A Pipeline for Automated Voxel Dosimetry: Application in Patients with Multi-SPECT/CT Imaging After 177 Lu-Peptide Receptor Radionuclide Therapy.

Autor:	Dewaraja YK; Department of Radiology, University of Michigan, Ann Arbor, Michigan; yuni@umich.edu., Mirando DM; MIM Software Inc., Cleveland, Ohio., Peterson AB; Department of Radiology, University of Michigan, Ann Arbor, Michigan.; Department of Radiation Oncology, Wayne State University, Detroit, Michigan; and., Niedbala J; Department of Radiology, University of Michigan, Ann Arbor, Michigan., Millet JD; Department of Radiology, University of Michigan, Ann Arbor, Michigan., Mikell JK; Radiation Oncology, University of Michigan, Ann Arbor, Michigan., Frey KA; Department of Radiology, University of Michigan, Ann Arbor, Michigan., Wong KK; Department of Radiology, University of Michigan, Ann Arbor, Michigan., Wilderman SJ; Department of Radiology, University of Michigan, Ann Arbor, Michigan., Nelson AS; MIM Software Inc., Cleveland, Ohio.
Jazyk:	angličtina
Zdroj:	Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2022 Nov; Vol. 63 (11), pp. 1665-1672. Date of Electronic Publication: 2022 Apr 14.
DOI:	10.2967/jnumed.121.263738
Abstrakt:	Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in 177 Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one post-therapy SPECT/CT scan. Second, local contour intensity-based SPECT--SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point 177 Lu SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2%-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment, and the voxel-level fitting had high R 2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2-10.4) for lesions, 0.49 (range, 0.24-1.02) for left kidney, 0.54 (range, 0.31-1.07) for right kidney, and 0.51 (range, 0.27-1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%-17%) for organs and 10% (range, 3%-37%) for lesions. For a typical patient, the time for the entire process was about 25 min (∼2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT. (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)
Databáze:	MEDLINE
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