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Zhang X, Yan D, Xiao H, Zhong R. Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review. Radiat Oncol 2024; 19:140. [PMID: 39380013 PMCID: PMC11463122 DOI: 10.1186/s13014-024-02532-4] [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: 08/05/2024] [Accepted: 09/26/2024] [Indexed: 10/10/2024] Open
Abstract
The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4 Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field.
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Affiliation(s)
- Xiangbin Zhang
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China
| | - Di Yan
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Haonan Xiao
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Shandong Provincial Key Medical and Health Laboratory of Pediatric Cancer Precision Radiotherapy, Jinan, China
| | - Renming Zhong
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China.
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Chen C, He Z, Zhao J, Zhu X, Li J, Wu X, Chen Z, Chen H, Jia G. Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis. BMC Infect Dis 2024; 24:1062. [PMID: 39333964 PMCID: PMC11437667 DOI: 10.1186/s12879-024-09892-y] [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: 03/12/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Zoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease control. The aim of this study was to develop zoonotic diseases risk predictive models based on time-series incidence data and three zoonotic diseases in mainland China were employed as cases. METHODS The incidence data for schistosomiasis, echinococcosis, and leptospirosis were downloaded from the Scientific Data Centre of the National Ministry of Health of China, and were processed by interpolation, dynamic curve reconstruction and time series decomposition. Data were decomposed into three distinct components: the trend component, the seasonal component, and the residual component. The trend component was used as input to construct the Long Short-Term Memory (LSTM) prediction model, while the seasonal component was used in the comparison of the periods and amplitudes. Finaly, the accuracy of the hybrid LSTM prediction model was comprehensive evaluated. RESULTS This study employed trend series of incidence numbers and incidence rates of three zoonotic diseases for modeling. The prediction results of the model showed that the predicted incidence number and incidence rate were very close to the real incidence data. Model evaluation revealed that the prediction error of the hybrid LSTM model was smaller than that of the single LSTM. Thus, these results demonstrate that using trending sequences as input sequences for the model leads to better-fitting predictive models. CONCLUSIONS Our study successfully developed LSTM hybrid models for disease outbreak risk prediction using three zoonotic diseases as case studies. We demonstrate that the LSTM, when combined with time series decomposition, delivers more accurate results compared to conventional LSTM models using the raw data series. Disease outbreak trends can be predicted more accurately using hybrid models.
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Affiliation(s)
- Chunrong Chen
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Zhaoyuan He
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Jin Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Xuhui Zhu
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jiabao Li
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xinnan Wu
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhongting Chen
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Hailan Chen
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China.
| | - Gengjie Jia
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
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Numakura K, Takao S, Matsuura T, Yokokawa K, Chen Y, Uchinami Y, Taguchi H, Katoh N, Aoyama H, Tomioka S, Miyamoto N. Application of motion prediction based on a long short-term memory network for imaging dose reduction in real-time tumor-tracking radiation therapy. Phys Med 2024; 125:104507. [PMID: 39217787 DOI: 10.1016/j.ejmp.2024.104507] [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: 03/15/2024] [Revised: 06/12/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024] Open
Abstract
PURPOSE To demonstrate the possibility of using a lower imaging rate while maintaining acceptable accuracy by applying motion prediction to minimize the imaging dose in real-time image-guided radiation therapy. METHODS Time-series of three-dimensional internal marker positions obtained from 98 patients in liver stereotactic body radiation therapy were used to train and test the long-short-term memory (LSTM) network. For real-time imaging, the root mean squared error (RMSE) of the prediction on three-dimensional marker position made by LSTM, the residual motion of the target under respiratory-gated irradiation, and irradiation efficiency were evaluated. In the evaluation of the residual motion, the system-specific latency was assumed to be 100 ms. RESULTS Except for outliers in the superior-inferior (SI) direction, the median/maximum values of the RMSE for imaging rates of 7.5, 5.0, and 2.5 frames per second (fps) were 0.8/1.3, 0.9/1.6, and 1.2/2.4 mm, respectively. The median/maximum residual motion in the SI direction at an imaging rate of 15.0 fps without prediction of the marker position, which is a typical clinical setting, was 2.3/3.6 mm. For rates of 7.5, 5.0, and 2.5 fps with prediction, the corresponding values were 2.0/2.6, 2.2/3.3, and 2.4/3.9 mm, respectively. There was no significant difference between the irradiation efficiency with and that without prediction of the marker position. The geometrical accuracy at lower frame rates with prediction applied was superior or comparable to that at 15 fps without prediction. In comparison with the current clinical setting for real-time image-guided radiation therapy, which uses an imaging rate of 15.0 fps without prediction, it may be possible to reduce the imaging dose by half or more. CONCLUSIONS Motion prediction can effectively lower the frame rate and minimize the imaging dose in real-time image-guided radiation therapy.
