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Rabe M, Kurz C, Thummerer A, Landry G. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlenther Onkol 2024:10.1007/s00066-024-02277-9. [PMID: 39138806 DOI: 10.1007/s00066-024-02277-9] [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: 03/01/2024] [Accepted: 07/07/2024] [Indexed: 08/15/2024]
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- Bavarian Cancer Research Center (BZKF), Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
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2
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Hu Y, Cheng M, Wei H, Liang Z. A joint learning framework for multisite CBCT-to-CT translation using a hybrid CNN-transformer synthesizer and a registration network. Front Oncol 2024; 14:1440944. [PMID: 39175474 PMCID: PMC11338897 DOI: 10.3389/fonc.2024.1440944] [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: 05/30/2024] [Accepted: 07/19/2024] [Indexed: 08/24/2024] Open
Abstract
Background Cone-beam computed tomography (CBCT) is a convenient method for adaptive radiation therapy (ART), but its application is often hindered by its image quality. We aim to develop a unified deep learning model that can consistently enhance the quality of CBCT images across various anatomical sites by generating synthetic CT (sCT) images. Methods A dataset of paired CBCT and planning CT images from 135 cancer patients, including head and neck, chest and abdominal tumors, was collected. This dataset, with its rich anatomical diversity and scanning parameters, was carefully selected to ensure comprehensive model training. Due to the imperfect registration, the inherent challenge of local structural misalignment of paired dataset may lead to suboptimal model performance. To address this limitation, we propose SynREG, a supervised learning framework. SynREG integrates a hybrid CNN-transformer architecture designed for generating high-fidelity sCT images and a registration network designed to correct local structural misalignment dynamically during training. An independent test set of 23 additional patients was used to evaluate the image quality, and the results were compared with those of several benchmark models (pix2pix, cycleGAN and SwinIR). Furthermore, the performance of an autosegmentation application was also assessed. Results The proposed model disentangled sCT generation from anatomical correction, leading to a more rational optimization process. As a result, the model effectively suppressed noise and artifacts in multisite applications, significantly enhancing CBCT image quality. Specifically, the mean absolute error (MAE) of SynREG was reduced to 16.81 ± 8.42 HU, whereas the structural similarity index (SSIM) increased to 94.34 ± 2.85%, representing improvements over the raw CBCT data, which had the MAE of 26.74 ± 10.11 HU and the SSIM of 89.73 ± 3.46%. The enhanced image quality was particularly beneficial for organs with low contrast resolution, significantly increasing the accuracy of automatic segmentation in these regions. Notably, for the brainstem, the mean Dice similarity coefficient (DSC) increased from 0.61 to 0.89, and the MDA decreased from 3.72 mm to 0.98 mm, indicating a substantial improvement in segmentation accuracy and precision. Conclusions SynREG can effectively alleviate the differences in residual anatomy between paired datasets and enhance the quality of CBCT images.
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Affiliation(s)
- Ying Hu
- School of Mathematics and Statistics, Hubei University of Education, Wuhan, Hubei, China
- Bigdata Modeling and Intelligent Computing Research Institute, Hubei University of Education, Wuhan, Hubei, China
| | - Mengjie Cheng
- Nutrition Department, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hui Wei
- Department of Radiotherapy, Affiliated Hospital of Hebei Engineering University, Handan, China
| | - Zhiwen Liang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
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3
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Erdur AC, Rusche D, Scholz D, Kiechle J, Fischer S, Llorián-Salvador Ó, Buchner JA, Nguyen MQ, Etzel L, Weidner J, Metz MC, Wiestler B, Schnabel J, Rueckert D, Combs SE, Peeken JC. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives. Strahlenther Onkol 2024:10.1007/s00066-024-02262-2. [PMID: 39105745 DOI: 10.1007/s00066-024-02262-2] [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: 01/21/2024] [Accepted: 06/13/2024] [Indexed: 08/07/2024]
Abstract
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
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Affiliation(s)
- Ayhan Can Erdur
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
| | - Daniel Rusche
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Daniel Scholz
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Johannes Kiechle
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
| | - Stefan Fischer
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
| | - Óscar Llorián-Salvador
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department for Bioinformatics and Computational Biology - i12, Technical University of Munich, Boltzmannstraße 3, 85748, Garching, Bavaria, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz (JGU), Hüsch-Weg 15, 55128, Mainz, Rhineland-Palatinate, Germany
| | - Josef A Buchner
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Mai Q Nguyen
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
| | - Jonas Weidner
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Julia Schnabel
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Bavaria, Germany
- School of Biomedical Engineering & Imaging Sciences, King's College London, Strand, WC2R 2LS, London, London, UK
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Faculty of Engineering, Department of Computing, Imperial College London, Exhibition Rd, SW7 2BX, London, London, UK
| | - Stephanie E Combs
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
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Akramova R, Watanabe Y. Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation. J Appl Clin Med Phys 2024; 25:e14442. [PMID: 38922790 PMCID: PMC11302798 DOI: 10.1002/acm2.14442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/04/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD). MATERIALS/METHODS The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors. RESULTS The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet-LLL first order Maximum, wavelet-LLL glcm MCC, wavelet-LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9 mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC. CONCLUSIONS This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto-segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto-segmentation methods and enhance trainees' segmentation skills in medical training and education.
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Affiliation(s)
- Rukhsora Akramova
- Department of Radiation OncologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Yoichi Watanabe
- Department of Radiation OncologyUniversity of MinnesotaMinneapolisMinnesotaUSA
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5
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Behzadipour M, Palta J, Ma T, Yuan L, Kim S, Kirby S, Torkelson L, Baker J, Koenig T, Khalifa MA, Hawranko R, Richeson D, Fields E, Weiss E, Song WY. Optimization of treatment workflow for 0.35T MR-Linac system. J Appl Clin Med Phys 2024; 25:e14393. [PMID: 38742819 PMCID: PMC11302807 DOI: 10.1002/acm2.14393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/15/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
PURPOSE This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.
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Affiliation(s)
- Mojtaba Behzadipour
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tianjun Ma
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Lulin Yuan
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Siyong Kim
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Suzanne Kirby
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Laurel Torkelson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James Baker
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tammy Koenig
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mateb Al Khalifa
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert Hawranko
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Dylan Richeson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Emma Fields
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Elisabeth Weiss
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William Y. Song
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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6
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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [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: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Ono T, Iramina H, Hirashima H, Adachi T, Nakamura M, Mizowaki T. Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions. JOURNAL OF RADIATION RESEARCH 2024; 65:421-432. [PMID: 38798135 PMCID: PMC11262865 DOI: 10.1093/jrr/rrae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/26/2024] [Indexed: 05/29/2024]
Abstract
Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.
