Affiliation:
1. Complejo Hospitalario Universitario de Toledo
2. University General Hospital
3. Castilla-La Mancha University
4. Sahlgrenska University Hospital
5. Castilla-La Mancha University. Ciudad Real
Abstract
Abstract
Aim
To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumour.
Material and methods
Patients with a recent diagnosis of intermediate/high risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose.
In positive-PSMA scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with both software. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers.
In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained.
We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen’s kappa coefficient (k) for the concordance analysis.
Results
Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p<0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k=0.733; p<0.001 and k=0.812; p<0.001, respectively) although the concordances with histopathology were moderate (p<0001).
Conclusions
The analytical validation of aPROMISE shows a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.
Publisher
Research Square Platform LLC