Justin K Mikell1, Armeen Mahvash2, Wendy Siman1, Veera Baladandayuthapani3, Firas Mourtada4, S Cheenu Kappadath5. 1. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas. 2. Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas. 3. The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. 4. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, Christiana Care, Newark, Delaware; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania. 5. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas. Electronic address: skappadath@mdanderson.org.
Abstract
PURPOSE: To quantify differences that exist between dosimetry models used for 90Y selective internal radiation therapy (SIRT). METHODS AND MATERIALS: Retrospectively, 37 tumors were delineated on 19 post-therapy quantitative 90Y single photon emission computed tomography/computed tomography scans. Using matched volumes of interest (VOIs), absorbed doses were reported using 3 dosimetry models: glass microsphere package insert standard model (SM), partition model (PM), and Monte Carlo (MC). Univariate linear regressions were performed to predict mean MC from SM and PM. Analysis was performed for 2 subsets: cases with a single tumor delineated (best case for PM), and cases with multiple tumors delineated (typical clinical scenario). Variability in PM from the ad hoc placement of a single spherical VOI to estimate the entire normal liver activity concentration for tumor (T) to nontumoral liver (NL) ratios (TNR) was investigated. We interpreted the slope of the resulting regression as bias and the 95% prediction interval (95%PI) as uncertainty. MCNLsingle represents MC absorbed doses to the NL for the single tumor patient subset; other combinations of calculations follow a similar naming convention. RESULTS: SM was unable to predict MCTsingle or MCTmultiple (p>.12, 95%PI >±177 Gy). However, SMsingle was able to predict (p<.012) MCNLsingle, albeit with large uncertainties; SMsingle and SMmultiple yielded biases of 0.62 and 0.71, and 95%PI of ±40 and ± 32 Gy, respectively. PMTsingle and PMTmultiple predicted (p<2E-6) MCTsingle and MCTmultiple with biases of 0.52 and 0.54, and 95%PI of ±38 and ± 111 Gy, respectively. The TNR variability in PMTsingle increased the 95%PI for predicting MCTsingle (bias = 0.46 and 95%PI = ±103 Gy). The TNR variability in PMTmultiple modified the bias when predicting MCTmultiple (bias = 0.32 and 95%PI = ±110 Gy). CONCLUSIONS: The SM is unable to predict mean MC tumor absorbed dose. The PM is statistically correlated with mean MC, but the resulting uncertainties in predicted MC are large. Large differences observed between dosimetry models for 90Y SIRT warrant caution when interpreting published SIRT absorbed doses. To reduce uncertainty, we suggest the entire NL VOI be used for TNR estimates when using PM.
PURPOSE: To quantify differences that exist between dosimetry models used for 90Y selective internal radiation therapy (SIRT). METHODS AND MATERIALS: Retrospectively, 37 tumors were delineated on 19 post-therapy quantitative 90Y single photon emission computed tomography/computed tomography scans. Using matched volumes of interest (VOIs), absorbed doses were reported using 3 dosimetry models: glass microsphere package insert standard model (SM), partition model (PM), and Monte Carlo (MC). Univariate linear regressions were performed to predict mean MC from SM and PM. Analysis was performed for 2 subsets: cases with a single tumor delineated (best case for PM), and cases with multiple tumors delineated (typical clinical scenario). Variability in PM from the ad hoc placement of a single spherical VOI to estimate the entire normal liver activity concentration for tumor (T) to nontumoral liver (NL) ratios (TNR) was investigated. We interpreted the slope of the resulting regression as bias and the 95% prediction interval (95%PI) as uncertainty. MCNLsingle represents MC absorbed doses to the NL for the single tumorpatient subset; other combinations of calculations follow a similar naming convention. RESULTS:SM was unable to predict MCTsingle or MCTmultiple (p>.12, 95%PI >±177 Gy). However, SMsingle was able to predict (p<.012) MCNLsingle, albeit with large uncertainties; SMsingle and SMmultiple yielded biases of 0.62 and 0.71, and 95%PI of ±40 and ± 32 Gy, respectively. PMTsingle and PMTmultiple predicted (p<2E-6) MCTsingle and MCTmultiple with biases of 0.52 and 0.54, and 95%PI of ±38 and ± 111 Gy, respectively. The TNR variability in PMTsingle increased the 95%PI for predicting MCTsingle (bias = 0.46 and 95%PI = ±103 Gy). The TNR variability in PMTmultiple modified the bias when predicting MCTmultiple (bias = 0.32 and 95%PI = ±110 Gy). CONCLUSIONS: The SM is unable to predict mean MC tumor absorbed dose. The PM is statistically correlated with mean MC, but the resulting uncertainties in predicted MC are large. Large differences observed between dosimetry models for 90Y SIRT warrant caution when interpreting published SIRT absorbed doses. To reduce uncertainty, we suggest the entire NL VOI be used for TNR estimates when using PM.
Authors: Maarten L J Smits; Mattijs Elschot; Daniel Y Sze; Yung H Kao; Johannes F W Nijsen; Andre H Iagaru; Hugo W A M de Jong; Maurice A A J van den Bosch; Marnix G E H Lam Journal: Cardiovasc Intervent Radiol Date: 2014-12-24 Impact factor: 2.740
Authors: Harun Ilhan; Anna Goritschan; Philipp Paprottka; Tobias F Jakobs; Wolfgang P Fendler; Andrei Todica; Peter Bartenstein; Marcus Hacker; Alexander R Haug Journal: J Nucl Med Date: 2015-08-27 Impact factor: 10.057
Authors: Mattijs Elschot; Johannes F W Nijsen; Marnix G E H Lam; Maarten L J Smits; Jip F Prince; Max A Viergever; Maurice A A J van den Bosch; Bernard A Zonnenberg; Hugo W A M de Jong Journal: Eur J Nucl Med Mol Imaging Date: 2014-05-13 Impact factor: 9.236
Authors: Mattijs Elschot; Marnix G E H Lam; Maurice A A J van den Bosch; Max A Viergever; Hugo W A M de Jong Journal: J Nucl Med Date: 2013-08-01 Impact factor: 10.057
Authors: C Chiesa; M Mira; M Maccauro; C Spreafico; R Romito; C Morosi; T Camerini; M Carrara; S Pellizzari; A Negri; G Aliberti; C Sposito; S Bhoori; A Facciorusso; E Civelli; R Lanocita; B Padovano; M Migliorisi; M C De Nile; E Seregni; A Marchianò; F Crippa; V Mazzaferro Journal: Eur J Nucl Med Mol Imaging Date: 2015-06-27 Impact factor: 9.236
Authors: Marta Cremonesi; Carlo Chiesa; Lidia Strigari; Mahila Ferrari; Francesca Botta; Francesco Guerriero; Concetta De Cicco; Guido Bonomo; Franco Orsi; Lisa Bodei; Amalia Di Dia; Chiara Maria Grana; Roberto Orecchia Journal: Front Oncol Date: 2014-08-19 Impact factor: 6.244
Authors: Justin K Mikell; Ravi K Kaza; Peter L Roberson; Kelly C Younge; Ravi N Srinivasa; Bill S Majdalany; Kyle C Cuneo; Dawn Owen; Theresa Devasia; Matthew J Schipper; Yuni K Dewaraja Journal: EJNMMI Phys Date: 2018-11-30
Authors: Remco Bastiaannet; S Cheenu Kappadath; Britt Kunnen; Arthur J A T Braat; Marnix G E H Lam; Hugo W A M de Jong Journal: EJNMMI Phys Date: 2018-11-02