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Ersted Rasmussen M, Dueholm Vestergaard C, Folsted Kallehauge J, Ren J, Haislund Guldberg M, Nørrevang O, Vindelev Elstrøm U, Sofia Korreman S. RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows. Phys Imaging Radiat Oncol 2024; 31:100607. [PMID: 39071159 PMCID: PMC11283118 DOI: 10.1016/j.phro.2024.100607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
The use of and research in automation and artificial intelligence (AI) in radiotherapy is moving with incredible pace. Many innovations do, however, not make it into the clinic. One technical reason for this may be the lack of a platform to deploy such software into clinical practice. We suggest RadDeploy as a framework for integrating containerized software in clinical workflows outside of treatment planning systems. RadDeploy supports multiple DICOM as input for model containers and can run model containers asynchronously across GPUs and computers. This technical note summarizes the inner workings of RadDeploy and demonstrates three use-cases with varying complexity.
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Affiliation(s)
- Mathis Ersted Rasmussen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Casper Dueholm Vestergaard
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Jesper Folsted Kallehauge
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Jintao Ren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Maiken Haislund Guldberg
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Ole Nørrevang
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Ulrik Vindelev Elstrøm
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Stine Sofia Korreman
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
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van Timmeren JE, Bussink J, Koopmans P, Smeenk RJ, Monshouwer R. Longitudinal Image Data for Outcome Modeling. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00277-2. [PMID: 39003124 DOI: 10.1016/j.clon.2024.06.053] [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: 10/23/2023] [Revised: 04/15/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
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Affiliation(s)
- J E van Timmeren
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - P Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R J Smeenk
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
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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.
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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
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Ren W, Liang B, Sun C, Wu R, Men K, Chen H, Feng X, Hou L, Han F, Yi J, Dai J. A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma. Phys Med 2024; 121:103362. [PMID: 38653120 DOI: 10.1016/j.ejmp.2024.103362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/27/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI). MATERIALS AND METHODS Spatial features of dose distribution within the temporal lobe were extracted using both the three-dimensional convolution (C3D) network and the dosiomics method. The Minimal Redundancy-Maximal-Relevance (mRMR) method was employed to rank the extracted features and select the most relevant ones. Four machine learning (ML) classifiers, including logistic regression (LR), k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF), were used to establish prediction models. Nested sampling and hyperparameter tuning methods were applied to train and validate the prediction models. For comparison, a prediction model base on the conventional D0.5cc of the temporal lobe obtained from dose volume (DV) histogram was established. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the predictive performance of the different models. RESULTS A total of 127 nasopharyngeal carcinoma (NPC) patients were included in the study. In the model based on C3D deep learning features, the highest AUC value of 0.843 was achieved with 5 features. For the dosiomics features model, the highest AUC value of 0.715 was attained with 1 feature. Both of these models demonstrated superior performance compared to the prediction model based on DV parameters, which yielded an AUC of 0.695. CONCLUSION The prediction model utilizing C3D deep learning features outperformed models based on dosiomics features or traditional parameters in predicting the onset of TLI. This approach holds promise for predicting radiation-induced toxicities and guide individualized radiotherapy.
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Affiliation(s)
- Wenting Ren
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bin Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Runye Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Huan Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xin Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Fei Han
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Zhao J, Vaios E, Wang Y, Yang Z, Cui Y, Reitman ZJ, Lafata KJ, Fecci P, Kirkpatrick J, Fang Yin F, Floyd S, Wang C. Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00505-4. [PMID: 38615888 DOI: 10.1016/j.ijrobp.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024]
Abstract
PURPOSE To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.
