Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025
Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.
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Sharma D, Singh G, Burela N, Gayen S, Aishwarya G, Nangia S
Аннотация
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).
MATERIALS AND METHODS: A 3D U-Net-based DLS model was applied to CT datasets of 124 HNC patients treated with IMPT at 50.4-70.0 GyRBE. Thirty organs-at-risk (OARs), delineated manually (GT-OARs) were analysed for similarity metrics with auto-segmented OARs, without (DLS-nonedited) and with (DLS-edited) manual correction, using volume, Dice similarity coefficient (DSC), and Hausdorff distance (HD). Dosimetric impact of auto-segmentation error was assessed as absolute dose difference of mean (ΔDmean) and maximum (ΔDmax).
RESULTS: The cohort includes patients with postoperative (47.6%), flap reconstruction (12.1%), mouth bites (79.8%), dental implants (54.8%), and surgical implants (3.2%). DLS failed in 11 patients with significant anatomical challenges and artifact. Compared with GT-OARs, DLS-nonedited under-segmented 11/12 Gr-A (central nervous system, arteries, bone) (p < 0.05) and over-segmented 13/18 Gr-B (glandular, digestive, airways) OARs. DSC scores were good (>0.8), intermediate (0.6-0.8), intermediate-poor (0.5-0.6), and poor (<0.5) in 12, 6, 4, and 8 OARs. HD were good (<4mm), intermediate (4-6mm), poor (6-8mm), and very poor (>8mm) in 5, 7, 4, and 14 OARs. Compared with manually corrected, DLS-edited OARs, all DLS-nonedited OARs demonstrated excellent similarity with DSC>0.8 and HD<4mm. On average, auto-segmentation took 2.51 minutes, while correction took 6.24 minutes. The mean values of ΔDmean and ΔDmax were within ±300 and ±3 cGyRBE, except for oesophagus and larynx, where the mean ΔDmean increases up to 837.14 cGyRBE.
CONCLUSION: Patient posture, nonbiological materials, and anatomical deformities influence DLS accuracy. The model's overall performance is adequate and efficient with skilled manual editing needed for few OARs.
MATERIALS AND METHODS: A 3D U-Net-based DLS model was applied to CT datasets of 124 HNC patients treated with IMPT at 50.4-70.0 GyRBE. Thirty organs-at-risk (OARs), delineated manually (GT-OARs) were analysed for similarity metrics with auto-segmented OARs, without (DLS-nonedited) and with (DLS-edited) manual correction, using volume, Dice similarity coefficient (DSC), and Hausdorff distance (HD). Dosimetric impact of auto-segmentation error was assessed as absolute dose difference of mean (ΔDmean) and maximum (ΔDmax).
RESULTS: The cohort includes patients with postoperative (47.6%), flap reconstruction (12.1%), mouth bites (79.8%), dental implants (54.8%), and surgical implants (3.2%). DLS failed in 11 patients with significant anatomical challenges and artifact. Compared with GT-OARs, DLS-nonedited under-segmented 11/12 Gr-A (central nervous system, arteries, bone) (p < 0.05) and over-segmented 13/18 Gr-B (glandular, digestive, airways) OARs. DSC scores were good (>0.8), intermediate (0.6-0.8), intermediate-poor (0.5-0.6), and poor (<0.5) in 12, 6, 4, and 8 OARs. HD were good (<4mm), intermediate (4-6mm), poor (6-8mm), and very poor (>8mm) in 5, 7, 4, and 14 OARs. Compared with manually corrected, DLS-edited OARs, all DLS-nonedited OARs demonstrated excellent similarity with DSC>0.8 and HD<4mm. On average, auto-segmentation took 2.51 minutes, while correction took 6.24 minutes. The mean values of ΔDmean and ΔDmax were within ±300 and ±3 cGyRBE, except for oesophagus and larynx, where the mean ΔDmean increases up to 837.14 cGyRBE.
CONCLUSION: Patient posture, nonbiological materials, and anatomical deformities influence DLS accuracy. The model's overall performance is adequate and efficient with skilled manual editing needed for few OARs.