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Affiliation(s)
- Tomohiro Ono
- Department of Radiation Oncology, Shiga General Hospital, 5-4-30 Moriyama, Moriyama-shi 524-8524, Shiga, Japan
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Hideaki Hirashima
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Mitsuhiro Nakamura
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
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8
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Kalsi S, French H, Chhaya S, Madani H, Mir R, Anosova A, Dubash S. The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning-A Literature Review. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00218-8. [PMID: 38981781 DOI: 10.1016/j.clon.2024.06.005] [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: 09/14/2023] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 07/11/2024]
Abstract
This paper examines the integration of artificial intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process make AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas such as cardiology and dermatology. Focusing on radiotherapy, the paper details AI's applications in target segmentation, dose optimization, and outcome prediction. It discusses adaptive radiotherapy's benefits and AI's potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI's role is crucial as it acknowledges potential job-role changes and concerns about patients' trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI's potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilization. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its' success in radiotherapy and healthcare.
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Affiliation(s)
- S Kalsi
- Lister Hospital, Stevenage, United Kingdom.
| | - H French
- University of Chester, United Kingdom
| | - S Chhaya
- New Cross Hospital, Wolverhampton, United Kingdom
| | - H Madani
- Lister Hospital, Stevenage, United Kingdom
| | - R Mir
- Mount Vernon Cancer Centre, Northwood, United Kingdom; University of Manchester, Manchester, United Kingdom
| | - A Anosova
- Mount Vernon Cancer Centre, East & North Hertfordshire NHS Trust, United Kingdom
| | - S Dubash
- Mount Vernon Cancer Centre, Northwood, United Kingdom; Department of Surgery and Cancer, Imperial College, London, United Kingdom
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9
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Temple SWP, Rowbottom CG. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. J Appl Clin Med Phys 2024; 25:e14273. [PMID: 38263866 PMCID: PMC11163497 DOI: 10.1002/acm2.14273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024] Open
Abstract
PURPOSE Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-observer variability. There has been little research investigating gross failure rates and failure modes of such systems. METHOD 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI-generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI-based commercial system was applied to 500 H&N patients. AI-generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI-based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub-types (setup position, anatomy, image artefacts and unknown). RESULTS There were 24 true failures of the AI-based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). CONCLUSION True failures of the AI-based system were predominantly associated with a non-standard element within the CT scan. It is likely that these non-standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI-based system in the H&N region for the OARs investigated was low (∼1%).
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Affiliation(s)
- Simon W. P. Temple
- Medical Physics DepartmentThe Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Carl G. Rowbottom
- Medical Physics DepartmentThe Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
- Department of PhysicsUniversity of LiverpoolLiverpoolUK
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10
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Wahid KA, Sahin O, Kundu S, Lin D, Alanis A, Tehami S, Kamel S, Duke S, Sherer MV, Rasmussen M, Korreman S, Fuentes D, Cislo M, Nelms BE, Christodouleas JP, Murphy JD, Mohamed AS, He R, Naser MA, Gillespie EF, Fuller CD. Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation. JCO Clin Cancer Inform 2024; 8:e2300174. [PMID: 38870441 PMCID: PMC11214868 DOI: 10.1200/cci.23.00174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/08/2024] [Accepted: 04/03/2024] [Indexed: 06/15/2024] Open
Abstract
PURPOSE The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.
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Affiliation(s)
- Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Onur Sahin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Suprateek Kundu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Anthony Alanis
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Salik Tehami
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Serageldin Kamel
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Simon Duke
- Department of Radiation Oncology, Cambridge University Hospitals, Cambridge, United Kingdom
| | - Michael V. Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA
| | - Mathis Rasmussen
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Stine Korreman
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Cislo
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - John P. Christodouleas
- Department of Radiation Oncology, The University of Pennsylvania Cancer Center, Philadelphia, PA
- Elekta, Atlanta, GA
| | - James D. Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA
| | - Abdallah S.R. Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mohammed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Erin F. Gillespie
- Department of Radiation Oncology, University of Washington Fred Hutchinson Cancer Center, Seattle, WA
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
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11
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Bozzo A, Tsui JMG, Bhatnagar S, Forsberg J. Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery. J Am Acad Orthop Surg 2024; 32:e523-e532. [PMID: 38652882 PMCID: PMC11075751 DOI: 10.5435/jaaos-d-23-00831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/13/2024] [Accepted: 03/01/2024] [Indexed: 04/25/2024] Open
Abstract
This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data. Then, we contrast these deep learning techniques with related but more limited techniques such as radiomics, describe how to interpret deep learning studies, and how to initiate such studies at your institution. Ultimately, by empowering orthopaedic surgeons with the knowledge and know-how of deep learning, this review aspires to facilitate the translation of research into clinical practice, thereby enhancing the efficacy and precision of real-world orthopaedic care for patients.
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Affiliation(s)
- Anthony Bozzo
- From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg)
| | - James M. G. Tsui
- From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg)
| | - Sahir Bhatnagar
- From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg)
| | - Jonathan Forsberg
- From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg)
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12
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Zhang Y, Xu HR, Wen JH, Hu YJ, Diao YL, Chen JL, Xia YF. A novel LVPA-UNet network for target volume automatic delineation: An MRI case study of nasopharyngeal carcinoma. Heliyon 2024; 10:e30763. [PMID: 38770315 PMCID: PMC11103467 DOI: 10.1016/j.heliyon.2024.e30763] [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: 02/08/2024] [Revised: 04/08/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
Accurate delineation of Gross Tumor Volume (GTV) is crucial for radiotherapy. Deep learning-driven GTV segmentation technologies excel in rapidly and accurately delineating GTV, providing a basis for radiologists in formulating radiation plans. The existing 2D and 3D segmentation models of GTV based on deep learning are limited by the loss of spatial features and anisotropy respectively, and are both affected by the variability of tumor characteristics, blurred boundaries, and background interference. All these factors seriously affect the segmentation performance. To address the above issues, a Layer-Volume Parallel Attention (LVPA)-UNet model based on 2D-3D architecture has been proposed in this study, in which three strategies are introduced. Firstly, 2D and 3D workflows are introduced in the LVPA-UNet. They work in parallel and can guide each other. Both the fine features of each slice of 2D MRI and the 3D anatomical structure and spatial features of the tumor can be extracted by them. Secondly, parallel multi-branch depth-wise strip convolutions adapt the model to tumors of varying shapes and sizes within slices and volumetric spaces, and achieve refined processing of blurred boundaries. Lastly, a Layer-Channel Attention mechanism is proposed to adaptively adjust the weights of slices and channels according to their different tumor information, and then to highlight slices and channels with tumor. The experiments by LVPA-UNet on 1010 nasopharyngeal carcinoma (NPC) MRI datasets from three centers show a DSC of 0.7907, precision of 0.7929, recall of 0.8025, and HD95 of 1.8702 mm, outperforming eight typical models. Compared to the baseline model, it improves DSC by 2.14 %, precision by 2.96 %, and recall by 1.01 %, while reducing HD95 by 0.5434 mm. Consequently, while ensuring the efficiency of segmentation through deep learning, LVPA-UNet is able to provide superior GTV delineation results for radiotherapy and offer technical support for precision medicine.
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Affiliation(s)
- Yu Zhang
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China
| | - Hao-Ran Xu
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China
| | - Jun-Hao Wen
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China
| | - Yu-Jun Hu
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China
| | - Yin-Liang Diao
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China
| | - Jun-Liang Chen
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China
| | - Yun-Fei Xia
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China
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13
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Finnegan RN, Quinn A, Booth J, Belous G, Hardcastle N, Stewart M, Griffiths B, Carroll S, Thwaites DI. Cardiac substructure delineation in radiation therapy - A state-of-the-art review. J Med Imaging Radiat Oncol 2024. [PMID: 38757728 DOI: 10.1111/1754-9485.13668] [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: 01/24/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024]
Abstract
Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.