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Affiliation(s)
- Kazuki Numakura
- Graduate School of Biomedical Science and Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
| | - Seishin Takao
- Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8638, Japan; Department of Medical Physics, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
| | - Taeko Matsuura
- Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8638, Japan; Department of Medical Physics, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
| | - Kouhei Yokokawa
- Department of Medical Physics, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
| | - Ye Chen
- Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8638, Japan; Department of Medical Physics, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
| | - Yusuke Uchinami
- Faculty of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan
| | - Hiroshi Taguchi
- Department of Radiation Oncology, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
| | - Norio Katoh
- Faculty of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan
| | - Hidefumi Aoyama
- Faculty of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan
| | - Satoshi Tomioka
- Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8638, Japan
| | - Naoki Miyamoto
- Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8638, Japan; Department of Medical Physics, Hokkaido University Hospital, North 14, West 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan.
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Podobnik G, Ibragimov B, Tappeiner E, Lee C, Kim JS, Mesbah Z, Modzelewski R, Ma Y, Yang F, Rudecki M, Wodziński M, Peterlin P, Strojan P, Vrtovec T. HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Radiother Oncol 2024; 198:110410. [PMID: 38917883 DOI: 10.1016/j.radonc.2024.110410] [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: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/15/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND AND PURPOSE To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. MATERIALS AND METHODS The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. RESULTS While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. CONCLUSION This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.
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Affiliation(s)
- Gašper Podobnik
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia.
| | - Bulat Ibragimov
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia; University of Copenhagen, Department of Computer Science, Universitetsparken 1, Copenhagen 2100, Denmark
| | - Elias Tappeiner
- UMIT Tirol - Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, Hall in Tirol 6060, Austria
| | - Chanwoong Lee
- Yonsei University, College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea; Yonsei Cancer Center, Department of RadiationOncology, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul 03722, South Korea
| | - Jin Sung Kim
- Yonsei University, College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea; Yonsei Cancer Center, Department of RadiationOncology, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul 03722, South Korea; Oncosoft Inc, 37 Myeongmul-gil, Seodaemun-gu, Seoul 03722, South Korea
| | - Zacharia Mesbah
- Henri Becquerel Cancer Center, 1 Rue d'Amiens, Rouen 76000, France; Siemens Healthineers, 6 Rue du Général Audran, CS20146, Courbevoie 92412, France
| | - Romain Modzelewski
- Henri Becquerel Cancer Center, 1 Rue d'Amiens, Rouen 76000, France; Litis UR 4108, 684 Av. de l'Université, Saint- Étienne-du-Rouvray 76800, France
| | - Yihao Ma
- Guizhou Medical University, School of Biology & Engineering, 9FW8+2P3, Ankang Avenue, Gui'an New Area, Guiyang, Guizhou Province 561113, China
| | - Fan Yang
- Guizhou Medical University, School of Biology & Engineering, 9FW8+2P3, Ankang Avenue, Gui'an New Area, Guiyang, Guizhou Province 561113, China
| | - Mikołaj Rudecki
- AGH University of Kraków, Department of Measurement and Electronicsal, Mickiewicza 30, Kraków 30-059, Poland
| | - Marek Wodziński
- AGH University of Kraków, Department of Measurement and Electronicsal, Mickiewicza 30, Kraków 30-059, Poland; University of Applied Sciences Western Switzerland, Information Systems Institute, Rue de la Plaine 2, Sierre 3960, Switzerland
| | - Primož Peterlin
- Institute of Oncology, Ljubljana, Zaloška cesta 2, Ljubljana 1000, Slovenia
| | - Primož Strojan
- Institute of Oncology, Ljubljana, Zaloška cesta 2, Ljubljana 1000, Slovenia
| | - Tomaž Vrtovec
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
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Wang X, Yang C, Liu Z, Zhang J, Xue C, Xing L, Zheng Y, Geng C, Yin X. R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement. Med Phys 2024; 51:6075-6089. [PMID: 38801342 DOI: 10.1002/mp.17183] [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: 01/10/2024] [Revised: 04/30/2024] [Accepted: 05/11/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453 and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Chang Yang
- College of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Ziqi Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Jushuo Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Chao Xue
- Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Lihong Xing
- Affiliated Hospital of Hebei University, Baoding, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding, China
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Salari E, Wang J, Wynne JF, Chang CW, Wu Y, Yang X. Artificial intelligence-based motion tracking in cancer radiotherapy: A review. J Appl Clin Med Phys 2024:e14500. [PMID: 39194360 DOI: 10.1002/acm2.14500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 07/13/2024] [Accepted: 07/27/2024] [Indexed: 08/29/2024] Open
Abstract
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.
