Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.

ABSTRACT:

Background and purpose

To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.

Materials and methods

Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared.

Results

Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians

SUBMITTER: Trimpl MJ 

PROVIDER: S-EPMC12181082 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature