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Li X, Liu Y, Zhao F, Yang F, Luo W. Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01296-3. [PMID: 39424665 DOI: 10.1007/s10278-024-01296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 07/12/2024] [Accepted: 08/13/2024] [Indexed: 10/21/2024]
Abstract
Radiotherapy is recognized as the major treatment of nasopharyngeal carcinoma. Rapid and accurate dose prediction can improve the efficiency of the treatment planning process and the quality of radiotherapy plans. Currently, deep learning-based methods have been widely applied to dose prediction for radiotherapy treatment planning. However, it is important to note that existing models based on Convolutional Neural Networks (CNN) often overlook long-distance information. Although some studies try to use Transformer to solve the problem, it lacks the ability of CNN to process the spatial information inherent in images. Therefore, we propose a novel CNN and Transformer hybrid dose prediction model. To enhance the transmission ability of features between CNN and Transformer, we design a hierarchical dense recurrent encoder with a channel attention mechanism. Additionally, we propose a progressive decoder that preserves richer texture information through layer-wise reconstruction of high-dimensional feature maps. The proposed model also introduces object-driven skip connections, which facilitate the flow of information between the encoder and decoder. Experiments are conducted on in-house datasets, and the results show that the proposed model is superior to baseline methods in most dosimetric criteria. In addition, the image analysis metrics including PSNR, SSIM, and NRMSE demonstrate that the proposed model is consistent with ground truth and produces promising visual effects compared to other advanced methods. The proposed method could be taken as a powerful clinical guidance tool for physicists, significantly enhancing the efficiency of radiotherapy planning. The source code is available at https://github.com/CDUTJ102/THCN-Net .
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Affiliation(s)
- Xiangchen Li
- College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China
| | - Yanhua Liu
- College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China.
| | - Feixiang Zhao
- College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China
| | - Feng Yang
- Sichuan Cancer Hospital, Chengdu, 610041, China
| | - Wang Luo
- College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China
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2
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Tan HS, Wang K, McBeth R. Deep evidential learning for radiotherapy dose prediction. Comput Biol Med 2024; 182:109172. [PMID: 39317056 DOI: 10.1016/j.compbiomed.2024.109172] [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: 07/02/2024] [Revised: 09/11/2024] [Accepted: 09/17/2024] [Indexed: 09/26/2024]
Abstract
BACKGROUND As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. METHOD Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. RESULTS We found that (i) epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii) the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii) relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. CONCLUSION Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. We have also demonstrated how this framework leads to uncertainty heatmaps that correlate strongly with model errors, and how it can be used to equip the predicted Dose-Volume-Histograms with confidence intervals.
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Affiliation(s)
- Hai Siong Tan
- Gryphon Center for Artificial Intelligence and Theoretical Sciences, Singapore; University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology, Philadelphia, USA.
| | | | - Rafe McBeth
- University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology, Philadelphia, USA
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Wang M, Pan Y, Zhang X, Yang R. Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy. Front Oncol 2024; 14:1433225. [PMID: 39351348 PMCID: PMC11439881 DOI: 10.3389/fonc.2024.1433225] [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: 05/15/2024] [Accepted: 08/26/2024] [Indexed: 10/04/2024] Open
Abstract
Purpose The 3D U-Net deep neural network structure is widely employed for dose prediction in radiotherapy. However, the attention to the network depth and its impact on the accuracy and robustness of dose prediction remains inadequate. Methods 92 cervical cancer patients who underwent Volumetric Modulated Arc Therapy (VMAT) are geometrically augmented to investigate the effects of network depth on dose prediction by training and testing three different 3D U-Net structures with depths of 3, 4, and 5. Results For planning target volume (PTV), the differences between predicted and true values of D98, D99, and Homogeneity were statistically 1.00 ± 0.23, 0.32 ± 0.72, and -0.02 ± 0.02 for the model with a depth of 5, respectively. Compared to the other two models, these parameters were also better. For most of the organs at risk, the mean and maximum differences between the predicted values and the true values for the model with a depth of 5 were better than for the other two models. Conclusions The results reveal that the network model with a depth of 5 exhibits superior performance, albeit at the expense of the longest training time and maximum computational memory in the three models. A small server with two NVIDIA GeForce RTX 3090 GPUs with 24 G of memory was employed for this training. For the 3D U-Net model with a depth of more than 5 cannot be supported due to insufficient training memory, the 3D U-Net neural network with a depth of 5 is the commonly used and optimal choice for small servers.