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Affiliation(s)
- Jingtong Zhao
- Duke University Medical Center, Durham, North Carolina
| | - Eugene Vaios
- Duke University Medical Center, Durham, North Carolina
| | - Yuqi Wang
- Duke University Medical Center, Durham, North Carolina
| | - Zhenyu Yang
- Duke University Medical Center, Durham, North Carolina
| | - Yunfeng Cui
- Duke University Medical Center, Durham, North Carolina
| | | | - Kyle J Lafata
- Duke University Medical Center, Durham, North Carolina
| | - Peter Fecci
- Duke University Medical Center, Durham, North Carolina
| | | | | | - Scott Floyd
- Duke University Medical Center, Durham, North Carolina
| | - Chunhao Wang
- Duke University Medical Center, Durham, North Carolina.
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Shan G, Yu S, Lai Z, Xuan Z, Zhang J, Wang B, Ge Y. A Review of Artificial Intelligence Application for Radiotherapy. Dose Response 2024; 22:15593258241263687. [PMID: 38912333 PMCID: PMC11193352 DOI: 10.1177/15593258241263687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/03/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Purpose Artificial intelligence (AI) is a technique which tries to think like humans and mimic human behaviors. It has been considered as an alternative in a lot of human-dependent steps in radiotherapy (RT), since the human participation is a principal uncertainty source in RT. The aim of this work is to provide a systematic summary of the current literature on AI application for RT, and to clarify its role for RT practice in terms of clinical views. Materials and Methods A systematic literature search of PubMed and Google Scholar was performed to identify original articles involving the AI applications in RT from the inception to 2022. Studies were included if they reported original data and explored the clinical applications of AI in RT. Results The selected studies were categorized into three aspects of RT: organ and lesion segmentation, treatment planning and quality assurance. For each aspect, this review discussed how these AI tools could be involved in the RT protocol. Conclusions Our study revealed that AI was a potential alternative for the human-dependent steps in the complex process of RT.
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Affiliation(s)
- Guoping Shan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
- Zhejiang Cancer Hospital, Hangzhou, China
| | - Shunfei Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhongjun Lai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhiqiang Xuan
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jie Zhang
- Zhejiang Cancer Hospital, Hangzhou, China
| | | | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
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Khaledi N, Khan R, Gräfe JL. Historical Progress of Stereotactic Radiation Surgery. J Med Phys 2023; 48:312-327. [PMID: 38223793 PMCID: PMC10783188 DOI: 10.4103/jmp.jmp_62_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 01/16/2024] Open
Abstract
Radiosurgery and stereotactic radiotherapy have established themselves as precise and accurate areas of radiation oncology for the treatment of brain and extracranial lesions. Along with the evolution of other methods of radiotherapy, this type of treatment has been associated with significant advances in terms of a variety of modalities and techniques to improve the accuracy and efficacy of treatment. This paper provides a comprehensive overview of the progress in stereotactic radiosurgery (SRS) over several decades, and includes a review of various articles and research papers, commencing with the emergence of stereotactic techniques in radiotherapy. Key clinical aspects of SRS, such as fixation methods, radiobiology considerations, quality assurance practices, and treatment planning strategies, are presented. In addition, the review highlights the technological advancements in treatment modalities, encompassing the transition from cobalt-based systems to linear accelerator-based modalities. By addressing these topics, this study aims to offer insights into the advancements that have shaped the field of SRS, that have ultimately enhanced the accuracy and effectiveness of treatment.
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Affiliation(s)
- Navid Khaledi
- Department of Medical Physics, Cancer Care Manitoba, Winnipeg, MB, Canada
| | - Rao Khan
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Department of Physics and Astronomy and Department of Radiation Oncology, Howard University, Washington, District of Columbia, USA
| | - James L. Gräfe
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Cancer Care Program, Dr. H. Bliss Murphy Cancer Center. 300 Prince Philip Drive St. John’s, NL, Canada
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8
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Ecker S, Kirisits C, Schmid M, Knoth J, Heilemann G, De Leeuw A, Sturdza A, Kirchheiner K, Jensen N, Nout R, Jürgenliemk-Schulz I, Pötter R, Spampinato S, Tanderup K, Eder-Nesvacil N. EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer. Radiother Oncol 2023; 186:109748. [PMID: 37330055 DOI: 10.1016/j.radonc.2023.109748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). METHODS A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. RESULTS EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. CONCLUSION A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.
