Artificial intelligence in image-guided cervical interventional radiotherapy: a systematic review
Valentina Lancellotta1, Angeles Rovirosa2, Maria Concetta La Milia1
1Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Radioterapia Oncologica, Dipartimento di Diagno-stica per Immagini e Radioterapia Oncologica, Rome, Italy.
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.