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Affiliation(s)
- Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Alexandra Quinn
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Jeremy Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Maegan Stewart
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Brooke Griffiths
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Susan Carroll
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
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14
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Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
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15
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Alzahrani NM, Henry AM, Clark AK, Al‐Qaisieh BM, Murray LJ, Nix MG. Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs. J Appl Clin Med Phys 2024; 25:e14345. [PMID: 38664894 PMCID: PMC11087158 DOI: 10.1002/acm2.14345] [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: 11/16/2023] [Revised: 01/12/2024] [Accepted: 03/05/2024] [Indexed: 05/12/2024] Open
Abstract
PURPOSE To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.
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Affiliation(s)
- Nouf M. Alzahrani
- Department of Diagnostic RadiologyKing Abdulaziz UniversityJeddahSaudi Arabia
- School of MedicineUniversity of LeedsLeedsUK
- Department of Medical Physics and EngineeringSt James's University HospitalLeedsUK
| | - Ann M. Henry
- School of MedicineUniversity of LeedsLeedsUK
- Department of Clinical OncologySt James's University HospitalLeedsUK
| | - Anna K. Clark
- Department of Medical Physics and EngineeringSt James's University HospitalLeedsUK
| | - Bashar M. Al‐Qaisieh
- Department of Medical Physics and EngineeringSt James's University HospitalLeedsUK
| | - Louise J. Murray
- School of MedicineUniversity of LeedsLeedsUK
- Department of Clinical OncologySt James's University HospitalLeedsUK
| | - Michael G. Nix
- Department of Medical Physics and EngineeringSt James's University HospitalLeedsUK
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Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, Nie X, Yu J, Tong J, Liao X. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging 2024; 24:36. [PMID: 38486342 PMCID: PMC10938723 DOI: 10.1186/s40644-024-00682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
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Affiliation(s)
- Haiqing Fan
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Fang Gu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yongqin Xiong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Guipeng Wu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Nie
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Jing Yu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Juan Tong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
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Yan Y, Kehayias C, He J, Aerts HJWL, Fitzgerald KJ, Kann BH, Kozono DE, Guthier CV, Mak RH. Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus. Sci Rep 2024; 14:2536. [PMID: 38291051 PMCID: PMC10827712 DOI: 10.1038/s41598-023-50382-z] [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: 08/09/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024] Open
Abstract
Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routine clinical care. We evaluated manual segmentation by comparing the length and edge roughness of segmentations among physicians to analyze inconsistencies. We trained eight multiple- and individual-physician segmentation models in total, based on U-Net architectures and residual backbones. We used the volumetric Dice coefficient to measure the performance for each model. We proposed a metric, edge roughness, to quantify the shift of segmentation among adjacent slices by calculating the curvature of edges of the 2D sagittal- and coronal-view projections. The auto-segmentation model trained on multiple physicians (MD1-7) achieved the highest mean Dice of 73.7 ± 14.8%. The individual-physician model (MD7) with the highest edge roughness (mean ± SD: 0.106 ± 0.016) demonstrated significantly lower volumetric Dice for test cases compared with other individual models (MD7: 58.5 ± 15.8%, MD6: 67.1 ± 16.8%, p < 0.001). A multiple-physician model trained after removing the MD7 data resulted in fewer outliers (e.g., Dice ≤ 40%: 4 cases for MD1-6, 7 cases for MD1-7, Ntotal = 394). While we initially detected this pattern in a single clinician, we validated the edge roughness metric across the entire dataset. The model trained with the lowest-quantile edge roughness (MDER-Q1, Ntrain = 62) achieved significantly higher Dice (Ntest = 270) than the model trained with the highest-quantile ones (MDER-Q4, Ntrain = 62) (MDER-Q1: 67.8 ± 14.8%, MDER-Q4: 62.8 ± 15.7%, p < 0.001). This study demonstrates that there is significant variation in style and quality in manual segmentations in clinical care, and that training AI auto-segmentation algorithms from real-world, clinical datasets may result in unexpectedly under-performing algorithms with the inclusion of outliers. Importantly, this study provides a novel evaluation metric, edge roughness, to quantify physician variation in segmentation which will allow developers to filter clinical training data to optimize model performance.
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Affiliation(s)
- Yujie Yan
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Christopher Kehayias
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - John He
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Kelly J Fitzgerald
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Benjamin H Kann
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - David E Kozono
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Christian V Guthier
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Raymond H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
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Paudyal R, Jiang J, Han J, Diplas BH, Riaz N, Hatzoglou V, Lee N, Deasy JO, Veeraraghavan H, Shukla-Dave A. Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae004. [PMID: 38476956 PMCID: PMC10928808 DOI: 10.1093/bjrai/ubae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/14/2024]
Abstract
Objectives Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant. Results No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively. Conclusions The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC. Advances in knowledge First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - James Han
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Bill H Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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19
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Kishigami Y, Nakamura M, Okamoto H, Takahashi A, Iramina H, Sasaki M, Kawata K, Igaki H. Organ-contour-driven auto-matching algorithm in image-guided radiotherapy. J Appl Clin Med Phys 2024; 25:e14220. [PMID: 37994694 PMCID: PMC10795436 DOI: 10.1002/acm2.14220] [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: 08/02/2023] [Revised: 11/02/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE This study aimed to demonstrate the potential clinical applicability of an organ-contour-driven auto-matching algorithm in image-guided radiotherapy. METHODS This study included eleven consecutive patients with cervical cancer who underwent radiotherapy in 23 or 25 fractions. Daily and reference magnetic resonance images were converted into mesh models. A weight-based algorithm was implemented to optimize the distance between the mesh model vertices and surface of the reference model during the positioning process. Within the cost function, weight parameters were employed to prioritize specific organs for positioning. In this study, three scenarios with different weight parameters were prepared. The optimal translation and rotation values for the cervix and uterus were determined based on the calculated translations alone or in combination with rotations, with a rotation limit of ±3°. Subsequently, the coverage probabilities of the following two planning target volumes (PTV), an isotropic 5 mm and anisotropic margins derived from a previous study, were evaluated. RESULTS The percentage of translations exceeding 10 mm varied from 9% to 18% depending on the scenario. For small PTV sizes, more than 80% of all fractions had a coverage of 80% or higher. In contrast, for large PTV sizes, more than 90% of all fractions had a coverage of 95% or higher. The difference between the median coverage with translational positioning alone and that with both translational and rotational positioning was 1% or less. CONCLUSION This algorithm facilitates quantitative positioning by utilizing a cost function that prioritizes organs for positioning. Consequently, consistent displacement values were algorithmically generated. This study also revealed that the impact of rotational corrections, limited to ±3°, on PTV coverage was minimal.