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Affiliation(s)
| | | | | | - Ruijie Yang
- Department of Radiation Oncology, Cancer Center, Peking University Third
Hospital, Beijing, China
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4
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Xie H, Zhang H, Chen Z, Tan T. Precision dose prediction for breast cancer patients undergoing IMRT: The Swin-UMamba-Channel Model. Comput Med Imaging Graph 2024; 116:102409. [PMID: 38878631 DOI: 10.1016/j.compmedimag.2024.102409] [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: 02/08/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 09/02/2024]
Abstract
BACKGROUND Radiation therapy is one of the crucial treatment modalities for cancer. An excellent radiation therapy plan relies heavily on an outstanding dose distribution map, which is traditionally generated through repeated trials and adjustments by experienced physicists. However, this process is both time-consuming and labor-intensive, and it comes with a degree of subjectivity. Now, with the powerful capabilities of deep learning, we are able to predict dose distribution maps more accurately, effectively overcoming these challenges. METHODS In this study, we propose a novel Swin-UMamba-Channel prediction model specifically designed for predicting the dose distribution of patients with left breast cancer undergoing radiotherapy after total mastectomy. This model integrates anatomical position information of organs and ray angle information, significantly enhancing prediction accuracy. Through iterative training of the generator (Swin-UMamba) and discriminator, the model can generate images that closely match the actual dose, assisting physicists in quickly creating DVH curves and shortening the treatment planning cycle. Our model exhibits excellent performance in terms of prediction accuracy, computational efficiency, and practicality, and its effectiveness has been further verified through comparative experiments with similar networks. RESULTS The results of the study indicate that our model can accurately predict the clinical dose of breast cancer patients undergoing intensity-modulated radiation therapy (IMRT). The predicted dose range is from 0 to 50 Gy, and compared with actual data, it shows a high accuracy with an average Dice similarity coefficient of 0.86. Specifically, the average dose change rate for the planning target volume ranges from 0.28 % to 1.515 %, while the average dose change rates for the right and left lungs are 2.113 % and 0.508 %, respectively. Notably, due to their small sizes, the heart and spinal cord exhibit relatively higher average dose change rates, reaching 3.208 % and 1.490 %, respectively. In comparison with similar dose studies, our model demonstrates superior performance. Additionally, our model possesses fewer parameters, lower computational complexity, and shorter processing time, further enhancing its practicality and efficiency. These findings provide strong evidence for the accuracy and reliability of our model in predicting doses, offering significant technical support for IMRT in breast cancer patients. CONCLUSION This study presents a novel Swin-UMamba-Channel dose prediction model, and its results demonstrate its precise prediction of clinical doses for the target area of left breast cancer patients undergoing total mastectomy and IMRT. These remarkable achievements provide valuable reference data for subsequent plan optimization and quality control, paving a new path for the application of deep learning in the field of radiation therapy.
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Affiliation(s)
- Hui Xie
- Faulty of Applied Sciences, Macao Polytechnic University, Macao 999078, PR China; Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou 423000, PR China
| | - Hua Zhang
- Beijing Linking Med Technology Co., Ltd, No.9, Fenghaodong 2C-5, Haidian, Beijing 100089, PR China
| | - Zijie Chen
- Shenying Medical Technology (Shenzhen) Co., Ltd, Shenzhen 518057, PR China
| | - Tao Tan
- Faulty of Applied Sciences, Macao Polytechnic University, Macao 999078, PR China.
