Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field. The displacement map for an optimized MRI treatment planning sequence was measured using a dedicated phantom in a 3 T MRI system. To simulate the distortion aspects of a synthetic CT (electron density derived from MR images), the displacement map was applied to CT images, referred to as distorted CT images. A volumetric modulated arc prostate treatment plan was applied to the original CT and the distorted CT, creating a reference and a distorted CT dose distribution. By applying the inverse of the displacement map to the distorted CT dose distribution, a dose distribution in the same geometry as the original CT images was created. For 10 prostate cancer patients, the dose difference between the reference dose distribution and inverse distorted CT dose distribution was analyzed in isodose level bins. The mean magnitude of the geometric distortion was 1.97 mm for the radial distance of 200-250 mm from isocenter. The mean percentage dose differences for all isodose level bins, were ⩽0.02% and the radiotherapy structure mean volume deviations were <0.2%. The method developed can quantify the dosimetric effects of MRI system specific distortion in a prostate MRI only radiotherapy workflow, separated from dosimetric effects originating from synthetic CT generation. No clinically relevant dose difference or structure deformation was found when 3D distortion correction and high acquisition bandwidth was used. The method could be used for any MRI sequence together with any anatomy of interest.
Purpose The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [18F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA PET/CT. Methods To evaluate the deep learning algorithm, the automated segmentations of the low-dose CT component of PSMA PET/CT scans from 20 patients were compared to manual segmentations. Dice scores were used to quantify the similarities between the automated and manual segmentations. Next, the automated quantification of tracer uptake in the reference organs and detection and pre-segmentation of potential lesions were evaluated in 339 patients with prostate cancer, who were all enrolled in the phase II/III OSPREY study. Three nuclear medicine physicians performed the retrospective independent reads of OSPREY images with aPROMISE. Quantitative consistency was assessed by the pairwise Pearson correlations and standard deviation between the readers and aPROMISE. The sensitivity of detection and pre-segmentation of potential lesions was evaluated by determining the percent of manually selected abnormal lesions that were automatically detected by aPROMISE. Results The Dice scores for bone segmentations ranged from 0.88 to 0.95. The Dice scores of the PSMA PET/CT reference organs, thoracic aorta and liver, were 0.89 and 0.97, respectively. Dice scores of other visceral organs, including prostate, were observed to be above 0.79. The Pearson correlation for blood pool reference was higher between any manual reader and aPROMISE, than between any pair of manual readers. The standard deviations of reference organ uptake across all patients as determined by aPROMISE (SD = 0.21 blood pool and SD = 1.16 liver) were lower compared to those of the manual readers. Finally, the sensitivity of aPROMISE detection and pre-segmentation was 91.5% for regional lymph nodes, 90.6% for all lymph nodes, and 86.7% for bone in metastatic patients. Conclusion In this analytical study, we demonstrated the segmentation accuracy of the deep learning algorithm, the consistency in quantitative assessment across multiple readers, and the high sensitivity in detecting potential lesions. The study provides a foundational framework for clinical evaluation of aPROMISE in standardized reporting of PSMA PET/CT.
Rationale: Standardized staging and quantitative reporting is necessary to demonstrate the association of 18 F-DCFPyL PET/CT (PSMA) imaging with clinical outcome. This work introduces an automated platform to implement and extend the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria -aPROMISE. The objective is to validate the performance of aPROMISE in staging and quantifying disease burden in patients with prostate cancer who undergo PSMA Imaging.Methods: This was a retrospective analysis of 109 Veterans with intermediate and high-risk prostate cancer, who underwent PSMA imaging. To validate the performance of aPROMISE, two independent nuclear-medicine physicians conducted aPROMISEassisted reads, resulting in standardized reports that quantify individual lesions and stage the patients. Patients were staged as having local only disease (miN0M0); regional lymph node only (miN1M0), metastatic disease only (miN0M1), and with both regional and distant metastatic disease (miN1M1). The staging obtained from aPROMISE-assisted reads was compared with the staging by conventional imaging. Cohen's pairwise kappa agreement was used to evaluate the inter-reader variability. Correlation coefficient and ICC was used to evaluate the inter-reader variability of the quantitative assessment (miPSMA-index) in each stage. Kendall Tau and t-test was used to evaluate the association of miPSMA-index with PSA and Gleason Score.Results: All PSMA images of 109 veterans met the DICOM conformity and the requirements for the aPROMISE analysis. Both independent aPROMISE-assisted analyses demonstrated significant upstaging in patients with localized (23%; N=20/87) and regional tumor burden (25%; N=2/8). However, a significant number of patients with bone metastases identified on conventional imaging (NaF PET/CT) were downstaged (29%; N=4/14). The comparison of the two independent aPROMISE-assisted reads demonstrated a high kappa agreement -0.82 (miN0M0), 0.90 (miN1M0), and 0.77 (miN0M1). The Spearman correlation of quantitative miPSMA-index was 0.93, 0.96 and 0.97, respectively. As a continuous variable, miPSMA index in the prostate (miT) was associated with risk groups defined by the PSA and Gleason.. Conclusion:Here we demonstrate consistency of the aPROMISE platform between readers and observed substantial upstaging in PSMA imaging compared to the conventional imaging. aPROMISE may contribute to the broader standardization of PSMA imaging assessment and to its clinical utility in management of prostate cancer patients.
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