Skip to main content
. 2022 Nov 8;24:121–128. doi: 10.1016/j.phro.2022.11.003

Table 1.

Methods of assessing auto-contouring using deep learning contouring (DLC) for prostate structures.

Centre 1 Centre 2 Centre 3
Number of patients for timing 9 42 manual, 42 DLC 10
Number of patients for quantitative analysis 9 10 N/A
Same patients for timing and quantitative analysis? Y N N/A
Timing Study design Paired Unpaired Paired
Number of staff 2 (both outlined 9 each) 4 10 for manual, 1 for DLCExpert
Staff group Radiotherapy Planners Radiotherapy Planners Radiotherapy Planners and clinicians. One clinician edited DLCExpert contours
Existing method Manual Manual Manual
CT scanner/slice thickness Philips Brilliance Big Bore (3 mm) GE Discovery (2.5 mm) GE Discovery (2.5 mm)
DLCExpert model name Prostate_CT_NL006_MO Prostate_CT_NL005_GN (bladder and rectum)Prostate_CT_NL010_NN (femoral heads) Prostate_CT_NL006_MO
DLCExpert model description Generic model based on data from a centre in the Netherlands. Generic models based on data from a centre in the Netherlands. Generic model based on data from a centre in the Netherlands.
Number of images for training model 437 242 (bladder and rectum)337 (femoral heads) 437
OARs Bladder, femoral heads, rectum Bladder, femoral heads, rectum Bladder, femoral heads, rectum
Targets None None Prostate, seminal vesicles
Editing software Pinnacle v16 (Philips, NL) RayStation v7 (RaySearch, Sweden) Eclipse v13.7 (Varian Medical Systems, Palo Alto, USA)
Outlining protocol basis CHHiP CHHiP, RTOG atlases CHHiP
Timing method Manual- time to draw or edit timed by the staff member Manual- time to draw or edit timed by the staff member Manual- time to draw or edit timed by the staff member Aria (Varian Medical Systems, Palo Alto, USA)-time to draw CTVs