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Affiliation(s)
- Yukako Kishigami
- Department of Advanced Medical PhysicsGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Mitsuhiro Nakamura
- Department of Advanced Medical PhysicsGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Hiroyuki Okamoto
- Radiation Safety and Quality Assurance DivisionNational Cancer Center HospitalTokyoJapan
| | - Ayaka Takahashi
- Department of Radiation OncologyNational Cancer Center HospitalTokyoJapan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image‐Applied TherapyKyoto UniversityKyotoJapan
| | - Makoto Sasaki
- Division of Clinical Radiology ServiceKyoto University HospitalKyotoJapan
| | - Kohei Kawata
- Department of Radiation Oncology and Image‐Applied TherapyKyoto UniversityKyotoJapan
| | - Hiroshi Igaki
- Department of Radiation OncologyNational Cancer Center HospitalTokyoJapan
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20
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Soomro S, Niaz A, Soomro TA, Kim J, Manzoor A, Choi KN. Selective image segmentation driven by region, edge and saliency functions. PLoS One 2023; 18:e0294789. [PMID: 38100430 PMCID: PMC10723724 DOI: 10.1371/journal.pone.0294789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/07/2023] [Indexed: 12/17/2023] Open
Abstract
Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.
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Affiliation(s)
- Shafiullah Soomro
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
- Department of Computer Science and Media Technology, Linnaeus University, Vaxjo, Sweden
| | - Asim Niaz
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
| | | | - Jin Kim
- SecuLayer Inc., Seoul, South Korea
| | - Adnan Manzoor
- Department of Artificial Intelligence, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah, Sindh, Pakistan
| | - Kwang Nam Choi
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
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21
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Nenoff L, Amstutz F, Murr M, Archibald-Heeren B, Fusella M, Hussein M, Lechner W, Zhang Y, Sharp G, Vasquez Osorio E. Review and recommendations on deformable image registration uncertainties for radiotherapy applications. Phys Med Biol 2023; 68:24TR01. [PMID: 37972540 PMCID: PMC10725576 DOI: 10.1088/1361-6560/ad0d8a] [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: 04/11/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
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Affiliation(s)
- Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, Dresden Germany
- Helmholtz-Zentrum Dresden—Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
| | - Florian Amstutz
- Department of Physics, ETH Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Mohammad Hussein
- Metrology for Medical Physics, National Physical Laboratory, Teddington, United Kingdom
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Greg Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
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22
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Yamauchi R, Itazawa T, Kobayashi T, Kashiyama S, Akimoto H, Mizuno N, Kawamori J. Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation. Med Dosim 2023; 49:167-176. [PMID: 38061916 DOI: 10.1016/j.meddos.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/03/2023] [Accepted: 11/02/2023] [Indexed: 08/04/2024]
Abstract
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. Recently, deep learning auto-segmentation (DL-AS) algorithms have emerged as the state-of-the-art in medical image segmentation. This study aimed to evaluate the effect of auto-segmentation on the clinical workflow for contouring different anatomical sites of cancer, such as head and neck (H&N), breast, abdominal region, and prostate. Patients with H&N, breast, abdominal, and prostate cancer (n = 30 each) were enrolled in the study. Twenty-seven different organs at four sites were evaluated. RayStation was used to apply the ABAS. Siemens AI-Rad Companion Organs RT was used to apply the DL-AS. Evaluations were performed with similarity indices using geometric methods, time-evaluation, and qualitative scoring visual evaluations by radiation oncologists. The DL-AS algorithm was more accurate than ABAS algorithm on geometric indices for half of the structures. The qualitative scoring results of the two algorithms were significantly different, and DL-AS was more accurate on many contours. DL-AS had 41%, 29%, 86%, and 15% shorter edit times in the HnN, breast, abdomen, and prostate groups, respectively, than ABAS. There were no correlations between the geometric indices and visual assessments. The time required to edit the contours was considerably shorter for DL-AS than for ABAS. Auto-segmentation with deep learning could be the first step for clinical workflow optimization in radiotherapy.
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Affiliation(s)
- Ryohei Yamauchi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan.
| | - Tomoko Itazawa
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Takako Kobayashi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Shiho Kashiyama
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Japanese Red Cross Saitama Hospital, Saitama, Japan
| | - Hiroyoshi Akimoto
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Nippon Medical School Musashikosugi Hospital, Kanagawa, Japan
| | - Norifumi Mizuno
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Jiro Kawamori
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
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23
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Andrearczyk V, Oreiller V, Boughdad S, Le Rest CC, Tankyevych O, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M, Depeursinge A. Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge. Med Image Anal 2023; 90:102972. [PMID: 37742374 DOI: 10.1016/j.media.2023.102972] [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: 09/20/2022] [Revised: 07/27/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.
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Affiliation(s)
- Vincent Andrearczyk
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
| | - Valentin Oreiller
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Hesham Elhalawani
- Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, United States of America
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Adrien Depeursinge
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
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24
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Vivancos Bargalló H, Stick LB, Korreman SS, Kronborg C, Nielsen MM, Borgen AC, Offersen BV, Nørrevang O, Kallehauge JF. Classification of laterality and mastectomy/lumpectomy for breast cancer patients for improved performance of deep learning auto segmentation. Acta Oncol 2023; 62:1546-1550. [PMID: 37584197 DOI: 10.1080/0284186x.2023.2245965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/03/2023] [Indexed: 08/17/2023]
Affiliation(s)
- Helena Vivancos Bargalló
- Medical Physics department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | - Stine Sofia Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Camilla Kronborg
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mathias M Nielsen
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | - Birgitte Vrou Offersen
- Department of Experimental Clinical Oncologyy, Aarhus University Hospital, Aarhus, Denmark
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Ole Nørrevang
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper F Kallehauge
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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25
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Landry G, Kurz C, Traverso A. The role of artificial intelligence in radiotherapy clinical practice. BJR Open 2023; 5:20230030. [PMID: 37942500 PMCID: PMC10630974 DOI: 10.1259/bjro.20230030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/13/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023] Open
Abstract
This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.
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Affiliation(s)
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
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26
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Groen JA, Crezee J, van Laarhoven HWM, Bijlsma MF, Kok HP. Quantification of tissue property and perfusion uncertainties in hyperthermia treatment planning: Multianalysis using polynomial chaos expansion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107675. [PMID: 37339535 DOI: 10.1016/j.cmpb.2023.107675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 06/22/2023]
Abstract
INTRODUCTION Hyperthermia treatment planning (HTP) tools can guide treatment delivery, particularly with locoregional radiative phased array systems. Uncertainties in tissue and perfusion property values presently lead to quantitative inaccuracy of HTP, leading to sub-optimal treatment. Assessment of these uncertainties would allow for better judgement of the reliability of treatment plans and improve their value for treatment guidance. However, systematically investigating the impact of all uncertainties on treatment plans is a complex, high-dimensional problem and too computationally expensive for traditional Monte Carlo approaches. This study aims to systematically quantify the treatment-plan impact of tissue property uncertainties by investigating their individual contribution to, and combined impact on predicted temperature distributions. METHODS A novel Polynomial Chaos Expansion (PCE)-based HTP uncertainty quantification was developed and applied for locoregional hyperthermia of modelled tumours in the pancreatic head, prostate, rectum, and cervix. Patient models were based on the Duke and Ella digital human models. Using Plan2Heat, treatment plans were created to optimise tumour temperature (represented by T90) for treatment using the Alba4D system. For all 25-34 modelled tissues, the impact of tissue property uncertainties was analysed individually i.e., electrical and thermal conductivity, permittivity, density, specific heat capacity and perfusion. Next, combined analyses were performed on the top 30 uncertainties with the largest impact. RESULTS Uncertainties in thermal conductivity and heat capacity were found to have negligible impact on the predicted temperature ( < 1 × 10-10 °C), density and permittivity uncertainties had a small impact (< 0.3 °C). Uncertainties in electrical conductivity and perfusion can lead to large variations in predicted temperature. However, variations in muscle properties result in the largest impact at locations that could limit treatment quality, with a standard deviation up to almost 6 °C (pancreas) and 3.5 °C (prostate) for perfusion and electrical conductivity, respectively. The combined influence of all significant uncertainties leads to large variations with a standard deviation up to 9.0, 3.6, 3.7 and 4.1 °C for the pancreatic, prostate, rectal and cervical cases, respectively. CONCLUSION Uncertainties in tissue and perfusion property values can have a large impact on predicted temperatures from hyperthermia treatment planning. PCE-based analysis helps to identify all major uncertainties, their impact and judge the reliability of treatment plans.
