Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.
Radiother Oncol
; 188: 109774, 2023 11.
Article
in En
| MEDLINE
| ID: mdl-37394103
ABSTRACT
PURPOSE:
With the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV) in prostate-specific membrane antigen PET (PSMA-PET) becomes crucial. Manual approaches are time-consuming and observer dependent. The purpose of this study was to create a deep learning model for the accurate delineation of the intraprostatic GTV in PSMA-PET.METHODS:
A 3D U-Net was trained on 128 different 18F-PSMA-1007 PET images from three different institutions. Testing was done on 52 patients including one independent internal cohort (Freiburg n = 19) and three independent external cohorts (Dresden n = 14 18F-PSMA-1007, Boston Massachusetts General Hospital (MGH) n = 9 18F-DCFPyL-PSMA and Dana-Farber Cancer Institute (DFCI) n = 10 68Ga-PSMA-11). Expert contours were generated in consensus using a validated technique. CNN predictions were compared to expert contours using Dice similarity coefficient (DSC). Co-registered whole-mount histology was used for the internal testing cohort to assess sensitivity/specificity.RESULTS:
Median DSCs were Freiburg 0.82 (IQR 0.73-0.88), Dresden 0.71 (IQR 0.53-0.75), MGH 0.80 (IQR 0.64-0.83) and DFCI 0.80 (IQR 0.67-0.84), respectively. Median sensitivity for CNN and expert contours were 0.88 (IQR 0.68-0.97) and 0.85 (IQR 0.75-0.88) (p = 0.40), respectively. GTV volumes did not differ significantly (p > 0.1 for all comparisons). Median specificity of 0.83 (IQR 0.57-0.97) and 0.88 (IQR 0.69-0.98) were observed for CNN and expert contours (p = 0.014), respectively. CNN prediction took 3.81 seconds on average per patient.CONCLUSION:
The CNN was trained and tested on internal and external datasets as well as histopathology reference, achieving a fast GTV segmentation for three PSMA-PET tracers with high diagnostic accuracy comparable to manual experts.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Prostatic Neoplasms
/
Deep Learning
Type of study:
Guideline
/
Prognostic_studies
Limits:
Humans
/
Male
Language:
En
Journal:
Radiother Oncol
Year:
2023
Document type:
Article