Applicator reconstruction in cervical cancer brachytherapy: A systematic review of current methods, challenges, and AI-driven future directions
Abstract
Purpose: Accurate applicator reconstruction is a critical step in 3D image-guided brachytherapy (3D-IGBT) for cervical cancer, directly influencing tumor control and organ-at-risk sparing. This systematic review evaluates the accuracy, efficiency, and clinical impact of applicator reconstruction methods, focusing on AI's potential to overcome existing limitations.
Methods and materials: Following PRISMA guidelines, 23 studies from MEDLINE, PubMed, Scopus, Embase, Lilacs and Web of Science (up to May 2025) were analyzed. Evaluation metrics included geometric accuracy (tip error, Hausdorff distance), reconstruction time, and dosimetric parameters (D90 HR-CTV, D2cc OARs). Methods assessed spanned manual (e.g., MPR, scout-based), semi-automatic (library method, clustering algorithms), and AI-driven approaches (e.g., U-Net, Dilated-Supervised Deep U-Net, Attention-Gated networks). Special focus was placed on deep learning (DL) architectures and their ability to overcome metallic artifacts, partial-volume effects, and inter-operator variability.
Results: Manual methods exhibited significant limitations, with tip errors reaching 4.1 mm. Semi-automated approaches reduced variability (library-based methods: 0.89) and dosimetric consistency (D2cc deviations <3%). Notably, DL models like DSD-UNet and Attention-Gated U-Net reduced reconstruction time to <30 s per case while maintaining robustness against CT artifacts. However, challenges persist, including limited clinical validation…
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