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Affiliation(s)
- Jort A Groen
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands.
| | - Johannes Crezee
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
| | - Hanneke W M van Laarhoven
- Amsterdam UMC location University of Amsterdam, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
| | - Maarten F Bijlsma
- Amsterdam UMC location University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and biomarkers, Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - H Petra Kok
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
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Heilemann G, Buschmann M, Lechner W, Dick V, Eckert F, Heilmann M, Herrmann H, Moll M, Knoth J, Konrad S, Simek IM, Thiele C, Zaharie A, Georg D, Widder J, Trnkova P. Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy. Phys Imaging Radiat Oncol 2023; 28:100515. [PMID: 38111502 PMCID: PMC10726238 DOI: 10.1016/j.phro.2023.100515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/20/2023] Open
Abstract
Background and purpose Tools for auto-segmentation in radiotherapy are widely available, but guidelines for clinical implementation are missing. The goal was to develop a workflow for performance evaluation of three commercial auto-segmentation tools to select one candidate for clinical implementation. Materials and Methods One hundred patients with six treatment sites (brain, head-and-neck, thorax, abdomen, and pelvis) were included. Three sets of AI-based contours for organs-at-risk (OAR) generated by three software tools and manually drawn expert contours were blindly rated for contouring accuracy. The dice similarity coefficient (DSC), the Hausdorff distance, and a dose/volume evaluation based on the recalculation of the original treatment plan were assessed. Statistically significant differences were tested using the Kruskal-Wallis test and the post-hoc Dunn Test with Bonferroni correction. Results The mean DSC scores compared to expert contours for all OARs combined were 0.80 ± 0.10, 0.75 ± 0.10, and 0.74 ± 0.11 for the three software tools. Physicians' rating identified equivalent or superior performance of some AI-based contours in head (eye, lens, optic nerve, brain, chiasm), thorax (e.g., heart and lungs), and pelvis and abdomen (e.g., kidney, femoral head) compared to manual contours. For some OARs, the AI models provided results requiring only minor corrections. Bowel-bag and stomach were not fit for direct use. During the interdisciplinary discussion, the physicians' rating was considered the most relevant. Conclusion A comprehensive method for evaluation and clinical implementation of commercially available auto-segmentation software was developed. The in-depth analysis yielded clear instructions for clinical use within the radiotherapy department.
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Affiliation(s)
- Gerd Heilemann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Wolfgang Lechner
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Vincent Dick
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Franziska Eckert
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Harald Herrmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Matthias Moll
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Johannes Knoth
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Stefan Konrad
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Inga-Malin Simek
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Christopher Thiele
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Alexandru Zaharie
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Petra Trnkova
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
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Robinson A, Asaduzzaman M, Jena R, Naemi R. Simulation as a tool to model potential workflow enhancements in radiotherapy treatment pathways - A systematic review. J Appl Clin Med Phys 2023; 24:e14132. [PMID: 37660393 PMCID: PMC10562027 DOI: 10.1002/acm2.14132] [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: 05/07/2023] [Revised: 06/20/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
This systematic review aimed to synthesize and summarize the use of simulation of radiotherapy pathways. The objective was to establish the suitability of those simulations in modeling the potential introduction of processes and technologies to speed up radiotherapy pathways. A systematic literature search was carried out using PubMed and Scopus databases to evaluate the use of simulation in radiotherapy pathways. Full journal articles and conference proceedings were considered, and the search was limited to the English language only. To be eligible for inclusion, articles had to model multiple sequential processes in the radiotherapy pathway concurrently to demonstrate the suitability of simulation modeling in typical pathways. Papers solely modeling scheduling, capacity, or queuing strategies were excluded. In total, 151 potential studies were identified and screened to find 18 relevant studies in October 2022. Studies showed that various pathways could be modeled, including the entire pathway from referral to end of treatment or the constituent phases such as pre-treatment, treatment, or other subcomponents. The data required to generate models varied from study to study, but at least 3 months of data were needed. This review demonstrates that modeling and simulation of radiotherapy pathways are feasible and that model output matches real-world systems. Validated models give researchers confidence to modify models with potential workflow enhancements to assess their potential effect on real-world systems. It is recommended that researchers follow best practice guidelines when building models to ensure that they are fit for purpose and to enable decision makers to have confidence in their results.
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Affiliation(s)
- Andrew Robinson
- School of Health, Science and WellbeingStaffordshire UniversityStoke on TrentUK
- Department of Medical PhysicsCambridge University Hospitals NHS Foundation TrustCambridgeUK
| | - Md Asaduzzaman
- School of DigitalTechnologies and ArtsStaffordshire UniversityStoke on TrentUK
| | - Raj Jena
- Department of OncologyUniversity of CambridgeCambridgeUK
| | - Roozbeh Naemi
- School of Health, Science and WellbeingStaffordshire UniversityStoke on TrentUK
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Wahid KA, Sahin O, Kundu S, Lin D, Alanis A, Tehami S, Kamel S, Duke S, Sherer MV, Rasmussen M, Korreman S, Fuentes D, Cislo M, Nelms BE, Christodouleas JP, Murphy JD, Mohamed ASR, He R, Naser MA, Gillespie EF, Fuller CD. Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.30.23294786. [PMID: 37693394 PMCID: PMC10491357 DOI: 10.1101/2023.08.30.23294786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
BACKGROUND Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean ± standard deviation: -0.97 ± 0.20), sarcoma (-1.04 ± 0.54), H&N (-1.00 ± 0.24), and GI (-2.95 ± 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.
