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

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.

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2025-05-27
DOI
https://doi.org/10.1038/s41746-025-01624-z
Citations
7
Primary Topic
Advanced Radiotherapy Techniques
Type
article
Field-Weighted Citation Impact
13.18
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Eric Pei Ping Pang, Kaisa Lehtiö, Timo Kiljunen, Marta Capala et al.
7 citations
npj Digital Medicine
Advanced Radiotherapy Techniques
13.18
article

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

Eric Pei Ping Pang, Kaisa Lehtiö, Timo Kiljunen, Marta Capala, Kristiina Vuolukka, Steven Petit, Jani Keyriläinen, Hong Qi Tan, D. Zolotuhhin, G. Bolard, Jarkko Niemelä, Juha Nikkinen, Melvin L.K. Chua, Jan Seppälä, E. Gershkevitsh, Miia Mokka, Susan Ramadan, Fuqiang Wang, Ingrid Kiitam
article en
7 citations

Abstract

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.

npj Digital MedicineVol. 8(1)
University of Helsinki (FI), Oulu University Hospital (FI), Helsinki Institute of Physics (FI), Finnish Institute of Occupational Health (FI), Turku University Hospital (FI), Kuopio University Hospital (FI), North Estonia Medical Centre (EE), Duke-NUS Medical School (SG), National Cancer Centre Singapore (SG), Erasmus MC Cancer Institute (NL), Docrates Cancer Center (FI), University of Oulu (FI)
Quality Education
Openalex Percentile: Top 1%
Advanced Radiotherapy Techniques
13.18
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.