Hugo Levillain1,2, Manuela Burghelea3, Ivan Duran Derijckere4, Thomas Guiot3, Akos Gulyban3, Bruno Vanderlinden3, Michael Vouche5, Patrick Flamen4, Nick Reynaert3. 1. Medical Physics Department, Jules Bordet Institute, Université Libre de Bruxelles, 1 Rue Héger-Bordet, B-1000, Brussels, Belgium. hugo.levillain@bordet.be. 2. Nuclear Medicine Department, Jules Bordet Institute, Université Libre de Bruxelles, 1 Rue Héger-Bordet, 1000, Brussels, Belgium. hugo.levillain@bordet.be. 3. Medical Physics Department, Jules Bordet Institute, Université Libre de Bruxelles, 1 Rue Héger-Bordet, B-1000, Brussels, Belgium. 4. Nuclear Medicine Department, Jules Bordet Institute, Université Libre de Bruxelles, 1 Rue Héger-Bordet, 1000, Brussels, Belgium. 5. Department of Radiology, Jules Bordet Institute, Université Libre de Bruxelles, 1 Rue Héger-Bordet, 1000, Brussels, Belgium.
Abstract
BACKGROUND: The relationship between the mean absorbed dose delivered to the tumour and the outcome in liver metastases from colorectal cancer patients treated with radioembolization has already been presented in several studies. The optimization of the personalized therapeutic activity to be administered is still an open challenge. In this context, how well the 99mTc-MAA SPECT/CT predicts the absorbed dose delivered by radioembolization is essential. This work aimed to analyse the differences between predictive 99mTc-MAA-SPECT/CT and post-treatment 90Y-microsphere PET/CT dosimetry at different levels. Dose heterogeneity was compared voxel-to-voxel using the quality-volume histograms, subsequently used to demonstrate how it could be used to identify potential clinical parameters that are responsible for quantitative discrepancies between predictive and post-treatment dosimetry. RESULTS: We analysed 130 lesions delineated in twenty-six patients. Dose-volume histograms were computed from predictive and post-treatment dosimetry for all volumes: individual lesion, whole tumoural liver (TL) and non-tumoural liver (NTL). For all dose-volume histograms, the following indices were extracted: D90, D70, D50, Dmean and D20. The results showed mostly no statistical differences between predictive and post-treatment dosimetries across all volumes and for all indices. Notably, the analysis showed no difference in terms of Dmean, confirming the results from previous studies. Quality factors representing the spread of the quality-volume histogram (QVH) curve around 0 (ideal QF = 0) were determined for lesions, TL and NTL. QVHs were classified into good (QF < 0.18), acceptable (0.18 ≤ QF < 0.3) and poor (QF ≥ 0.3) correspondence. For lesions and TL, dose- and quality-volume histograms are mostly concordant: 69% of lesions had a QF within good/acceptable categories (40% good) and 65% of TL had a QF within good/acceptable categories (23% good). For NTL, the results showed mixed results with 48% QF within the poor concordance category. Finally, it was demonstrated how QVH analysis could be used to define the parameters that predict the significant differences between predictive and post-treatment dose distributions. CONCLUSION: It was shown that the use of the QVH is feasible in assessing the predictive value of 99mTc-MAA SPECT/CT dosimetry and in estimating the absorbed dose delivered to liver metastases from colorectal cancer via 90Y-microspheres. QVH analyses could be used in combination with DVH to enhance the predictive value of 99mTc-MAA SPECT/CT dosimetry and to assist personalized activity prescription.
BACKGROUND: The relationship between the mean absorbed dose delivered to the tumour and the outcome in liver metastases from colorectal cancerpatients treated with radioembolization has already been presented in several studies. The optimization of the personalized therapeutic activity to be administered is still an open challenge. In this context, how well the 99mTc-MAA SPECT/CT predicts the absorbed dose delivered by radioembolization is essential. This work aimed to analyse the differences between predictive 99mTc-MAA-SPECT/CT and post-treatment 90Y-microsphere PET/CT dosimetry at different levels. Dose heterogeneity was compared voxel-to-voxel using the quality-volume histograms, subsequently used to demonstrate how it could be used to identify potential clinical parameters that are responsible for quantitative discrepancies between predictive and post-treatment dosimetry. RESULTS: We analysed 130 lesions delineated in twenty-six patients. Dose-volume histograms were computed from predictive and post-treatment dosimetry for all volumes: individual lesion, whole tumoural liver (TL) and non-tumoural liver (NTL). For all dose-volume histograms, the following indices were extracted: D90, D70, D50, Dmean and D20. The results showed mostly no statistical differences between predictive and post-treatment dosimetries across all volumes and for all indices. Notably, the analysis showed no difference in terms of Dmean, confirming the results from previous studies. Quality factors representing the spread of the quality-volume histogram (QVH) curve around 0 (ideal QF = 0) were determined for lesions, TL and NTL. QVHs were classified into good (QF < 0.18), acceptable (0.18 ≤ QF < 0.3) and poor (QF ≥ 0.3) correspondence. For lesions and TL, dose- and quality-volume histograms are mostly concordant: 69% of lesions had a QF within good/acceptable categories (40% good) and 65% of TL had a QF within good/acceptable categories (23% good). For NTL, the results showed mixed results with 48% QF within the poor concordance category. Finally, it was demonstrated how QVH analysis could be used to define the parameters that predict the significant differences between predictive and post-treatment dose distributions. CONCLUSION: It was shown that the use of the QVH is feasible in assessing the predictive value of 99mTc-MAA SPECT/CT dosimetry and in estimating the absorbed dose delivered to liver metastases from colorectal cancer via 90Y-microspheres. QVH analyses could be used in combination with DVH to enhance the predictive value of 99mTc-MAA SPECT/CT dosimetry and to assist personalized activity prescription.
Authors: Arthur J A T Braat; S Cheenu Kappadath; Rutger C G Bruijnen; Andor F van den Hoven; Armeen Mahvash; Hugo W A M de Jong; Marnix G E H Lam Journal: Lancet Oncol Date: 2017-10-31 Impact factor: 41.316
Authors: Hugo Levillain; Manuela Burghelea; Ivan Duran Derijckere; Thomas Guiot; Akos Gulyban; Bruno Vanderlinden; Michael Vouche; Patrick Flamen; Nick Reynaert Journal: EJNMMI Phys Date: 2021-01-27