Applicator reconstruction in cervical cancer brachytherapy: A systematic review of current methods, challenges, and AI-driven future directions.
Authors
Affiliations (3)
Affiliations (3)
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Radiology, Faculty of Paramedicine, Zahedan University of Medical Sciences, Zahedan, Iran.
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Physics Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Nuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: [email protected].
Abstract
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. 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. Manual methods exhibited significant limitations, with tip errors reaching 4.1 mm. Semi-automated approaches reduced variability (library-based methods: <0.5 mm mean deviation) but remained constrained by predefined applicator models. AI-driven workflows demonstrated superior precision, achieving submillimeter accuracy (median tip error: 0.64 mm; Dice Similarity Coefficient (DSC) > 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 (60% of studies used phantoms), data heterogeneity (slice thickness: 0.6-5 mm), and generalizability to novel applicator designs. AI-driven reconstruction reduces human-dependent errors and enhances efficiency, but clinical validation remains a priority. Reducing CT slice thickness (≤1.5 mm) and combining scout images to mitigate metal artifacts are recommended. Future research should focus on generalizable AI models for nonlibrary applicators and large-scale clinical validation.