While Computed Tomography (CT) has been commonly used clinically for the cancer staging and follow-up, Positron Emission Tomography (PET) is known to be generally more accurate and sensitive for metastases and thus has great prognostic value. However, PET is more expensive and less accessible. This research is to explore the use of multivariate models to extract valuable information from CT to mimic the effects of PET. Based on the original 6 CT measures, 10 CT biomarkers are derived. The strongest correlation with PET SUV in the multivariate regression on the 6 original measures is r2=0.41 (r=0.64), on the 10 derived biomarkers is r2=0.55 (r=0.74). We developed a twostage hybrid model, where a multivariate classifier was developed to first separate the patients into the group with high SUV values vs. low SUV values, then the regression model was developed for each group respectively. The overall performance of this two-stage model is more promising with an r2=0.81 (r=0.90). We conclude advanced CT analytics has the potential to extract valuable information that correlates with PET SUV. Rationale and objectives: Pancreatic adenocarcinoma is commonly studied by CT and PET. We aimed to see if information from CT could be used to simulate the results of PET. Materials and methods: A retrospective study of 24 patients with pancreatic cancer who had both CT and PET in close temporal proximity was conducted. Measurements of the aorta, normal pancreatic tissue, solid and cystic portions of pancreatic tumors were performed resulting in 6 biomarkers. Ten more biomarkers were derived including the ratios of solid and cystic tumor mean and standard deviation to normal pancreas (and to each other), as well as signal-to-noise ratios of solid and cystic tumors to normal pancreas. Univariate analysis and multivariate regression were conducted on the original measures (6 biomarkers) and derived measures (10 biomarkers). A two-stage hybrid model integrating machine learning model with multivariate regression analysis was also studied. Results: The best results were obtained using the two-stage hybrid model. The regression model for low SUV (≤5) used cystic tumor mean (r2=0.68, r=0.83). The regression model for high SUV(>5) used tumor mean, the ratios of tumor mean to pancreas mean, tumor mean to aorta mean, standard deviation of tumor to aorta mean and signal-to-noise ratio of difference between the normal pancreas mean and solid tumor mean to standard deviation of pancreas (r2=0.86, r=0.93). The overall performance of the two-stage model is r2=0.81(r=0.90). Conclusion: Two-stage multivariate analysis of CT parameters can mimic the effects of PET to a reasonable extent, and signal-to-noise and standard deviation ratios may capture the essential nonlinearity of these relationships.