1
|
Wahid KA, Kaffey ZY, Farris DP, Humbert-Vidan L, Moreno AC, Rasmussen M, Ren J, Naser MA, Netherton TJ, Korreman S, Balakrishnan G, Fuller CD, Fuentes D, Dohopolski MJ. Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.13.24307226. [PMID: 38798581 PMCID: PMC11118597 DOI: 10.1101/2024.05.13.24307226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background/purpose The 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. Methods We 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. Results We identified 56 articles published from 2015-2024. 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. Conclusion Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was 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.
Collapse
Affiliation(s)
- Kareem A. Wahid
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zaphanlene Y. Kaffey
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David P. Farris
- Research Medical Library, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laia Humbert-Vidan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Amy C. Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Jintao Ren
- Department of Oncology, Aarhus University Hospital, Denmark
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stine Korreman
- Department of Oncology, Aarhus University Hospital, Denmark
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael J. Dohopolski
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| |
Collapse
|
2
|
Knäusl B, Belotti G, Bertholet J, Daartz J, Flampouri S, Hoogeman M, Knopf AC, Lin H, Moerman A, Paganelli C, Rucinski A, Schulte R, Shimizu S, Stützer K, Zhang X, Zhang Y, Czerska K. A review of the clinical introduction of 4D particle therapy research concepts. Phys Imaging Radiat Oncol 2024; 29:100535. [PMID: 38298885 PMCID: PMC10828898 DOI: 10.1016/j.phro.2024.100535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Background and purpose Many 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments. Material and methods This review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic. Results Available technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments. Conclusion This review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.
Collapse
Affiliation(s)
- Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gabriele Belotti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Juliane Daartz
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Mischa Hoogeman
- Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Antje C Knopf
- Institut für Medizintechnik und Medizininformatik Hochschule für Life Sciences FHNW, Muttenz, Switzerland
| | - Haibo Lin
- New York Proton Center, New York, NY, USA
| | - Astrid Moerman
- Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
| | - Reinhard Schulte
- Division of Biomedical Engineering Sciences, School of Medicine, Loma Linda University
| | - Shing Shimizu
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kristin Stützer
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden – Rossendorf, Institute of Radiooncology – OncoRay, Dresden, Germany
| | - Xiaodong Zhang
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Katarzyna Czerska
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| |
Collapse
|