Qualitative and quantitative evaluation of auto-segmented clinical target volumes and organs at risk in radiotherapy of rectal cancer

Abstract Introduction: Manual contouring (MC) is time-consuming work in radiotherapy planning for rectal cancer. Artificial intelligence (AI) can reduce the time required for clinical target volume (CTV) and organs-at-risk (OARs) delineation. In this study, we evaluated the quality of auto-segmented CTVs and OARs. Methods: Dose-planning data were collected from ten patients who underwent preoperative radiotherapy for locally advanced rectal cancer in 2024. Auto-segmented structures from the AI-Rad and Contour+ software tools were added. Constructed AI-CTVs, based on Contour+ segmentations and AI-OARs, i.e., bladder, femoral heads and bowel bag, by both AI tools, were compared to their MC counterparts by use of quantitative metrics, volumetric/surface Dice similarity coefficients (vDSC/sDSC) and maximum/average Hausdorff distance (HD/aHD). The constructed AI-CTVs and MC counterparts were graded by two radiotherapists with two qualitative methods. Results: The median vDSC, sDSC, HD and aHD values of our constructed AI-CTVs compared with the MC-CTVs were 0.86, 0.61, 23.19 and 0.62 mm, respectively. For both AI tools, the agreement in the OAR metrics was overall good but less similar for the bowel bag. The qualitative evaluations of the AI-CTVs, compared to the MC-CTVs, were in clear favour of the MC-CTVs. The cranial-anterior nodal levels were anatomical areas with poorer coverage, where the contouring guidelines differed. Conclusion: The quality of our constructed AI-CTVs was inferior to the MC-CTVs. Thus, the auto-segmentation methods need further development on this aspect for use in the clinical setting. In contrast, the agreement of the quantitative metrics for the OARs was overall good, except for the bowel bag.