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Affiliation(s)
- Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Onur Sahin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Suprateek Kundu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Anthony Alanis
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Salik Tehami
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Serageldin Kamel
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Simon Duke
- Department of Radiation Oncology, Cambridge University Hospitals, Cambridge, UK
| | - Michael V. Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | | | - Stine Korreman
- Department of Oncology, Aarhus University Hospital, Denmark
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael Cislo
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - John P. Christodouleas
- Department of Radiation Oncology, The University of Pennsylvania Cancer Center, Philadelphia, PA, USA
- Elekta, Atlanta, GA, USA
| | - James D. Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Abdallah S. R. Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohammed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Fiandra C, Rosati S, Arcadipane F, Dinapoli N, Fato M, Franco P, Gallio E, Scaffidi Gennarino D, Silvetti P, Zara S, Ricardi U, Balestra G. Active bone marrow segmentation based on computed tomography imaging in anal cancer patients: A machine-learning-based proof of concept. Phys Med 2023; 113:102657. [PMID: 37567068 DOI: 10.1016/j.ejmp.2023.102657] [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: 03/22/2023] [Revised: 06/30/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
PURPOSE Different methods are available to identify haematopoietically active bone marrow (ActBM). However, their use can be challenging for radiotherapy routine treatments, since they require specific equipment and dedicated time. A machine learning (ML) approach, based on radiomic features as inputs to three different classifiers, was applied to computed tomography (CT) images to identify haematopoietically active bone marrow in anal cancer patients. METHODS A total of 40 patients was assigned to the construction set (training set + test set). Fluorine-18-Fluorodeoxyglucose Positron Emission Tomography (18FDG-PET) images were used to detect the active part of the pelvic bone marrow (ActPBM) and stored as ground-truth for three subregions: iliac, lower pelvis and lumbosacral bone marrow (ActIBM, ActLPBM, ActLSBM). Three parameters were used for the correspondence analyses between 18FDG-PET and ML classifiers: DICE index, Precision and Recall. RESULTS For the 40-patient cohort, median values [min; max] of the Dice index were 0.69 [0.20; 0.84], 0.76 [0.25; 0.89], and 0.36 [0.15; 0.67] for ActIBM, ActLSBM, and ActLPBM, respectively. The Precision/Recall (P/R) ratio median value for the ActLPBM structure was 0.59 [0.20; 1.84] (over segmentation), while for the other two subregions the P/R ratio median has values of 1.249 [0.43; 4.15] for ActIBM and 1.093 [0.24; 1.91] for ActLSBM (under segmentation). CONCLUSION A satisfactory degree of overlap compared to 18FDG-PET was found for 2 out of the 3 subregions within pelvic bones. Further optimization and generalization of the process is required before clinical implementation.
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Affiliation(s)
- C Fiandra
- Department of Oncology, University of Turin, Turin, Italy.
| | - S Rosati
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - F Arcadipane
- Department of Oncology, University of Turin, Turin, Italy
| | - N Dinapoli
- UOC Radioterapia Oncologica, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - M Fato
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy
| | - P Franco
- Department of Oncology, University of Turin, Turin, Italy
| | - E Gallio
- Medical Physics Unit, A.O.U. Città della Salute e della Scienza, Turin, Italy
| | - D Scaffidi Gennarino
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - P Silvetti
- Department of Oncology, University of Turin, Turin, Italy
| | - S Zara
- Tecnologie Avanzate, Torino, Italy
| | - U Ricardi
- Department of Oncology, University of Turin, Turin, Italy
| | - G Balestra
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
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Amjad A, Xu J, Thill D, Zhang Y, Ding J, Paulson E, Hall W, Erickson BA, Li XA. Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs. Front Oncol 2023; 13:1209558. [PMID: 37483486 PMCID: PMC10358771 DOI: 10.3389/fonc.2023.1209558] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs. Materials and methods Using a previously developed 3DResUnet network, a mS-DLAS model using 4 T1 and T2 weighted MRI acquired during routine RT simulation for 71 cases with abdominal tumors was trained and tested. Strategies including data pre-processing, Z-normalization approach, and data augmentation were employed. Additional 2 sequence specific T1 weighted (T1-M) and T2 weighted (T2-M) models were trained to evaluate performance of sequence-specific DLAS. Performance of all models was quantitatively evaluated using 6 surface and volumetric accuracy metrics. Results The developed DLAS models were able to generate reasonable contours of 12 upper abdomen organs within 21 seconds for each testing case. The 3D average values of dice similarity coefficient (DSC), mean distance to agreement (MDA mm), 95 percentile Hausdorff distance (HD95% mm), percent volume difference (PVD), surface DSC (sDSC), and relative added path length (rAPL mm/cc) over all organs were 0.87, 1.79, 7.43, -8.95, 0.82, and 12.25, respectively, for mS-DLAS model. Collectively, 71% of the auto-segmented contours by the three models had relatively high quality. Additionally, the obtained mS-DLAS successfully segmented 9 out of 16 MRI sequences that were not used in the model training. Conclusion We have developed an MRI-based mS-DLAS model for auto-segmenting of upper abdominal organs on MRI. Multi-sequence segmentation is desirable in routine clinical practice of RT for accurate organ and target delineation, particularly for abdominal tumors. Our work will act as a stepping stone for acquiring fast and accurate segmentation on multi-contrast MRI and make way for MR only guided radiation therapy.
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Affiliation(s)
- Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Dan Thill
- Elekta Inc., ST. Charles, MO, United States
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jie Ding
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Beth A. Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
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Court L, Aggarwal A, Burger H, Cardenas C, Chung C, Douglas R, du Toit M, Jaffray D, Jhingran A, Mejia M, Mumme R, Muya S, Naidoo K, Ndumbalo J, Nealon K, Netherton T, Nguyen C, Olanrewaju N, Parkes J, Shaw W, Trauernicht C, Xu M, Yang J, Zhang L, Simonds H, Beadle BM. Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant. JCO Glob Oncol 2023; 9:e2200431. [PMID: 37471671 PMCID: PMC10581646 DOI: 10.1200/go.22.00431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/08/2023] [Accepted: 04/24/2023] [Indexed: 07/22/2023] Open
Abstract
PURPOSE Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.
