ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2024
Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.Journal of medical Internet research · 2026Article
- Uncertainty estimation for reliable neural network-based radiotherapy dose modelling using Monte Carlo dropout, mean variance estimation and deep ensemble.Physics and imaging in radiation oncology · 2026Article
- Improving confidence in MRI-based auto-segmentation via uncertainty assessment.Acta oncologica (Stockholm, Sweden) · 2026Article
- An evaluation of uncertainty quantification methods and measures for deep learning outcome prediction models in head and neck cancer radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton radiotherapy plans with uncertainty-aware external validation.Physics and imaging in radiation oncology · 2026Article
- Deep learning-based independent lymph node segmentation in esophageal cancer: a precise and efficient approach for radiotherapy planning.Quantitative imaging in medicine and surgery · 2026Article
- Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.Physics and imaging in radiation oncology · 2026Article
- Large language models for scalable standardization in radiation oncology.Physics and imaging in radiation oncology · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Article
- A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.Physics and imaging in radiation oncology · 2025Article
- Harmonizing organ-at-risk structure names using open-source large language models.Physics and imaging in radiation oncology · 2025Article
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- LUND-PROBE - LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset.Scientific data · 2025Article
- Leveraging network uncertainty to identify regions in rectal cancer clinical target volume auto-segmentations likely requiring manual edits.Physics and imaging in radiation oncology · 2025Article
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Understanding and leveraging uncertainties in autosegmentation for radiotherapy.BJR artificial intelligence · 2025Review
- Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Enhancing online adaptive radiotherapy with uncertainty based segmentation error and out-of-distribution detection.Frontiers in oncology · 2025Article
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14 authors.
Funding
Abstract
background/purposeThe use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.
methodsWe followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics.
resultsWe identified 56 articles published from 2015 to 2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50 %), followed by image-synthesis (13 %), and multiple applications simultaneously (11 %). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32 %). Imaging data was used in 91 % of studies, while only 13 % incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60 %), with Monte Carlo dropout being the most commonly implemented UQ method (32 %) followed by ensembling (16 %). 55 % of studies did not share code or datasets.
conclusionOur review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, we identified a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.