Artificial intelligence in image-guided cervical interventional radiotherapy: a systematic review
8 affiliations
- 1Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Radioterapia Oncologica, Dipartimento di Diagno-stica per Immagini e Radioterapia Oncologica
- 2Radiation Oncology Department, Hospital Clínic-Universitat de Barcelona
- 3UOC Fisica per le Scienze della Vita, Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS
- 4Department of Theoretical and Applied Sciences, eCampus University
- 5Unità Operativa Complessa Ginecologia Oncologica, Dipartimento per la Salute Della Donna e del Bambino e Della Salute Pubblica, Policlinico Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico
- 6Department of Imaging and Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS
- 7Radiation Oncology Unit, Responsible Research Hospital
- 8Università Cattolica del Sacro Cuore Sede di Roma
Abstract
Background: Imaging-guided interventional radiotherapy (IG-IRT) is the standard of care for locally advanced cervical cancer but remains time-consuming and operator-dependent. Artificial intelligence (AI) has emerged as a promising tool to improve efficiency and reproducibility.
Methods: A systematic review was conducted according to PRISMA 2020 guidelines, searching PubMed, Scopus, and Web of Science for studies published between 2010 and 2026 investigating AI-based imaging applications in cervical IRT. Eligible studies included original research evaluating segmentation (targets and organ at risk), planning, reconstruction, image registration and applicator selection.
Results: Fifty-two studies were included. Deep learning–based segmentation showed variable performance across anatomical structures and studies, with Dice coefficients approaching or exceeding 0.90 for some organs at risk and target volumes in the best-performing models. Individual studies reported shorter processing times, reduced contouring variability, accurate applicator reconstruction, and potential support for applicator selection. However, the substantial methodological and clinical heterogeneity precluded direct comparisons among AI approaches.
Conclusions: The available studies suggest that AI may support the automation of selected workflow steps and assist treatment-related decision-making in image-guided cervical interventional radiotherapy. While several applications have achieved high technical performance, most remain at the proof-of-concept stage, and robust external validation and prospective clinical evaluation are needed before routine clinical adoption.
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BibTeX
@article{lancellotta2026artificial,
title = {{Artificial intelligence in image-guided cervical interventional radiotherapy: a systematic review}},
author = {Valentina Lancellotta and Angeles Rovirosa and Maria Concetta La Milia and Enrico Rosa and Bruno Fionda and Maria Vaccaro and Pierpaolo Dragonetti and Leonardo Bannoni and Rosa Autorino and Nicolò Bizzarri and Luca Russo and Benedetta Gui and Gabriella Macchia and Luca Tagliaferri and Maria Antonietta Gambacorta},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1910070},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1910070/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence in image-guided cervical interventional radiotherapy: a systematic review
AU - Valentina Lancellotta
AU - Angeles Rovirosa
AU - Maria Concetta La Milia
AU - Enrico Rosa
AU - Bruno Fionda
AU - Maria Vaccaro
AU - Pierpaolo Dragonetti
AU - Leonardo Bannoni
AU - Rosa Autorino
AU - Nicolò Bizzarri
AU - Luca Russo
AU - Benedetta Gui
AU - Gabriella Macchia
AU - Luca Tagliaferri
AU - Maria Antonietta Gambacorta
PY - 2026
DA - 2026/10/15
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1910070
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1910070/full
AB - Background: Imaging-guided interventional radiotherapy (IG-IRT) is the standard of care for locally advanced cervical cancer but remains time-consuming and operator-dependent. Artificial intelligence (AI) has emerged as a promising tool to improve efficiency and reproducibility. Methods: A systematic review was conducted according to PRISMA 2020 guidelines, searching PubMed, Scopus, and Web of Science for studies published between 2010 and 2026 investigating AI-based imaging applications in cervical IRT. Eligible studies included original research evaluating segmentation (targets and organ at risk), planning, reconstruction, image registration and applicator selection. Results: Fifty-two studies were included. Deep learning–based segmentation showed variable performance across anatomical structures and studies, with Dice coefficients approaching or exceeding 0.90 for some organs at risk and target volumes in the best-performing models. Individual studies reported shorter processing times, reduced contouring variability, accurate applicator reconstruction, and potential support for applicator selection. However, the substantial methodological and clinical heterogeneity precluded direct comparisons among AI approaches. Conclusions: The available studies suggest that AI may support the automation of selected workflow steps and assist treatment-related decision-making in image-guided cervical interventional radiotherapy. While several applications have achieved high technical performance, most remain at the proof-of-concept stage, and robust external validation and prospective clinical evaluation are needed before routine clinical adoption.
SN - 2234-943X
ER -