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Wang E, Abdallah H, Snir J, Chong J, Palma DA, Mattonen SA, Lang P. Predicting the 3-Dimensional Dose Distribution of Multilesion Lung Stereotactic Ablative Radiation Therapy With Generative Adversarial Networks. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)03175-4. [PMID: 39154905 DOI: 10.1016/j.ijrobp.2024.07.2329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/06/2024] [Accepted: 07/29/2024] [Indexed: 08/20/2024]
Abstract
PURPOSE Because SABR therapy is being used to treat greater numbers of lung metastases, selecting the optimal dose and fractionation to balance local failure and treatment toxicity becomes increasingly challenging. Multilesion lung SABR therapy plans include spatially diverse lesions with heterogeneous prescriptions and interacting dose distributions. In this study, we developed and evaluated a generative adversarial network (GAN) to provide real-time dosimetry predictions for these complex cases. METHODS AND MATERIALS A GAN was trained to predict dosimetry on a data set of patients who received SABR therapy for lung lesions at a tertiary center. Model input included the planning computed tomography scan, the organs at risk (OARs) and target structures, and an initial estimate of exponential dose fall-off. Multilesion plans were split 80/20 for training and evaluation. Models were evaluated on voxel-voxel, clinical dose-volume histogram, and conformality metrics. An out-of-sample validation and analysis of model variance were performed. RESULTS There were 125 multilesion plans from 102 patients with 357 lesions. Patients were treated for 2 to 7 lesions, with 19 unique dose-fractionation schemes over 1 to 3 courses of treatment. The out-of-sample validation set contained an additional 90 plans from 80 patients. The mean absolute difference and gamma pass fraction between the predicted and true dosimetry was <3 Gy and >90% for all OARs. The absolute differences in lung V20 and CV14 were 1.40% ± 0.99% and 75.8 ± 42.0 cc, respectively. The ratios of predicted to true R50%, R100%, and D2cm were 1.00 ± 0.16, 0.96 ± 0.32, and 1.01 ± 0.36, respectively. The out-of-sample validation set maintained mean absolute difference and gamma pass fraction of <3 Gy and >90%, respectively for all OARs. The median standard deviation of variance in V20 and CV14 prediction was 0.49% and 22.2 cc, respectively. CONCLUSIONS A GAN for predicting the 3-D dosimetry of complex multilesion lung SABR therapy is presented. Rapid dosimetry prediction can be used to assess treatment feasibility and explore dosimetric differences between varying prescriptions.
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Affiliation(s)
- Edward Wang
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Hassan Abdallah
- Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Jonatan Snir
- Schulich School of Medicine and Dentistry, London, Ontario, Canada; London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada
| | - Jaron Chong
- Schulich School of Medicine and Dentistry, London, Ontario, Canada; Department of Medical Imaging, Western University, London, Ontario, Canada
| | - David A Palma
- Schulich School of Medicine and Dentistry, London, Ontario, Canada; London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada
| | - Sarah A Mattonen
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada
| | - Pencilla Lang
- Schulich School of Medicine and Dentistry, London, Ontario, Canada; London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada.
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Chen L, Sun H, Wang Z, Zhang T, Zhang H, Wang W, Sun X, Duan J, Gao Y, Zhao L. Deep learning architecture with shunted transformer and 3D deformable convolution for voxel-level dose prediction of head and neck tumors. Phys Eng Sci Med 2024:10.1007/s13246-024-01462-5. [PMID: 39101991 DOI: 10.1007/s13246-024-01462-5] [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: 03/14/2024] [Accepted: 07/15/2024] [Indexed: 08/06/2024]
Abstract
Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.
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Affiliation(s)
- Liting Chen
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Zhongfei Wang
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Te Zhang
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Hailang Zhang
- Ministry of Education Key Laboratory of Intelligent and Network Security, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, 710049, Shaanxi, China
| | - Wei Wang
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Xiaohuan Sun
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Jie Duan
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Yue Gao
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, No.169 Changle West Road, Xi'an, 710032, Shaanxi, China.