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Affiliation(s)
- Laurence Court
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ajay Aggarwal
- Guy's and St Thomas' Hospital, London, United Kingdom
| | - Hester Burger
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | | | - Christine Chung
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Raphael Douglas
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Monique du Toit
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - David Jaffray
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anuja Jhingran
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Mejia
- Benavides Cancer Institute, University of Santo Tomas, Manila, Philippines
| | - Raymond Mumme
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Komeela Naidoo
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | | | - Kelly Nealon
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Niki Olanrewaju
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jeannette Parkes
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Willie Shaw
- University of the Free State, Bloemfontein, South Africa
| | | | - Melody Xu
- University of California San Francisco, San Francisco, CA
| | - Jinzhong Yang
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lifei Zhang
- The University of Texas MD Anderson Cancer Center, Houston, TX
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Yang T, Zhu G, Cai L, Yeo JH, Mao Y, Yang J. A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root. Front Bioeng Biotechnol 2023; 11:1171868. [PMID: 37397959 PMCID: PMC10311214 DOI: 10.3389/fbioe.2023.1171868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/06/2023] [Indexed: 07/04/2023] Open
Abstract
Recent clinical studies have suggested that introducing 3D patient-specific aortic root models into the pre-operative assessment procedure of transcatheter aortic valve replacement (TAVR) would reduce the incident rate of peri-operative complications. Tradition manual segmentation is labor-intensive and low-efficient, which cannot meet the clinical demands of processing large data volumes. Recent developments in machine learning provided a viable way for accurate and efficient medical image segmentation for 3D patient-specific models automatically. This study quantitively evaluated the auto segmentation quality and efficiency of the four popular segmentation-dedicated three-dimensional (3D) convolutional neural network (CNN) architectures, including 3D UNet, VNet, 3D Res-UNet and SegResNet. All the CNNs were implemented in PyTorch platform, and low-dose CTA image sets of 98 anonymized patients were retrospectively selected from the database for training and testing of the CNNs. The results showed that despite all four 3D CNNs having similar recall, Dice similarity coefficient (DSC), and Jaccard index on the segmentation of the aortic root, the Hausdorff distance (HD) of the segmentation results from 3D Res-UNet is 8.56 ± 2.28, which is only 9.8% higher than that of VNet, but 25.5% and 86.4% lower than that of 3D UNet and SegResNet, respectively. In addition, 3D Res-UNet and VNet also performed better in the 3D deviation location of interest analysis focusing on the aortic valve and the bottom of the aortic root. Although 3D Res-UNet and VNet are evenly matched in the aspect of classical segmentation quality evaluation metrics and 3D deviation location of interest analysis, 3D Res-UNet is the most efficient CNN architecture with an average segmentation time of 0.10 ± 0.04 s, which is 91.2%, 95.3% and 64.3% faster than 3D UNet, VNet and SegResNet, respectively. The results from this study suggested that 3D Res-UNet is a suitable candidate for accurate and fast automatic aortic root segmentation for pre-operative assessment of TAVR.
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Affiliation(s)
- Tingting Yang
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Guangyu Zhu
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Li Cai
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China
| | - Joon Hock Yeo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yu Mao
- Department of Cardiac Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Jian Yang
- Department of Cardiac Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
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Bakx N, van der Sangen M, Theuws J, Bluemink H, Hurkmans C. Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets. Tech Innov Patient Support Radiat Oncol 2023; 26:100209. [PMID: 37213441 PMCID: PMC10199413 DOI: 10.1016/j.tipsro.2023.100209] [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: 02/14/2023] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction The development of deep learning (DL) models for auto-segmentation is increasing and more models become commercially available. Mostly, commercial models are trained on external data. To study the effect of using a model trained on external data, compared to the same model trained on in-house collected data, the performance of these two DL models was evaluated. Methods The evaluation was performed using in-house collected data of 30 breast cancer patients. Quantitative analysis was performed using Dice similarity coefficient (DSC), surface DSC (sDSC) and 95th percentile of Hausdorff Distance (95% HD). These values were compared with previously reported inter-observer variations (IOV). Results For a number of structures, statistically significant differences were found between the two models. For organs at risk, mean values for DSC ranged from 0.63 to 0.98 and 0.71 to 0.96 for the in-house and external model, respectively. For target volumes, mean DSC values of 0.57 to 0.94 and 0.33 to 0.92 were found. The difference of 95% HD values ranged 0.08 to 3.23 mm between the two models, except for CTVn4 with 9.95 mm. For the external model, both DSC and 95% HD are outside the range of IOV for CTVn4, whereas this is the case for the DSC found for the thyroid of the in-house model. Conclusions Statistically significant differences were found between both models, which were mostly within published inter-observer variations, showing clinical usefulness of both models. Our findings could encourage discussion and revision of existing guidelines, to further decrease inter-observer, but also inter-institute variability.
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Affiliation(s)
- Nienke Bakx
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | | | - Jacqueline Theuws
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | - Hanneke Bluemink
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
- Technical University Eindhoven, Faculties of Physics and Electrical Engineering, 5600MB Eindhoven, the Netherlands
- Corresponding author at: Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands.
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Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use, Perceptions of and Barriers to Implementation. Clin Oncol (R Coll Radiol) 2023; 35:219-226. [PMID: 36725406 DOI: 10.1016/j.clon.2023.01.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/14/2022] [Accepted: 01/19/2023] [Indexed: 01/25/2023]
Abstract
AIMS Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors. MATERIALS AND METHODS The Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters. RESULTS In total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country. CONCLUSION Careful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.
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Bourbonne V, Laville A, Wagneur N, Ghannam Y, Larnaudie A. Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective. Cancers (Basel) 2023; 15:cancers15072040. [PMID: 37046704 PMCID: PMC10093734 DOI: 10.3390/cancers15072040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/21/2023] [Accepted: 03/24/2023] [Indexed: 04/01/2023] Open
Abstract
Introduction: Segmentation of organs at risk (OARs) and target volumes need time and precision but are highly repetitive tasks. Radiation oncology has known tremendous technological advances in recent years, the latest being brought by artificial intelligence (AI). Despite the advantages brought by AI for segmentation, some concerns were raised by academics regarding the impact on young radiation oncologists’ training. A survey was thus conducted on young french radiation oncologists (ROs) by the SFjRO (Société Française des jeunes Radiothérapeutes Oncologues). Methodology: The SFjRO organizes regular webinars focusing on anatomical localization, discussing either segmentation or dosimetry. Completion of the survey was mandatory for registration to a dosimetry webinar dedicated to head and neck (H & N) cancers. The survey was generated in accordance with the CHERRIES guidelines. Quantitative data (e.g., time savings and correction needs) were not measured but determined among the propositions. Results: 117 young ROs from 35 different and mostly academic centers participated. Most centers were either already equipped with such solutions or planning to be equipped in the next two years. AI segmentation software was mostly useful for H & N cases. While for the definition of OARs, participants experienced a significant time gain using AI-proposed delineations, with almost 35% of the participants saving between 50–100% of the segmentation time, time gained for target volumes was significantly lower, with only 8.6% experiencing a 50–100% gain. Contours still needed to be thoroughly checked, especially target volumes for some, and edited. The majority of participants suggested that these tools should be integrated into the training so that future radiation oncologists do not neglect the importance of radioanatomy. Fully aware of this risk, up to one-third of them even suggested that AI tools should be reserved for senior physicians only. Conclusions: We believe this survey on automatic segmentation to be the first to focus on the perception of young radiation oncologists. Software developers should focus on enhancing the quality of proposed segmentations, while young radiation oncologists should become more acquainted with these tools.
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Affiliation(s)
- Vincent Bourbonne
- Radiation Oncology Department, University Hospital Brest, 2 Avenue Foch, 29200 Brest, France
- Société Française des Jeunes Radiothérapeutes Oncologues, 47 Rue de la Colonie, 75013 Paris, France
- Correspondence: ; Tel.: +33-298223398; Fax: +33-98223087
| | - Adrien Laville
- Radiation Oncology Department, University Hospital Amiens-Picardie, 30 Avenue de la Croix Jourdain, 80054 Amiens, France
| | - Nicolas Wagneur
- Société Française des Jeunes Radiothérapeutes Oncologues, 47 Rue de la Colonie, 75013 Paris, France
- Radiation Oncology Department, Institut de Cancérologie de l’Ouest, Centre Paul Papin, 15 Rue André Bocquel, 49055 Angers, France
| | - Youssef Ghannam
- Société Française des Jeunes Radiothérapeutes Oncologues, 47 Rue de la Colonie, 75013 Paris, France
- Radiation Oncology Department, Institut de Cancérologie de l’Ouest, Centre Paul Papin, 15 Rue André Bocquel, 49055 Angers, France
| | - Audrey Larnaudie
- Société Française des Jeunes Radiothérapeutes Oncologues, 47 Rue de la Colonie, 75013 Paris, France
- Radiation Oncology Department, Centre François Baclesse, 3 Avenue du Général Harris, 14000 Caen, France
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Wahid KA, Lin D, Sahin O, Cislo M, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Murphy JD, Fuller CD, Gillespie EF. Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites. Sci Data 2023; 10:161. [PMID: 36949088 PMCID: PMC10033824 DOI: 10.1038/s41597-023-02062-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/10/2023] [Indexed: 03/24/2023] Open
Abstract
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.
