Seierstad, T. | bioRxiv Science

bioRxiv2019

Inter-reader agreement of 18F-FDG PET/CT for the quantification of carotid artery plaque inflammation

BackgroundA significant proportion of ischemic strokes are caused by emboli from unstable atherosclerotic carotid artery plaques with inflammation being a key feature of plaque instability and stroke risk. Positron emission tomography (PET) depicting the uptake of 2-deoxy-2-(18F)-fluoro-D-glucose (18F-FDG) in carotid artery plaques is a promising technique to quantify plaque inflammation. A consensus on the methodology for plaque localization and quantification of inflammation by 18F-FDG PET/computed tomography (CT) in atherosclerosis has not been established. High inter-reader agreement is essential if 18F-FDG PET/CT is to be used as a clinical tool for the assessment of unstable plaques and stroke risk. The aim of our study was to assess the inter-reader variability of different methods for quantification of 18F-FDG uptake in carotid atherosclerotic plaques with a separate CT angiography (CTA) providing anatomical guidance. Methods and resultsForty-three patients with carotid artery stenosis [≥]70% underwent 18F-FDG PET/CT. Two independent readers separately delineated the plaque in all axial PET slices containing the atherosclerotic plaque and the maximum standardized uptake value (SUVmax) from each slice was measured. Uptake values with and without background correction were calculated. Intraclass correlation coefficients were highest for uncorrected uptake values (0.97-0.98) followed by those background corrected by subtraction (0.89-0.94) and lowest for those background corrected by division (0.74-0.79). There was a significant difference between the two readers definition of plaque extension, but this did not affect the inter-reader agreement of the uptake parameters. ConclusionsQuantification methods without background correction have the highest inter-reader agreement for 18F-FDG PET of carotid artery plaque inflammation. The use of the single highest uptake value (max SUVmax) from the plaque will facilitate the methods clinical utility in stroke prevention.

pathology↗

bioRxiv2018

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.

cancer biology↗