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7
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Wang N, Fan J, Xu Y, Yan L, Chen D, Wang W, Men K, Dai J, Liu Z. Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer. Phys Med 2024; 124:104492. [PMID: 39094213 DOI: 10.1016/j.ejmp.2024.104492] [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: 02/12/2024] [Revised: 07/12/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
PURPOSE The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer. METHODS A DL model was developed for predicting patient-specific doses, trained and validated on a dataset of 235 patients with diverse target volumes and prescriptions. The model was integrated into clinical workflow with DL-predicted objective functions. The automatic plans were retrospectively designed for additional 50 treated manual volumetric modulated arc therapy (VMAT) plans. A comparison was made between automatic and manual plans in terms of dosimetric indexes, monitor units (MUs) and planning time. Plan quality metric (PQM) encompassing these indexes was evaluated, with higher PQM values indicating superior plan quality. Qualitative evaluations of two plans were conducted by four reviewers. RESULTS The PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans (P = 0.75). Compared to manual plans, the targets coverage and homogeneity of automatic plans demonstrated no significant difference. Manual plans exhibited better sparing for lung in V5 (difference: 1.8 ± 4.2 %, P = 0.02), whereas automatic plans showed enhanced sparing for heart in V30 (difference: 1.4 ± 4.7 %, P = 0.02) and for spinal cord in Dmax (difference: 0.7 ± 4.7 Gy, P = 0.04). The planning time and MUs of automatic plans were significantly reduced by 70.5 ± 20.0 min and 97.4 ± 82.1. Automatic plans were deemed acceptable in 88 % of the reviews (176/200). CONCLUSIONS The DL-assisted approach for lung cancer notably decreased planning time and MUs, while demonstrating comparable or superior quality relative to manual plans. It has the potential to provide benefit to lung cancer patients.
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Affiliation(s)
- Ningyu Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Shanghai Clinical Research Center for Radiation Oncology, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.
| | - Yingjie Xu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Lingling Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wenqing Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Zhiqiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Das J, Nath J, Bhattacharyya M, Kalita AK. Fostering Transformation: Unleashing the Power of Artifical Intelligence and Machine Learning in the Field of Radiation Oncology. Indian J Otolaryngol Head Neck Surg 2024; 76:3750-3754. [PMID: 39130229 PMCID: PMC11306808 DOI: 10.1007/s12070-024-04658-z] [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: 01/10/2024] [Accepted: 03/21/2024] [Indexed: 08/13/2024] Open
Abstract
The article explores AI and ML's transformative potential in reshaping the radiation therapy landscape. The article navigates through the evolving field of radiation oncology, highlighting the constant influx of information facilitated by advanced imaging techniques. The technical scrutiny of AI's potential within radiation oncology is explored, contrasting definitions by Russell and Norvig with Goel's more insightful perspective. A detailed overview of the radiation therapy process, from diagnosis to follow-up, sets the stage for discussing the role of AI and ML. The utilities of AI in radiation oncology are dissected, emphasizing the reduction of clinical load through decision support systems, streamlined treatment planning, and the automated enhancement of radiation therapy. The article showcases various AI algorithms deployed in the workflow, their applications, and the promising results they offer. While acknowledging the challenges, including the opaque nature of AI and the critical need for clinical adoption, the article outlines criteria for evaluating AI tools in clinical settings. It stresses the importance of trust-building, transparency and overcoming challenges to harness AI's full potential in radiation oncology. In conclusion, the article advocates for a proactive integration of AI and ML, envisioning a future where these technologies empower radiation oncologists to enhance patient care, optimize workflows, and advance the field.
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Affiliation(s)
- Jahnabi Das
- Department of Radiation Oncology, Dr B Boorach Cancer Institute, Guwahati, 781016 Assam India
| | - Jyotiman Nath
- Department of Radiation Oncology, Dr B Boorach Cancer Institute, Guwahati, 781016 Assam India
| | - Mouchumee Bhattacharyya
- Department of Radiation Oncology, Dr B Boorach Cancer Institute, Guwahati, 781016 Assam India
| | - Apurba Kumar Kalita
- Department of Radiation Oncology, Dr B Boorach Cancer Institute, Guwahati, 781016 Assam India
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9
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Lagedamon V, Leni PE, Gschwind R. Deep learning applied to dose prediction in external radiation therapy: A narrative review. Cancer Radiother 2024; 28:402-414. [PMID: 39138047 DOI: 10.1016/j.canrad.2024.03.005] [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: 02/14/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 08/15/2024]
Abstract
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.
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Affiliation(s)
- V Lagedamon
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France
| | - P-E Leni
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France.
| | - R Gschwind
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France
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10
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Fu Q, Chen X, Liu Y, Zhang J, Xu Y, Yang X, Huang M, Men K, Dai J. Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning-based dose prediction. Front Oncol 2024; 14:1407016. [PMID: 39040460 PMCID: PMC11260613 DOI: 10.3389/fonc.2024.1407016] [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: 03/26/2024] [Accepted: 06/17/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose Difficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning-based dose prediction. Materials and methods A total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101-based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses. Results The redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (-3.02%, -1.71%, and -1.19 Gy, respectively) and rectum (-4.82%, -1.97%, and -4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112). Conclusion This study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.
