Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation

Background and purpose

Computed tomography (CT) imaging poses challenges for delineation of soft tissue structures for prostate cancer external beam radiotherapy. Guidelines require the input of magnetic resonance imaging (MRI) information. We developed a deep learning (DL) prostate and organ-at-risk contouring model designed to find the MRI-truth in CT imaging.

Material and Methods

The study utilized CT-scan data from 165 prostate cancer patients, with 136 scans for training and 29 for testing. The research focused on contouring five regions of interest (ROIs): clinical target volume of the prostate including the venous plexus (VP) (CTV-iVP) and excluding the VP (CTV-eVP), bladder, anorectum and the whole seminal vesicles (SV) according to The European Society for Radiotherapy and Oncology (ESTRO) and Advisory Committee on Radiation Oncology Practice (ACROP) contouring guidelines. Human delineation included fusion of MRI-imaging with the planning CT-scans in the process, but the model itself has never been shown MRI-images during its development. Model training involved a three-dimensional U-Net architecture. A qualitative review was independently performed by two clinicians scoring the model on time-based criteria and the DL segmentation results were compared to manual adaptations using the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95).

Results

The qualitative review of DL segmentations for CTV-iVP and CTV-eVP showed 2 or 3 out of 3 in 96 % of cases, indicating minimal manual adjustments were needed by clinicians. The DL model demonstrated comparable quantitative performance in delineating CTV-iVP and CTV-eVP with a DSC of 89 % with a standard deviation of 3.3 %. HD95 is 4 mm for CTV-iVP and 4.1 mm CTV-eVP with a standard deviation of 2.1 mm for both contours. Anorectum, bladder and SV scored 3 out of 3 in the qualitative analysis in 62 %, 72 % and 55 % of cases respectively. DSC and HD95 are 90 % and 5.5 mm for anorectum, 96 % and 2.9 mm for bladder, and 81 % and 4.6 mm for the seminal vesicles.

Conclusion

To our knowledge, this is the first DL model designed to implement MRI contouring guidelines in CT imaging and the first model trained according to ESTRO-ACROP contouring guidelines. This CT-based DL model presents a valuable tool for aiding prostate delineation without requiring the actual MRI information.