Comparative analysis of artificial intelligence-based contouring of cardiac substructures on computed tomography scans for radiation therapy

Background and purpose: Artificial intelligence-based contouring tools enable assessment of radiation doses to cardiac substructures beyond mean heart dose. This study examined inter-solution variations in raw contours and the impact of non-contrast enhancement on contours for each solution. Materials and methods: Contrast-enhanced (CE) and non-contrast-enhanced (NCE) breath-hold thoracic computed tomography (CT) scans, sequentially acquired during the same imaging session for twenty lung cancer patients, were used. Seven commercial, three open-source, and one in-house AI solutions were evaluated. On CE-CTs, solutions were compared using Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff distance (HD95) across each pair of solutions. Then, the effect of non-contrast enhancement on contours was assessed using volume ratios between NCE-CT and CE-CT for each solution. Results: Typically, ten cardiac substructures were contoured by most of the solutions. For the whole heart, cardiac chambers and great vessels, the average median DSC was above 0.8 for 55 of the 123 structure-solution pairs (45%), and the average median HD95 was below 10 mm for 47 of the 123 structure-solution pairs (38%). For the coronary arteries, the average median DSC ranged between 0.03 and 0.50 and the average median HD95 ranged between 19 mm and 70 mm. Non-contrast enhancement influenced results variably; volume differences were below 10% for 84 of the 123 structure-solution pairs (68%). Conclusions: Automatic contouring solutions exhibited inter-solution variability for cardiac substructures that may have clinical impact. Greater transparency and standardisation of models, ideally through international consensus and shared datasets, are essential.