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Affiliation(s)
- Qi Fu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Xinyuan Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
- School of Physics and Technology, Wuhan University, Wuhan, China
| | - Jingbo Zhang
- Department of Radiotherapy Technology, The Cancer and Tuberculosis Hospital, Jiamusi, China
| | - Yingjie Xu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Xi Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Manni Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
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Jiang C, Ji T, Qiao Q. Application and progress of artificial intelligence in radiation therapy dose prediction. Clin Transl Radiat Oncol 2024; 47:100792. [PMID: 38779524 PMCID: PMC11109740 DOI: 10.1016/j.ctro.2024.100792] [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: 04/23/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a time-consuming and labor-intensive process. In addition, due to the differences in knowledge and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) has been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority. In this paper, we will review the research of AI in dose prediction, focusing on the progress in deep learning (DL).
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Affiliation(s)
- Chen Jiang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Tianlong Ji
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Qiao Qiao
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
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12
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Duan Y, Wang J, Wu P, Shao Y, Chen H, Wang H, Cao H, Gu H, Feng A, Huang Y, Shen Z, Lin Y, Kong Q, Liu J, Li H, Fu X, Yang Z, Cai X, Xu Z. AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions. Int J Radiat Oncol Biol Phys 2024; 119:978-989. [PMID: 38159780 DOI: 10.1016/j.ijrobp.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 11/06/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy. METHODS AND MATERIALS We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits. RESULTS The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%. CONCLUSIONS The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.
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Affiliation(s)
- Yanhua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Jiyong Wang
- Shanghai Pulse Medical Technology Inc, Shanghai, China
| | - Puyu Wu
- Verisk Information Technology Ltd, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongbin Cao
- Department of Radiation Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengle Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aihui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ying Huang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Zhenjiong Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Lin
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Jun Liu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongxuan Li
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhangru Yang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xuwei Cai
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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13
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Chen M, Pang B, Zeng Y, Xu C, Chen J, Yang K, Chang Y, Yang Z. Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy. Phys Med Biol 2024; 69:115056. [PMID: 38718814 DOI: 10.1088/1361-6560/ad48f6] [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: 12/08/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024]
Abstract
Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.
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Affiliation(s)
- Mei Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Bo Pang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Yiling Zeng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Cheng Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Kunyu Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
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14
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Huang P, Shang J, Hu Z, Liu Z, Yan H. Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases. Front Oncol 2024; 14:1339126. [PMID: 38420019 PMCID: PMC10900235 DOI: 10.3389/fonc.2024.1339126] [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: 11/15/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Purpose Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan. In this study, a deep learning model is applied to the dose prediction of a single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases. Method A U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. The 3D dose distribution is then predicted with the input of the CT image and contours of regions of interest (ROIs). A total of 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs), including (D max and D mean) for OARs, (D 98, D 95, D 50, and D 2) for PTVs, homogeneity index, and conformity index. The similarity between the predicted and clinically approved plan dose distribution is also evaluated. Result For 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%. The mean absolute differences of D 98, D 95, D 50, and D2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0%, and 3.0%, respectively. The prediction accuracy of OARs for D max and D mean is within 3.2% and 1.2%, respectively. The average DSC ranges from 0.86 to 1 for all tested patients. Conclusion U-ResNet is viable to produce accurate dose distribution that is comparable to those of the clinically approved treatment plans. The predicted results can be used to improve current treatment planning design, plan quality, efficiency, etc.
