Comparable performance between automatic and manual laryngeal and hypopharyngeal GTV delineations validated with pathology.

Purpose: Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations.

Materials And Methods: A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference. For validation, a dataset comprising 18 patients who underwent imaging before total laryngectomy was used, with histopathology-based (n=16) tumor delineations as ground truth. The performance of the automatic segmentations was compared to that of clinicians' manual delineations, both quantitatively and qualitatively.

Results: Median sensitivity (0.90 and 0.91) and largest required CTV margin (6.4 and 6.6 mm) were comparable between automatic and manual GTV delineations. The positive predictive value yielded the only significant difference between automatic and manual GTV delineations, with medians of 0.52 and 0.61, respectively (P=.03). Clinical target volumes derived from automatic and manual GTVs exhibited similar sizes (median of 44.5 and 40.1 mL) and achieved a sensitivity of 1.00 in 13/16 and 14/16 tumors, respectively. Automatic segmentations were considered clinically acceptable in 67% of cases, compared to 63% of manual delineations.

Conclusion: The proposed deep learning model for laryngeal and hypopharyngeal GTV segmentation achieved comparable results to clinicians' manual delineations, showing the potential for more consistency and efficiency in the radiotherapy workflow.