Objective: While artificial intelligence (AI) is known to benefit radiotherapy (RT), its applications in carbon ion RT (CIRT) is yet to be surveyed. This scoping review aims to examine the current landscape of AI in CIRT and highlight future directions. Materials and methods: Following PRISMA guidelines, three databases (PubMed, Scopus, and Web of Science) were searched to find eligible English articles investigating AI in CIRT between January 2014 and June 2025. Two human reviewers and an AI tool independently screened articles. Agreement was quantified using Cohen’s kappa and McNemar’s test (α = 0.05). Results: Of the 413 unique articles screened, 16 were selected, and an additional three were included through citation search, totaling 19 articles. The screening agreement between human and AI reviewers was substantial (Cohen’s kappa = 0.75) with no statistical difference (p = 0.18). The AI contributions in CIRT were categorized into treatment planning, optimization, and verification; synthetic imaging; tumor control; and normal tissue complication prediction. All selected studies were limited by small datasets and lacked external validation. Conclusions: AI development in the CIRT domain is in early stages but holds promise for reducing costs and improving treatment, especially in adaptive settings. Future efforts should foster collaboration across CIRT facilities to support robust AI applications.
The landscape of artificial intelligence in carbon ion radiotherapy: A scoping review
Thulasi Seetha, Sithin;Lillo, Sara;Molinelli, Silvia;Preda, Lorenzo;Barcellini, Amelia;Orlandi, Ester
2026-01-01
Abstract
Objective: While artificial intelligence (AI) is known to benefit radiotherapy (RT), its applications in carbon ion RT (CIRT) is yet to be surveyed. This scoping review aims to examine the current landscape of AI in CIRT and highlight future directions. Materials and methods: Following PRISMA guidelines, three databases (PubMed, Scopus, and Web of Science) were searched to find eligible English articles investigating AI in CIRT between January 2014 and June 2025. Two human reviewers and an AI tool independently screened articles. Agreement was quantified using Cohen’s kappa and McNemar’s test (α = 0.05). Results: Of the 413 unique articles screened, 16 were selected, and an additional three were included through citation search, totaling 19 articles. The screening agreement between human and AI reviewers was substantial (Cohen’s kappa = 0.75) with no statistical difference (p = 0.18). The AI contributions in CIRT were categorized into treatment planning, optimization, and verification; synthetic imaging; tumor control; and normal tissue complication prediction. All selected studies were limited by small datasets and lacked external validation. Conclusions: AI development in the CIRT domain is in early stages but holds promise for reducing costs and improving treatment, especially in adaptive settings. Future efforts should foster collaboration across CIRT facilities to support robust AI applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
