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(18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h...

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Autores principales: Besson, Florent L., Fernandez, Brice, Faure, Sylvain, Mercier, Olaf, Seferian, Andrei, Mignard, Xavier, Mussot, Sacha, le Pechoux, Cecile, Caramella, Caroline, Botticella, Angela, Levy, Antonin, Parent, Florence, Bulifon, Sophie, Montani, David, Mitilian, Delphine, Fadel, Elie, Planchard, David, Besse, Benjamin, Ghigna-Bellinzoni, Maria-Rosa, Comtat, Claude, Lebon, Vincent, Durand, Emmanuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392998/
https://www.ncbi.nlm.nih.gov/pubmed/32734484
http://dx.doi.org/10.1186/s13550-020-00671-9
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author Besson, Florent L.
Fernandez, Brice
Faure, Sylvain
Mercier, Olaf
Seferian, Andrei
Mignard, Xavier
Mussot, Sacha
le Pechoux, Cecile
Caramella, Caroline
Botticella, Angela
Levy, Antonin
Parent, Florence
Bulifon, Sophie
Montani, David
Mitilian, Delphine
Fadel, Elie
Planchard, David
Besse, Benjamin
Ghigna-Bellinzoni, Maria-Rosa
Comtat, Claude
Lebon, Vincent
Durand, Emmanuel
author_facet Besson, Florent L.
Fernandez, Brice
Faure, Sylvain
Mercier, Olaf
Seferian, Andrei
Mignard, Xavier
Mussot, Sacha
le Pechoux, Cecile
Caramella, Caroline
Botticella, Angela
Levy, Antonin
Parent, Florence
Bulifon, Sophie
Montani, David
Mitilian, Delphine
Fadel, Elie
Planchard, David
Besse, Benjamin
Ghigna-Bellinzoni, Maria-Rosa
Comtat, Claude
Lebon, Vincent
Durand, Emmanuel
author_sort Besson, Florent L.
collection PubMed
description OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T(1)-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (r(s)) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute r(s) values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute r(s) values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION: A dynamic “one-stop shop” procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.
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spelling pubmed-73929982020-08-18 (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data Besson, Florent L. Fernandez, Brice Faure, Sylvain Mercier, Olaf Seferian, Andrei Mignard, Xavier Mussot, Sacha le Pechoux, Cecile Caramella, Caroline Botticella, Angela Levy, Antonin Parent, Florence Bulifon, Sophie Montani, David Mitilian, Delphine Fadel, Elie Planchard, David Besse, Benjamin Ghigna-Bellinzoni, Maria-Rosa Comtat, Claude Lebon, Vincent Durand, Emmanuel EJNMMI Res Original Research OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T(1)-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (r(s)) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute r(s) values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute r(s) values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION: A dynamic “one-stop shop” procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC. Springer Berlin Heidelberg 2020-07-30 /pmc/articles/PMC7392998/ /pubmed/32734484 http://dx.doi.org/10.1186/s13550-020-00671-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Besson, Florent L.
Fernandez, Brice
Faure, Sylvain
Mercier, Olaf
Seferian, Andrei
Mignard, Xavier
Mussot, Sacha
le Pechoux, Cecile
Caramella, Caroline
Botticella, Angela
Levy, Antonin
Parent, Florence
Bulifon, Sophie
Montani, David
Mitilian, Delphine
Fadel, Elie
Planchard, David
Besse, Benjamin
Ghigna-Bellinzoni, Maria-Rosa
Comtat, Claude
Lebon, Vincent
Durand, Emmanuel
(18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title_full (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title_fullStr (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title_full_unstemmed (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title_short (18)F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data
title_sort (18)f-fdg pet and dce kinetic modeling and their correlations in primary nsclc: first voxel-wise correlative analysis of human simultaneous [18f]fdg pet-mri data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392998/
https://www.ncbi.nlm.nih.gov/pubmed/32734484
http://dx.doi.org/10.1186/s13550-020-00671-9
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