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Affiliation(s)
| | | | | | - Zhiqiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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15
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Gheshlaghi T, Nabavi S, Shirzadikia S, Moghaddam ME, Rostampour N. A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy. Phys Med Biol 2024; 69:045010. [PMID: 38241717 DOI: 10.1088/1361-6560/ad209a] [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: 08/14/2023] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
Objective. Radiation therapy is one of the primary methods used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume while protecting the surrounding organs at risk (OARs). However, the traditional workflow used by dosimetrists to plan the treatment is time-consuming and subjective, requiring iterative adjustments based on their experience. Deep learning methods can be used to predict dose distribution maps to address these limitations.Approach. The study proposes a cascade model for OARs segmentation and dose distribution prediction. An encoder-decoder network has been developed for the segmentation task, in which the encoder consists of transformer blocks, and the decoder uses multi-scale convolutional blocks. Another cascade encoder-decoder network has been proposed for dose distribution prediction using a pyramid architecture. The proposed model has been evaluated using an in-house head and neck cancer dataset of 96 patients and OpenKBP, a public head and neck cancer dataset of 340 patients.Main results. The segmentation subnet achieved 0.79 and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the existing baselines. The dose distribution prediction subnet outperformed the winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and dose-volume histogram scores, respectively. Besides, the end-to-end model, including both subnets simultaneously, outperformed the related studies.Significance. The predicted dose maps showed good coincidence with ground-truth, with a superiority after linking with the auxiliary segmentation task. The proposed model outperformed state-of-the-art methods, especially in regions with low prescribed doses. The codes are available athttps://github.com/GhTara/Dose_Prediction.
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Affiliation(s)
- Tara Gheshlaghi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Shahabedin Nabavi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Samireh Shirzadikia
- Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Nima Rostampour
- Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
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16
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Teng L, Wang B, Xu X, Zhang J, Mei L, Feng Q, Shen D. Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy. Med Image Anal 2024; 92:103045. [PMID: 38071865 DOI: 10.1016/j.media.2023.103045] [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: 10/29/2022] [Revised: 10/12/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024]
Abstract
Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.
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Affiliation(s)
- Lin Teng
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Bin Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Xuanang Xu
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Lanzhuju Mei
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China.
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17
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Zou Z, Gong C, Zeng L, Guan Y, Huang B, Yu X, Liu Q, Zhang M. Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:60-71. [PMID: 38343215 DOI: 10.1007/s10278-023-00930-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 03/02/2024]
Abstract
Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.
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Affiliation(s)
- Zhongsheng Zou
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Changfei Gong
- Department of Radiation Oncology, 1st Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lingpeng Zeng
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Yu Guan
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Bin Huang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Xiuwen Yu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
| | - Minghui Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
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Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 PMCID: PMC11362966 DOI: 10.1016/j.ejca.2023.113504] [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: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
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Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
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19
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Tzanis E, Stratakis J, Myronakis M, Damilakis J. A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT. Phys Med 2024; 117:103195. [PMID: 38048731 DOI: 10.1016/j.ejmp.2023.103195] [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: 08/20/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023] Open
Abstract
PURPOSE To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Stratakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - Marios Myronakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece.
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20
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Gianoli C, De Bernardi E, Parodi K. "Under the hood": artificial intelligence in personalized radiotherapy. BJR Open 2024; 6:tzae017. [PMID: 39104573 PMCID: PMC11299549 DOI: 10.1093/bjro/tzae017] [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: 08/24/2023] [Revised: 05/10/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on 2 different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy. The second level is referred to as biology-driven workflow, explored in the research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A 2-fold role for AI is defined according to these 2 different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers that were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or multiomics, when complemented by clinical and biological parameters (ie, biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI's growing role in personalized radiotherapy.
