Joseph Ryan Petrulli1,2,3, Mingqiang Zheng2, Yiyun Huang2, Nabeel B Nabulsi2, Sarah B Goldberg4, Joseph N Contessa4, Evan D Morris1,2. 1. Department of Biomedical Engineering, Yale University New Haven, CT, United States. 2. Department of Radiology and Biomedical Imaging, Yale University New Haven, CT, United States. 3. Invicro, A Konica Minolta Company New Haven, CT, United States. 4. Department of Therapeutic Radiology, Yale University New Haven, CT, United States.
Abstract
BACKGROUND: [11C]-Erlotinib is a radiolabeled analogue of a tyrosine kinase inhibitor used to treat non-small cell lung cancer (NSCLC) which expresses specific kinase domain mutations of the epidermal growth factor receptor (EGFR). In this study, 10 subjects with NSCLC and assorted EGFR mutation status underwent a dynamic, multi-bed positron emission tomography (PET) scan using [11C]-erlotinib. Data were analyzed using a variety of quantitative techniques common in PET (graphical methods, kinetic models, and uptake value-based endpoints). Our primary goal was to determine the most reliable imaging endpoint given the need for maintaining minimal patient burden and recognizing the advantage of simple calculations in future trials. RESULTS: Standard uptake values (a semi-quantitative endpoint) were well correlated with both binding potential and volume of distribution (fully quantitative endpoints). Normalized tracer uptake was found to stabilize approximately 60 minutes post tracer injection. Conclusions: The kinetic properties of [11C]-erlotinib varied greatly across subjects. Our novel scanning protocol produced an important dataset which highlights the great heterogeneity of NSCLC and its apparent impact on [11C]-erlotinib kinetics. A lack of correlation between EGFR mutational status and quantitative endpoints appears to be due to disease heterogeneity and low tracer uptake. The most reliable fits of the dynamic data were based on the one-tissue compartmental model which were well correlated with mean SUV. Due to this correlation and good stability at late-time, SUV seems sufficiently well-suited to quantitative imaging of NSCLC lesions in the whole body with [11C]-erlotinib. AJNMMI
BACKGROUND: [11C]-Erlotinib is a radiolabeled analogue of a tyrosine kinase inhibitor used to treat non-small cell lung cancer (NSCLC) which expresses specific kinase domain mutations of the epidermal growth factor receptor (EGFR). In this study, 10 subjects with NSCLC and assorted EGFR mutation status underwent a dynamic, multi-bed positron emission tomography (PET) scan using [11C]-erlotinib. Data were analyzed using a variety of quantitative techniques common in PET (graphical methods, kinetic models, and uptake value-based endpoints). Our primary goal was to determine the most reliable imaging endpoint given the need for maintaining minimal patient burden and recognizing the advantage of simple calculations in future trials. RESULTS: Standard uptake values (a semi-quantitative endpoint) were well correlated with both binding potential and volume of distribution (fully quantitative endpoints). Normalized tracer uptake was found to stabilize approximately 60 minutes post tracer injection. Conclusions: The kinetic properties of [11C]-erlotinib varied greatly across subjects. Our novel scanning protocol produced an important dataset which highlights the great heterogeneity of NSCLC and its apparent impact on [11C]-erlotinib kinetics. A lack of correlation between EGFR mutational status and quantitative endpoints appears to be due to disease heterogeneity and low tracer uptake. The most reliable fits of the dynamic data were based on the one-tissue compartmental model which were well correlated with mean SUV. Due to this correlation and good stability at late-time, SUV seems sufficiently well-suited to quantitative imaging of NSCLC lesions in the whole body with [11C]-erlotinib. AJNMMI
Authors: Paul A Yushkevich; Joseph Piven; Heather Cody Hazlett; Rachel Gimpel Smith; Sean Ho; James C Gee; Guido Gerig Journal: Neuroimage Date: 2006-03-20 Impact factor: 6.556
Authors: J Ryan Petrulli; Jenna M Sullivan; Ming-Qiang Zheng; Daniel C Bennett; Jonathan Charest; Yiyun Huang; Evan D Morris; Joseph N Contessa Journal: Neoplasia Date: 2013-12 Impact factor: 5.715
Authors: Floris H P van Velden; Patsuree Cheebsumon; Maqsood Yaqub; Egbert F Smit; Otto S Hoekstra; Adriaan A Lammertsma; Ronald Boellaard Journal: Eur J Nucl Med Mol Imaging Date: 2011-05-27 Impact factor: 9.236
Authors: E A Eisenhauer; P Therasse; J Bogaerts; L H Schwartz; D Sargent; R Ford; J Dancey; S Arbuck; S Gwyther; M Mooney; L Rubinstein; L Shankar; L Dodd; R Kaplan; D Lacombe; J Verweij Journal: Eur J Cancer Date: 2009-01 Impact factor: 9.162
Authors: Ashfaque A Memon; Steen Jakobsen; Frederik Dagnaes-Hansen; Boe S Sorensen; Susanne Keiding; Ebba Nexo Journal: Cancer Res Date: 2009-01-20 Impact factor: 12.701
Authors: George Crișan; Nastasia Sanda Moldovean-Cioroianu; Diana-Gabriela Timaru; Gabriel Andrieș; Călin Căinap; Vasile Chiș Journal: Int J Mol Sci Date: 2022-04-30 Impact factor: 6.208