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Affiliation(s)
- Stefan Ecker
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.
| | - Christian Kirisits
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Maximilian Schmid
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Johannes Knoth
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Gerd Heilemann
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Astrid De Leeuw
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Alina Sturdza
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Kathrin Kirchheiner
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Nina Jensen
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Remi Nout
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Ina Jürgenliemk-Schulz
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Richard Pötter
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Sofia Spampinato
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Kari Tanderup
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
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Tan D, Mohd Nasir NF, Abdul Manan H, Yahya N. Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review. Cancer Radiother 2023; 27:398-406. [PMID: 37482464 DOI: 10.1016/j.canrad.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity. MATERIALS AND METHODS A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria. RESULTS Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed. CONCLUSION Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.
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Affiliation(s)
- D Tan
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - N F Mohd Nasir
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - H Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia
| | - N Yahya
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia.
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10
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Liu J, Xiao H, Fan J, Hu W, Yang Y, Dong P, Xing L, Cai J. An overview of artificial intelligence in medical physics and radiation oncology. JOURNAL OF THE NATIONAL CANCER CENTER 2023; 3:211-221. [PMID: 39035195 PMCID: PMC11256546 DOI: 10.1016/j.jncc.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 05/03/2023] [Accepted: 08/08/2023] [Indexed: 07/23/2024] Open
Abstract
Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.
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Affiliation(s)
- Jiali Liu
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Clinical Oncology, Hong Kong University Li Ka Shing Medical School, Hong Kong, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Peng Dong
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Cheon W, Han M, Jeong S, Oh ES, Lee SU, Lee SB, Shin D, Lim YK, Jeong JH, Kim H, Kim JY. Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients. Cancers (Basel) 2023; 15:3463. [PMID: 37444573 DOI: 10.3390/cancers15133463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
(1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD2cc) as the most important feature, followed by BD5cc and the ICRU bladder point. In the case of the lightweight DL model, the F-score and AUROC were 0.90 and 0.91, respectively. (4) The DL models exhibited superior performance in predicting late bladder toxicity compared with the statistical method. Through the interpretation of the model, it further emphasized its potential for improving patient outcomes and minimizing treatment-related complications with a high level of reliability.
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Affiliation(s)
- Wonjoong Cheon
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Mira Han
- Biostatistics Collaboration Team, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Seonghoon Jeong
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Eun Sang Oh
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Sung Uk Lee
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Se Byeong Lee
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Dongho Shin
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Young Kyung Lim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Jong Hwi Jeong
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Haksoo Kim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Joo Young Kim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
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12
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Reber B, Van Dijk L, Anderson B, Mohamed ASR, Fuller C, Lai S, Brock K. Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy. Adv Radiat Oncol 2023; 8:101163. [PMID: 36798732 PMCID: PMC9926206 DOI: 10.1016/j.adro.2022.101163] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Purpose Deep-learning (DL) techniques have been successful in disease-prediction tasks and could improve the prediction of mandible osteoradionecrosis (ORN) resulting from head and neck cancer (HNC) radiation therapy. In this study, we retrospectively compared the performance of DL algorithms and traditional machine-learning (ML) techniques to predict mandible ORN binary outcome in an extensive cohort of patients with HNC. Methods and Materials Patients who received HNC radiation therapy at the University of Texas MD Anderson Cancer Center from 2005 to 2015 were identified for the ML (n = 1259) and DL (n = 1236) studies. The subjects were followed for ORN development for at least 12 months, with 173 developing ORN and 1086 having no evidence of ORN. The ML models used dose-volume histogram parameters to predict ORN development. These models included logistic regression, random forest, support vector machine, and a random classifier reference. The DL models were based on ResNet, DenseNet, and autoencoder-based architectures. The DL models used each participant's dose cropped to the mandible. The effect of increasing the amount of available training data on the DL models' prediction performance was evaluated by training the DL models using increasing ratios of the original training data. Results The F1 score for the logistic regression model, the best-performing ML model, was 0.3. The best-performing ResNet, DenseNet, and autoencoder-based models had F1 scores of 0.07, 0.14, and 0.23, respectively, whereas the random classifier's F1 score was 0.17. No performance increase was apparent when we increased the amount of training data available for DL model training. Conclusions The ML models had superior performance to their DL counterparts. The lack of improvement in DL performance with increased training data suggests that either more data are needed for appropriate DL model construction or that the image features used in DL models are not suitable for this task.