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Affiliation(s)
- Elahheh Salari
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Jing Wang
- Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jacob Frank Wynne
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Yizhou Wu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
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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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Huang Y, Gomaa A, Höfler D, Schubert P, Gaipl U, Frey B, Fietkau R, Bert C, Putz F. Principles of artificial intelligence in radiooncology. Strahlenther Onkol 2024:10.1007/s00066-024-02272-0. [PMID: 39105746 DOI: 10.1007/s00066-024-02272-0] [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: 02/26/2024] [Accepted: 06/17/2024] [Indexed: 08/07/2024]
Abstract
PURPOSE In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology. METHODS In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively. RESULTS This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size. CONCLUSION This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.
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Affiliation(s)
- Yixing Huang
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany.
| | - Ahmed Gomaa
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Philipp Schubert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Udo Gaipl
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
- Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Benjamin Frey
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
- Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
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Renner A, Gulyas I, Buschmann M, Heilemann G, Knäusl B, Heilmann M, Widder J, Georg D, Trnková P. Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data. Phys Imaging Radiat Oncol 2024; 30:100594. [PMID: 38883146 PMCID: PMC11176922 DOI: 10.1016/j.phro.2024.100594] [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: 02/07/2024] [Revised: 05/17/2024] [Accepted: 05/25/2024] [Indexed: 06/18/2024] Open
Abstract
Background and purpose Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers. Material and methods Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm. Results Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms. Conclusion It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.
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Affiliation(s)
- Andreas Renner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ingo Gulyas
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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10
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Sui Z, Palaniappan P, Brenner J, Paganelli C, Kurz C, Landry G, Riboldi M. Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy. Med Phys 2024; 51:1899-1917. [PMID: 37665948 DOI: 10.1002/mp.16702] [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: 04/24/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. PURPOSE The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). METHODS An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. RESULTS Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. CONCLUSIONS Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.
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Affiliation(s)
- Zhuojie Sui
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Prasannakumar Palaniappan
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Jakob Brenner
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
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11
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Winter JD, Reddy V, Li W, Craig T, Raman S. Impact of technological advances in treatment planning, image guidance, and treatment delivery on target margin design for prostate cancer radiotherapy: an updated review. Br J Radiol 2024; 97:31-40. [PMID: 38263844 PMCID: PMC11027310 DOI: 10.1093/bjr/tqad041] [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: 05/07/2023] [Revised: 08/22/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024] Open
Abstract
Recent innovations in image guidance, treatment delivery, and adaptive radiotherapy (RT) have created a new paradigm for planning target volume (PTV) margin design for patients with prostate cancer. We performed a review of the recent literature on PTV margin selection and design for intact prostate RT, excluding post-operative RT, brachytherapy, and proton therapy. Our review describes the increased focus on prostate and seminal vesicles as heterogenous deforming structures with further emergence of intra-prostatic GTV boost and concurrent pelvic lymph node treatment. To capture recent innovations, we highlight the evolution in cone beam CT guidance, and increasing use of MRI for improved target delineation and image registration and supporting online adaptive RT. Moreover, we summarize new and evolving image-guidance treatment platforms as well as recent reports of novel immobilization strategies and motion tracking. Our report also captures recent implementations of artificial intelligence to support image guidance and adaptive RT. To characterize the clinical impact of PTV margin changes via model-based risk estimates and clinical trials, we highlight recent high impact reports. Our report focusses on topics in the context of PTV margins but also showcase studies attempting to move beyond the PTV margin recipes with robust optimization and probabilistic planning approaches. Although guidelines exist for target margins conventional using CT-based image guidance, further validation is required to understand the optimal margins for online adaptation either alone or combined with real-time motion compensation to minimize systematic and random uncertainties in the treatment of patients with prostate cancer.
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Affiliation(s)
- Jeff D Winter
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Varun Reddy
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
| | - Winnie Li
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Tim Craig
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
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12
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Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, Placidi L. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiother Oncol 2024; 190:109970. [PMID: 37898437 DOI: 10.1016/j.radonc.2023.109970] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
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Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Denis Page
- University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom
| | - Cristina Garibaldi
- IEO, Unit of Radiation Research, European Institute of Oncology IRCCS, Milan, Italy
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; 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
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Rob H N Tijssen
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
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13
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Lombardo E, Liu PZY, Waddington DEJ, Grover J, Whelan B, Wong E, Reiner M, Corradini S, Belka C, Riboldi M, Kurz C, Landry G, Keall PJ. Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac. Med Phys 2023; 50:7083-7092. [PMID: 37782077 DOI: 10.1002/mp.16770] [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: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. PURPOSE To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. METHODS Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. RESULTS The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. CONCLUSIONS This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.
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Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - David E J Waddington
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - James Grover
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Brendan Whelan
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Esther Wong
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael Reiner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
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14
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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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