Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy

: Pang, Eric Pei Ping; Tan, Hong Qi; Wang, Fuqiang; Niemelä, Jarkko; Bolard, Gregory; Ramadan, Susan; Kiljunen, Timo; Capala, Marta; Petit, Steven; Seppala, Jan; Vuolukka, Kristiina; Kiitam, Ingrid; Zolotuhhin, Danil; Gershkevitsh, Eduard; Lehtiö, Kaisa; Nikkinen, Juha; Keyriläinen, Jani; Mokka, Miia; Chua, Melvin Lee Kiang

Publisher: NATURE PORTFOLIO

: BERLIN

: 2025

: npj Digital Medicine

: NPJ DIGITAL MEDICINE

: NPJ DIGIT MED

: 312

: 8

: 1

: 11

: 2398-6352

DOI: https://doi.org/10.1038/s41746-025-01624-z

: https://www.nature.com/articles/s41746-025-01624-z

: https://research.utu.fi/converis/portal/detail/Publication/498728007

This is a multi-institutional study to evaluate a head-and-neck CT auto-segmentation software across seven institutions globally. 11 lymph node levels and 7 organs-at-risk contours were evaluated in a two-phase study design. Time savings were measured in both phases, and the inter-observer variability across the seven institutions was quantified in phase two. Overall time savings were found to be 42% in phase one and 49% in phase two. Lymph node levels IA, IB, III, IVA, and IVB showed no significant time savings, with some centers reporting longer editing times than manual delineation. All the edited ROIs showed reduced inter-observer variability compared to manual segmentation. Our study shows that auto-segmentation plays a crucial role in harmonizing contouring practices globally. However, the clinical benefits of auto-segmentation software vary significantly across ROIs and between clinics. To maximize its potential, institution-specific commissioning is required to optimize the clinical benefits.

s41746-025-01624-z.pdf