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Affiliation(s)
- Brandon Reber
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lisanne Van Dijk
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- University of Groningen, Groningen, Netherlands
| | - Brian Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- University of California, San Diego, San Diego, California
| | | | - Clifton Fuller
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stephen Lai
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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13
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Chounta S, Allodji R, Vakalopoulou M, Bentriou M, Do DT, De Vathaire F, Diallo I, Fresneau B, Charrier T, Zossou V, Christodoulidis S, Lemler S, Letort Le Chevalier V. Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer. Cancers (Basel) 2023; 15:3107. [PMID: 37370717 DOI: 10.3390/cancers15123107] [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: 03/01/2023] [Revised: 05/16/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors' Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart-dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines.
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Affiliation(s)
- Stefania Chounta
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Rodrigue Allodji
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Polytechnic School of Abomey-Calavi (EPAC), University of Abomey-Calavi, 01, Cotonou P.O. Box 2009, Benin
| | - Maria Vakalopoulou
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Mahmoud Bentriou
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Duyen Thi Do
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
| | - Florent De Vathaire
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
| | - Ibrahima Diallo
- Department of Radiation Oncology, Gustave Roussy, F-94800 Villejuif, France
- Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, Paris-Saclay University, F-94800 Villejuif, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Pediatric Oncology, F-94805 Villejuif, France
| | - Thibaud Charrier
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Institut Curie, PSL Research University, INSERM, U900, F-92210 Saint Cloud, France
| | - Vincent Zossou
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Polytechnic School of Abomey-Calavi (EPAC), University of Abomey-Calavi, 01, Cotonou P.O. Box 2009, Benin
- Institut de Formation et de Recherche en Informatique, (IFRI-UAC), Cotonou P.O. Box 2009, Benin
| | - Stergios Christodoulidis
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Sarah Lemler
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Veronique Letort Le Chevalier
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
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14
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Franzese C, Dei D, Lambri N, Teriaca MA, Badalamenti M, Crespi L, Tomatis S, Loiacono D, Mancosu P, Scorsetti M. Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review. J Pers Med 2023; 13:946. [PMID: 37373935 DOI: 10.3390/jpm13060946] [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: 05/05/2023] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. METHODS The PubMed database was queried, and a total of 168 articles (2016-2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. RESULTS The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. CONCLUSIONS AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.
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Affiliation(s)
- Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Damiano Dei
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Nicola Lambri
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Maria Ausilia Teriaca
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Marco Badalamenti
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Leonardo Crespi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
- Centre for Health Data Science, Human Technopole, 20157 Milan, Italy
| | - Stefano Tomatis
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Daniele Loiacono
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Pietro Mancosu
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
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15
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Verhaegen F, Butterworth KT, Chalmers AJ, Coppes RP, de Ruysscher D, Dobiasch S, Fenwick JD, Granton PV, Heijmans SHJ, Hill MA, Koumenis C, Lauber K, Marples B, Parodi K, Persoon LCGG, Staut N, Subiel A, Vaes RDW, van Hoof S, Verginadis IL, Wilkens JJ, Williams KJ, Wilson GD, Dubois LJ. Roadmap for precision preclinical x-ray radiation studies. Phys Med Biol 2023; 68:06RM01. [PMID: 36584393 DOI: 10.1088/1361-6560/acaf45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.
