Usefulness of semi-automatic harmonization strategy of standardized uptake values for multicenter PET studies

This study assessed the possibility of semi-automatic harmonization of standardized uptake values (SUVs) in multicenter studies. Phantom data were acquired using 16 PET/CT scanners (including 3 PET/CT scanners with a silicon photomultiplier detector). PET images obtained using 30-min/bed scans for optimum harmonization filter calculations and using 90–180-s/bed scans for SUV validation under clinical conditions were obtained. Time of flight and a reconstruction method with point-spread function correction were allowed. The optimal full width at half maximum of the 3D-Gaussian filter that minimizes the root mean square error with the median value of the JSNM harmonization range was calculated semi-automatically. The SUVmax and the SUVpeak of the hot spheres were measured, and the inter-scanner coefficient of variation (COV) was calculated before and after harmonization. The harmonization filter was applied to 11 of the 15 PET/CT scanners in which the SUV calibration accuracy had been verified, but not in the remaining 4 scanners. Under noiseless conditions before harmonization, the inter-scanner COVs of the SUVmax and the SUVpeak were as high as 21.57% and 12.20%, respectively, decreasing to 8.79% and 5.73% after harmonization, respectively. Harmonization brought the SUVmax of all the hot spheres to within the harmonization range. Even under clinical conditions affected by image noise, the inter-scanner COVs for the SUVmax and SUVpeak were as high as 8.83% and 5.18% after harmonization, respectively. By applying an optimal harmonization filter that is calculated semi-automatically, the harmonization of SUVs according to the JSNM strategy is possible in multicenter studies, thereby reducing inter-scanner COVs.

[1]  Elena Prieto,et al.  Impact of Time-of-Flight and Point-Spread-Function in SUV Quantification for Oncological PET , 2013, Clinical nuclear medicine.

[2]  K. Akashi,et al.  Standardization of image quality across multiple centers by optimization of acquisition and reconstruction parameters with interim FDG-PET/CT for evaluating diffuse large B cell lymphoma , 2013, Annals of Nuclear Medicine.

[3]  A. Kuten,et al.  Clinical performance of PET/CT in evaluation of cancer: additional value for diagnostic imaging and patient management. , 2003, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[4]  N. Aide,et al.  Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[5]  N. Obuchowski,et al.  The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy. , 2020, Radiology.

[6]  Jeong Won Lee,et al.  18F-FDG PET/CT Can Predict Survival of Advanced Hepatocellular Carcinoma Patients: A Multicenter Retrospective Cohort Study , 2017, The Journal of Nuclear Medicine.

[7]  R. Boellaard,et al.  Quantitative implications of the updated EARL 2019 PET–CT performance standards , 2019, EJNMMI Physics.

[8]  W. Oyen,et al.  EANM/EARL FDG-PET/CT accreditation - summary results from the first 200 accredited imaging systems , 2017, European Journal of Nuclear Medicine and Molecular Imaging.

[9]  R. Boellaard,et al.  EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies , 2017, European Journal of Nuclear Medicine and Molecular Imaging.

[10]  D. Binns,et al.  Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[11]  Eric J. W. Visser,et al.  FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[12]  P. Christian,et al.  Quantitative PET/CT Scanner Performance Characterization Based Upon the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network Oncology Clinical Simulator Phantom , 2015, The Journal of Nuclear Medicine.

[13]  Martin A Lodge,et al.  Noise Considerations for PET Quantification Using Maximum and Peak Standardized Uptake Value , 2012, The Journal of Nuclear Medicine.

[14]  D. Binns,et al.  Does PET SUV Harmonization Affect PERCIST Response Classification? , 2016, The Journal of Nuclear Medicine.

[15]  M. Senda,et al.  Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of Version 2.0 , 2014, Annals of Nuclear Medicine.

[16]  Ronald Boellaard,et al.  Mutatis Mutandis: Harmonize the Standard! , 2012, The Journal of Nuclear Medicine.

[17]  Yuji Tsutsui,et al.  Multicentre analysis of PET SUV using vendor-neutral software: the Japanese Harmonization Technology (J-Hart) study , 2018, EJNMMI Research.

[18]  Robert Jeraj,et al.  Impact of the Definition of Peak Standardized Uptake Value on Quantification of Treatment Response , 2012, The Journal of Nuclear Medicine.

[19]  Martin A Lodge,et al.  Feasibility of state of the art PET/CT systems performance harmonisation , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[20]  Marc Kachelriess,et al.  Procedure guideline for tumor imaging with 18F-FDG PET/CT 1.0. , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[21]  Giuseppe Esposito,et al.  Appropriate Use Criteria for 18F-FDG PET/CT in Restaging and Treatment Response Assessment of Malignant Disease , 2017, The Journal of Nuclear Medicine.