M Veronese1, G Rizzo, E O Aboagye, A Bertoldo. 1. Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy.
Abstract
PURPOSE: [(18)F]Fluoro-3'-deoxy-3'-L-fluorothymidine ([(18)F]FLT) is a tissue proliferation marker which has been widely validated as a tumour-specific imaging tracer for PET. [(18)F]FLT uptake in breast cancer is generally quantified at the region level or through first-order statistical descriptors (mean or maximum value), approaches that ignore the known complexity and heterogeneity of cancer tissues. Our aims were: (1) to validate a robust and reproducible voxel-wise approach to the quantification of [(18)F]FLT PET data in breast cancer patients, and (2) to exploit the entire distribution of the [(18)F]FLT retention estimates and their variability in the tumour region for the prediction of early treatment response. METHODS: The dataset was derived from 15 patients with stage II-IV breast cancer, scanned twice before chemotherapy and once 1 week after therapy. Using RECIST criteria (after 60 days) nine patients were categorized as responders or nonresponders to treatment. Kinetic modelling (compartmental modelling, Patlak analysis and spectral analysis with iterative filter), tissue-to-plasma ratio and standardized uptake value were applied at the voxel level. Test-retest estimates were used to assess reproducibility and reliability of the [(18)F]FLT uptake values before and after therapy for responder/nonresponder prediction. RESULTS: All the methods provided a measure of [(18)F]FLT uptake that was reliable and reproducible with ICC >0.94. Moreover, a very strong correlation was found among the methods (R (2) > 0.81). All the methods provided a limited number of outliers (<20 % in tumour), with the exception of compartmental modelling (>25 %) which was therefore excluded from the prediction analysis. Differences between before and after therapy in mean voxel-wise uptake in tumour did not allow a complete responder/nonresponder classification. In contrast, considering the full estimate distributions within the tumour (changes in median and mode between before and after therapy) improved therapy response for all the analysed methods. CONCLUSION: We showed that kinetic modelling (Patlak and spectral analysis with iterative filter) applied voxel-wise allows appropriate [(18)F]FLT uptake estimation in breast cancer with good reproducibility. Notably, this study indicated that a more comprehensive statistical investigation could improve tumour characterization and prediction of treatment response.
PURPOSE: [(18)F]Fluoro-3'-deoxy-3'-L-fluorothymidine ([(18)F]FLT) is a tissue proliferation marker which has been widely validated as a tumour-specific imaging tracer for PET. [(18)F]FLT uptake in breast cancer is generally quantified at the region level or through first-order statistical descriptors (mean or maximum value), approaches that ignore the known complexity and heterogeneity of cancer tissues. Our aims were: (1) to validate a robust and reproducible voxel-wise approach to the quantification of [(18)F]FLT PET data in breast cancer patients, and (2) to exploit the entire distribution of the [(18)F]FLT retention estimates and their variability in the tumour region for the prediction of early treatment response. METHODS: The dataset was derived from 15 patients with stage II-IV breast cancer, scanned twice before chemotherapy and once 1 week after therapy. Using RECIST criteria (after 60 days) nine patients were categorized as responders or nonresponders to treatment. Kinetic modelling (compartmental modelling, Patlak analysis and spectral analysis with iterative filter), tissue-to-plasma ratio and standardized uptake value were applied at the voxel level. Test-retest estimates were used to assess reproducibility and reliability of the [(18)F]FLT uptake values before and after therapy for responder/nonresponder prediction. RESULTS: All the methods provided a measure of [(18)F]FLT uptake that was reliable and reproducible with ICC >0.94. Moreover, a very strong correlation was found among the methods (R (2) > 0.81). All the methods provided a limited number of outliers (<20 % in tumour), with the exception of compartmental modelling (>25 %) which was therefore excluded from the prediction analysis. Differences between before and after therapy in mean voxel-wise uptake in tumour did not allow a complete responder/nonresponder classification. In contrast, considering the full estimate distributions within the tumour (changes in median and mode between before and after therapy) improved therapy response for all the analysed methods. CONCLUSION: We showed that kinetic modelling (Patlak and spectral analysis with iterative filter) applied voxel-wise allows appropriate [(18)F]FLT uptake estimation in breast cancer with good reproducibility. Notably, this study indicated that a more comprehensive statistical investigation could improve tumour characterization and prediction of treatment response.
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