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Affiliation(s)
- Frank Verhaegen
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Karl T Butterworth
- Patrick G. Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Anthony J Chalmers
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, United Kingdom
| | - Rob P Coppes
- Departments of Biomedical Sciences of Cells & Systems, Section Molecular Cell Biology and Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 AD Groningen, The Netherlands
| | - Dirk de Ruysscher
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sophie Dobiasch
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Department of Medical Physics, Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Germany
| | - John D Fenwick
- Department of Medical Physics & Biomedical Engineering University College LondonMalet Place Engineering Building, London WC1E 6BT, United Kingdom
| | | | | | - Mark A Hill
- MRC Oxford Institute for Radiation Oncology, University of Oxford, ORCRB Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kirsten Lauber
- Department of Radiation Oncology, University Hospital, LMU München, Munich, Germany
- German Cancer Consortium (DKTK), Partner site Munich, Germany
| | - Brian Marples
- Department of Radiation Oncology, University of Rochester, NY, United States of America
| | - Katia Parodi
- German Cancer Consortium (DKTK), Partner site Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching b. Munich, Germany
| | | | - Nick Staut
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Anna Subiel
- National Physical Laboratory, Medical Radiation Science Hampton Road, Teddington, Middlesex, TW11 0LW, United Kingdom
| | - Rianne D W Vaes
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Ioannis L Verginadis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jan J Wilkens
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Physics Department, Technical University of Munich (TUM), Germany
| | - Kaye J Williams
- Division of Pharmacy and Optometry, University of Manchester, Manchester, United Kingdom
| | - George D Wilson
- Department of Radiation Oncology, Beaumont Health, MI, United States of America
- Henry Ford Health, Detroit, MI, United States of America
| | - Ludwig J Dubois
- The M-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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16
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Elhaminia B, Gilbert A, Lilley J, Abdar M, Frangi AF, Scarsbrook A, Appelt A, Gooya A. Toxicity Prediction in Pelvic Radiotherapy Using Multiple Instance Learning and Cascaded Attention Layers. IEEE J Biomed Health Inform 2023; PP:1958-1966. [PMID: 37022057 DOI: 10.1109/jbhi.2023.3238825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Modern radiotherapy delivers treatment plans optimised on an individual patient level, using CT-based 3D models of patient anatomy. This optimisation is fundamentally based on simple assumptions about the relationship between radiation dose delivered to the cancer (increased dose will increase cancer control) and normal tissue (increased dose will increase rate of side effects). The details of these relationships are still not well understood, especially for radiation-induced toxicity. We propose a convolutional neural network based on multiple instance learning to analyse toxicity relationships for patients receiving pelvic radiotherapy. A dataset comprising of 315 patients were included in this study; with 3D dose distributions, pre-treatment CT scans with annotated abdominal structures, and patient-reported toxicity scores provided for each participant. In addition, we propose a novel mechanism for segregating the attentions over space and dose/imaging features independently for a better understanding of the anatomical distribution of toxicity. Quantitative and qualitative experiments were performed to evaluate the network performance. The proposed network could predict toxicity with 80% accuracy. Attention analysis over space demonstrated that there was a significant association between radiation dose to the anterior and right iliac of the abdomen and patient-reported toxicity. Experimental results showed that the proposed network had outstanding performance for toxicity prediction, localisation and explanation with the ability of generalisation for an unseen dataset.
