Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning.
J Appl Clin Med Phys
; 24(7): e13949, 2023 Jul.
Article
en En
| MEDLINE
| ID: mdl-36871161
ABSTRACT
Contouring has become an increasingly important aspect of radiotherapy due to inverse planning. Several studies have suggested that the clinical implementation of automated contouring tools can reduce inter-observer variation while increasing contouring efficiency, thereby improving the quality of radiotherapy treatment and reducing the time between simulation and treatment. In this study, a novel, commercial automated contouring tool based on machine learning, the AI-Rad Companion Organs RT™ (AI-Rad) software (Version VA31) (Siemens Healthineers, Munich, Germany), was assessed against both manually delineated contours and another commercially available automated contouring software, Varian Smart Segmentation™ (SS) (Version 16.0) (Varian, Palo Alto, CA, United States). The quality of contours generated by AI-Rad in Head and Neck (H&N), Thorax, Breast, Male Pelvis (Pelvis_M), and Female Pelvis (Pevis_F) anatomical areas was evaluated both quantitatively and qualitatively using several metrics. A timing analysis was subsequently performed to explore potential time savings achieved by AI-Rad. Results showed that most automated contours generated by AI-Rad were not only clinically acceptable and required minimal editing, but also superior in quality to contours generated by SS in multiple structures. In addition, timing analysis favored AI-Rad over manual contouring, indicating the largest time saving (753s per patient) in the Thorax area. AI-Rad was concluded to be a promising automated contouring solution that generated clinically acceptable contours and achieved time savings, thereby greatly benefiting the radiotherapy process.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Planificación de la Radioterapia Asistida por Computador
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Neoplasias de Cabeza y Cuello
Tipo de estudio:
Etiology_studies
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Guideline
Límite:
Female
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Humans
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Male
Idioma:
En
Año:
2023
Tipo del documento:
Article