TY - JOUR
T1 - Manual Versus Artificial Intelligence-Based Segmentations as a Pre-processing Step in Whole-body PET Dosimetry Calculations
AU - van Sluis, Joyce
AU - Noordzij, Walter
AU - de Vries, Elisabeth G.E.
AU - Kok, Iris C.
AU - de Groot, Derk Jan A.
AU - Jalving, Mathilde
AU - Lub-de Hooge, Marjolijn N.
AU - Brouwers, Adrienne H.
AU - Boellaard, Ronald
N1 - Funding Information:
This work was supported by the Dutch Cancer Society Grant POINTING (RUG 2016–10034) and the Innovative Medicines Initiatives2 Joint Undertaking project TRISTAN under grant agreement GA no. 116106. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EPFIA.
Publisher Copyright:
© 2022, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - Purpose: As novel tracers are continuously under development, it is important to obtain reliable radiation dose estimates to optimize the amount of activity that can be administered while keeping radiation burden within acceptable limits.Organ segmentation is required for quantification of specific uptake in organs of interest and whole-body dosimetry but is a time-consuming task which induces high interobserver variability. Therefore, we explored using manual segmentations versus an artificial intelligence (AI)-based automated segmentation tool as a pre-processing step for calculating whole-body effective doses to determine the influence of variability in volumetric whole-organ segmentations on dosimetry.Procedures: PET/CT data of six patients undergoing imaging with 89Zr-labelled pembrolizumab were included. Manual organ segmentations were performed, using in-house developed software, and biodistribution information was obtained. Based on the activity biodistribution information, residence times were calculated. The residence times served as input for OLINDA/EXM version 1.0 (Vanderbilt University, 2003) to calculate the whole-body effective dose (mSv/MBq).Subsequently, organ segmentations were performed using RECOMIA, a cloud-based AI platform for nuclear medicine and radiology research. The workflow for calculating residence times and whole-body effective doses, as described above, was repeated.Results: Data were acquired on days 2, 4, and 7 post-injection, resulting in 18 scans. Overall analysis time per scan was approximately 4 h for manual segmentations compared to ≤ 30 min using AI-based segmentations. Median Jaccard similarity coefficients between manual and AI-based segmentations varied from 0.05 (range 0.00–0.14) for the pancreas to 0.78 (range 0.74–0.82) for the lungs. Whole-body effective doses differed minimally for the six patients with a median difference in received mSv/MBq of 0.52% (range 0.15–1.95%).Conclusion: This pilot study suggests that whole-body dosimetry calculations can benefit from fast, automated AI-based whole organ segmentations.
AB - Purpose: As novel tracers are continuously under development, it is important to obtain reliable radiation dose estimates to optimize the amount of activity that can be administered while keeping radiation burden within acceptable limits.Organ segmentation is required for quantification of specific uptake in organs of interest and whole-body dosimetry but is a time-consuming task which induces high interobserver variability. Therefore, we explored using manual segmentations versus an artificial intelligence (AI)-based automated segmentation tool as a pre-processing step for calculating whole-body effective doses to determine the influence of variability in volumetric whole-organ segmentations on dosimetry.Procedures: PET/CT data of six patients undergoing imaging with 89Zr-labelled pembrolizumab were included. Manual organ segmentations were performed, using in-house developed software, and biodistribution information was obtained. Based on the activity biodistribution information, residence times were calculated. The residence times served as input for OLINDA/EXM version 1.0 (Vanderbilt University, 2003) to calculate the whole-body effective dose (mSv/MBq).Subsequently, organ segmentations were performed using RECOMIA, a cloud-based AI platform for nuclear medicine and radiology research. The workflow for calculating residence times and whole-body effective doses, as described above, was repeated.Results: Data were acquired on days 2, 4, and 7 post-injection, resulting in 18 scans. Overall analysis time per scan was approximately 4 h for manual segmentations compared to ≤ 30 min using AI-based segmentations. Median Jaccard similarity coefficients between manual and AI-based segmentations varied from 0.05 (range 0.00–0.14) for the pancreas to 0.78 (range 0.74–0.82) for the lungs. Whole-body effective doses differed minimally for the six patients with a median difference in received mSv/MBq of 0.52% (range 0.15–1.95%).Conclusion: This pilot study suggests that whole-body dosimetry calculations can benefit from fast, automated AI-based whole organ segmentations.
KW - Artificial intelligence
KW - Dosimetry
KW - Segmentation
U2 - 10.1007/s11307-022-01775-5
DO - 10.1007/s11307-022-01775-5
M3 - Article
C2 - 36195742
AN - SCOPUS:85139411113
SN - 1536-1632
VL - 25
SP - 435
EP - 441
JO - Molecular Imaging and Biology
JF - Molecular Imaging and Biology
ER -