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17
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Zhang Y, Qiu L, Ren Y, Cheng Z, Li L, Yao S, Zhang C, Luo Z, Lu H. A meta-learning approach to improving radiation response prediction in cancers. Comput Biol Med 2022; 150:106163. [PMID: 37070625 DOI: 10.1016/j.compbiomed.2022.106163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/18/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Predicting the efficacy of radiotherapy in individual patients has drawn widespread attention, but the limited sample size remains a bottleneck for utilizing high-dimensional multi-omics data to guide personalized radiotherapy. We hypothesize the recently developed meta-learning framework could address this limitation. METHODS AND MATERIALS By combining gene expression, DNA methylation, and clinical data of 806 patients who had received radiotherapy from The Cancer Genome Atlas (TCGA), we applied the Model-Agnostic Meta-Learning (MAML) framework to tasks consisting of pan-cancer data, to obtain the best initial parameters of a neural network for a specific cancer with smaller number of samples. The performance of meta-learning framework was compared with four traditional machine learning methods based on two training schemes, and tested on Cancer Cell Line Encyclopedia (CCLE) and Chinese Glioma Genome Atlas (CGGA) datasets. Moreover, biological significance of the models was investigated by survival analysis and feature interpretation. RESULTS The mean AUC (Area under the ROC Curve) [95% confidence interval] of our models across nine cancer types was 0.702 [0.691-0.713], which improved by 0.166 on average over other the four machine learning methods on two training schemes. Our models performed significantly better (p < 0.05) in seven cancer types and performed comparable to the other predictors in the rest of two cancer types. The more pan-cancer samples were used to transfer meta-knowledge, the greater the performance improved (p < 0.05). The predicted response scores that our models generated were negatively correlated with cell radiosensitivity index in four cancer types (p < 0.05), while not statistically significant in the other three cancer types. Moreover, the predicted response scores were shown to be prognostic factors in seven cancer types and eight potential radiosensitivity-related genes were identified. CONCLUSIONS For the first time, we established the meta-learning approach to improving individual radiation response prediction by transferring common knowledge from pan-cancer data with MAML framework. The results demonstrated the superiority, generalizability, and biological significance of our approach.
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18
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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19
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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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Deantonio L, Garo ML, Paone G, Valli MC, Cappio S, La Regina D, Cefali M, Palmarocchi MC, Vannelli A, De Dosso S. 18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:861638. [PMID: 35371989 PMCID: PMC8965232 DOI: 10.3389/fonc.2022.861638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/16/2022] [Indexed: 12/22/2022] Open
Abstract
The best treatment strategy for oesophageal cancer patients achieving a complete clinical response after neoadjuvant chemoradiation is a burning topic. The available diagnostic tools, such as 18F-FDG PET/CT performed routinely, cannot accurately evaluate the presence or absence of the residual tumour. The emerging field of radiomics may encounter the critical challenge of personalised treatment. Radiomics is based on medical image analysis, executed by extracting information from many image features; it has been shown to provide valuable information for predicting treatment responses in oesophageal cancer. This systematic review with a meta-analysis aims to provide current evidence of 18F-FDG PET-based radiomics in predicting response treatments following neoadjuvant chemoradiotherapy in oesophageal cancer. A comprehensive literature review identified 1160 studies, of which five were finally included in the study. Our findings provided that pooled Area Under the Curve (AUC) of the five selected studies was relatively high at 0.821 (95% CI: 0.737–0.904) and not influenced by the sample size of the studies. Radiomics models exhibited a good performance in predicting pathological complete responses (pCRs). This review further strengthens the great potential of 18F-FDG PET-based radiomics to predict pCRs in oesophageal cancer patients who underwent neoadjuvant chemoradiotherapy. Additionally, our review imparts additional support to prospective studies on 18F-FDG PET radiomics for a tailored treatment strategy of oesophageal cancer patients.
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Affiliation(s)
- Letizia Deantonio
- Radiation Oncology Clinic, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland.,University of Southern Switzerland, Faculty of Biomedical Sciences, Lugano, Switzerland
| | | | - Gaetano Paone
- Clinic for Nuclear Medicine and Molecular Imaging, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Maria Carla Valli
- Radiation Oncology Clinic, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
| | - Stefano Cappio
- Clinic for Radiology, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Davide La Regina
- Department of Surgery, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.,University of Southern Switzerland, Faculty of Biomedical Sciences, Lugano, Switzerland
| | - Marco Cefali
- Department of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
| | - Maria Celeste Palmarocchi
- Department of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
| | | | - Sara De Dosso
- University of Southern Switzerland, Faculty of Biomedical Sciences, Lugano, Switzerland.,Department of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
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