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Affiliation(s)
- Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Onur Sahin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael Cislo
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohammed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Simon Duke
- Department of Radiation Oncology, Cambridge University Hospitals, Cambridge, UK
| | - Michael V Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - John P Christodouleas
- Department of Radiation Oncology, The University of Pennsylvania Cancer Center, Philadelphia, PA, USA
- Elekta, Atlanta, GA, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Erin F Gillespie
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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Cubero L, Castelli J, Simon A, de Crevoisier R, Acosta O, Pascau J. Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data. ENTROPY (BASEL, SWITZERLAND) 2022; 24:e24111661. [PMID: 36421515 PMCID: PMC9689629 DOI: 10.3390/e24111661] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 06/06/2023]
Abstract
Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.
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Affiliation(s)
- Lucía Cubero
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, 28911 Madrid, Spain
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Joël Castelli
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Antoine Simon
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Renaud de Crevoisier
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Oscar Acosta
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Javier Pascau
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, 28911 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
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Sritharan K, Dunlop A, Mohajer J, Adair-Smith G, Barnes H, Brand D, Greenlay E, Hijab A, Oelfke U, Pathmanathan A, Mitchell A, Murray J, Nill S, Parker C, Sundahl N, Tree AC. Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow? Clin Transl Radiat Oncol 2022; 37:25-32. [PMID: 36052018 PMCID: PMC9424262 DOI: 10.1016/j.ctro.2022.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 10/31/2022] Open
Abstract
Background The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac. Methods Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume. Results The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface. Conclusion Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.
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Affiliation(s)
- Kobika Sritharan
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Alex Dunlop
- The Joint Department of Physics, the Royal Marsden Hospital and the Institute of Cancer Research, United Kingdom
| | - Jonathan Mohajer
- The Joint Department of Physics, the Royal Marsden Hospital and the Institute of Cancer Research, United Kingdom
| | | | - Helen Barnes
- The Royal Marsden NHS Foundation Trust, United Kingdom
| | | | | | - Adham Hijab
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Uwe Oelfke
- The Joint Department of Physics, the Royal Marsden Hospital and the Institute of Cancer Research, United Kingdom
| | - Angela Pathmanathan
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Adam Mitchell
- The Joint Department of Physics, the Royal Marsden Hospital and the Institute of Cancer Research, United Kingdom
| | - Julia Murray
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Simeon Nill
- The Joint Department of Physics, the Royal Marsden Hospital and the Institute of Cancer Research, United Kingdom
| | - Chris Parker
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Nora Sundahl
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
| | - Alison C. Tree
- The Royal Marsden NHS Foundation Trust, United Kingdom
- The Institute of Cancer Research, United Kingdom
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Watkins WT, Qing K, Han C, Hui S, Liu A. Auto-segmentation for total marrow irradiation. Front Oncol 2022; 12:970425. [PMID: 36110933 PMCID: PMC9468379 DOI: 10.3389/fonc.2022.970425] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis. Methods An AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen-Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures. Results A total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%). Conclusions AI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.
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Affiliation(s)
- William Tyler Watkins
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
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Delpon G, Barateau A, Beneux A, Bessières I, Latorzeff I, Welmant J, Tallet A. [What do we need to deliver "online" adapted radiotherapy treatment plans?]. Cancer Radiother 2022; 26:794-802. [PMID: 36028418 DOI: 10.1016/j.canrad.2022.06.024] [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: 06/22/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022]
Abstract
During the joint SFRO/SFPM session of the 2019 congress, a state of the art of adaptive radiotherapy announced a strong impact in our clinical practice, in particular with the availability of treatment devices coupled to an MRI system. Three years later, it seems relevant to take stock of adaptive radiotherapy in practice, and especially the "online" strategy because it is indeed more and more accessible with recent hardware and software developments, such as coupled accelerators to a three-dimensional imaging device and algorithms based on artificial intelligence. However, the deployment of this promising strategy is complex because it contracts the usual time scale and upsets the usual organizations. So what do we need to deliver adapted treatment plans with an "online" strategy?
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Affiliation(s)
- G Delpon
- Institut de cancérologie de l'Ouest, Saint-Herblain et IMT Atlantique, Nantes université, CNRS/IN2P3, Subatech, Nantes, France.
| | - A Barateau
- Université Rennes, CLCC Eugène-Marquis, Inserm, LTSI-UMR 1099, Rennes, France
| | - A Beneux
- Hospices Civils de Lyon, Lyon, France
| | - I Bessières
- Centre Georges-François Leclerc, Dijon, France
| | | | - J Welmant
- Institut du cancer de Montpellier, Montpellier, France
| | - A Tallet
- Institut Paoli-Calmettes, Marseille, France
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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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Nenoff L, Buti G, Bobić M, Lalonde A, Nesteruk KP, Winey B, Sharp GC, Sudhyadhom A, Paganetti H. Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans. Cancers (Basel) 2022; 14:cancers14163926. [PMID: 36010919 PMCID: PMC9406068 DOI: 10.3390/cancers14163926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023] Open
Abstract
Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.
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Affiliation(s)
- Lena Nenoff
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Correspondence:
| | - Gregory Buti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Mislav Bobić
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Physics, ETH Zurich, 8092 Zurich, Switzerland
| | - Arthur Lalonde
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Konrad P. Nesteruk
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian Winey
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gregory Charles Sharp
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Atchar Sudhyadhom
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Harald Paganetti
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
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Boon I, Rahman O, Gooding M, Boon C. Adopting Machine Learning Auto-Segmentation Into Radiotherapy Workflow. Clin Oncol (R Coll Radiol) 2022; 34:e314. [DOI: 10.1016/j.clon.2022.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 11/29/2022]
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Jena R, Dekker A, Kang J. A Glimmer of Hope Within the Mountain of Hype - Reviewing the Role of Artificial Intelligence in Radiotherapy. Clin Oncol (R Coll Radiol) 2021; 34:71-73. [PMID: 34924257 DOI: 10.1016/j.clon.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/03/2021] [Indexed: 11/03/2022]
Affiliation(s)
- R Jena
- Department of Oncology, University of Cambridge, Cambridge, UK.
| | - A Dekker
- MAASTRO Clinic, Maastricht, the Netherlands
| | - J Kang
- University of Washington Medical Center Montlake - Radiation Oncology Center, Seattle, Washington, USA
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