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Affiliation(s)
- Chiara Gianoli
- Department of Experimental Physics – Medical Physics, Faculty for Physics of the Ludwig-Maximilians-Universität München (LMU Munich), Geschwister-Scholl-Platz 1, München, 80539, Germany
| | - Elisabetta De Bernardi
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milano, 20126, Italy
| | - Katia Parodi
- Department of Experimental Physics – Medical Physics, Faculty for Physics of the Ludwig-Maximilians-Universität München (LMU Munich), Geschwister-Scholl-Platz 1, München, 80539, Germany
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21
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Portik D, Clementel E, Krayenbühl J, Bakx N, Andratschke N, Hurkmans C. Knowledge-based versus deep learning based treatment planning for breast radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100539. [PMID: 38303923 PMCID: PMC10832493 DOI: 10.1016/j.phro.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
Background and Purpose To improve radiotherapy (RT) planning efficiency and plan quality, knowledge-based planning (KBP) and deep learning (DL) solutions have been developed. We aimed to make a direct comparison of these models for breast cancer planning using the same training, validation, and testing sets. Materials and Methods Two KBP models were trained and validated with 90 RT plans for left-sided breast cancer with 15 fractions of 2.6 Gy. The versions either used the full dataset (non-clean model) or a cleaned dataset (clean model), thus eliminating geometric and dosimetric outliers. Results were compared with a DL U-net model (previously trained and validated with the same 90 RT plans) and manually produced RT plans, for the same independent dataset of 15 patients. Clinically relevant dose volume histogram parameters were evaluated according to established consensus criteria. Results Both KBP models underestimated the mean heart and lung dose equally 0.4 Gy (0.3-1.1 Gy) and 1.4 Gy (1.1-2.8 Gy) compared to the clinical plans 0.8 Gy (0.5-1.8 Gy) and 1.7 Gy (1.3-3.2 Gy) while in the final calculations the mean lung dose was higher 1.9-2.0 Gy (1.5-3.5 Gy) for both KPB models. The U-Net model resulted in a mean planning target volume dose of 40.7 Gy (40.4-41.3 Gy), slightly higher than the clinical plans 40.5 Gy (40.1-41.0 Gy). Conclusions Only small differences were observed between the estimated and final dose calculation and the clinical results for both KPB models and the DL model. With a good set of breast plans, the data cleaning module is not needed and both KPB and DL models lead to clinically acceptable results.
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Affiliation(s)
- Daniel Portik
- European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, Brussels, Belgium
| | - Enrico Clementel
- European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, Brussels, Belgium
| | - Jérôme Krayenbühl
- Department of Radiation Oncology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Nienke Bakx
- Department of Radiation Oncology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands
- Department of Applied Physics and Department of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands
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22
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Wang Y, Piao Z, Gu H, Chen M, Zhang D, Zhu J. Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry. Technol Cancer Res Treat 2024; 23:15330338241256594. [PMID: 38808514 PMCID: PMC11190807 DOI: 10.1177/15330338241256594] [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: 01/12/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 05/30/2024] Open
Abstract
Purpose: Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. Methods: A target conformal plan (TCP) was created based on the maximum planning target volume (PTV). Input data included TCP dose distribution, images, target structures, and organ-at-risk (OAR) information. The role of target conformal plan dose (TCPD) was assessed by comparing the TCPD-CNN (with dose information) and NonTCPD-CNN models (without dose information) using statistical analyses with the ranked Wilcoxon test (P < .05 considered significant). Results: The TCPD-CNN model showed no statistical differences in predicted target indices, except for PTV60, where differences in the D98% indicator were < 0.5%. For OARs, there were no significant differences in predicted results, except for some small-volume or closely located OARs. On comparing TCPD-CNN and NonTCPD-CNN models, TCPD-CNN's dose-volume histograms closely resembled clinical plans with higher similarity index. Mean dose differences for target structures (predicted TCPD-CNN and NonTCPD-CNN results) were within 3% of the maximum prescription dose for both models. TCPD-CNN and NonTCPD-CNN outcomes were 67.9% and 54.2%, respectively. 3D gamma pass rates of the target structures and the entire body were higher in TCPD-CNN than in the NonTCPD-CNN models (P < .05). Additional evaluation on previously unseen volumetric modulated arc therapy plans revealed that average 3D gamma pass rates of the target structures were larger than 90%. Conclusions: This study presents a novel framework for dose distribution prediction using deep learning and multichannel input, specifically incorporating TCPD information, enhancing prediction accuracy for IMRT in NPC treatment.
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Affiliation(s)
- Yixuan Wang
- 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 510060, P. R. China
| | - Zun Piao
- 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 510060, P. R. China
| | - Huikuan Gu
- 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 510060, P. R. China
| | - Meining Chen
- 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 510060, P. R. China
| | - Dandan Zhang
- 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 510060, P. R. China
| | - Jinhan Zhu
- 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 510060, P. R. China
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23
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Cui J, Xiao J, Hou Y, Wu X, Zhou J, Peng X, Wang Y. Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation. Int J Neural Syst 2023; 33:2350057. [PMID: 37771298 DOI: 10.1142/s0129065723500570] [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] [Indexed: 09/30/2023]
Abstract
Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time and effort to acquire. For cancers of low-incidence, such as cervical cancer, it is often a luxury to collect an adequate amount of labeled data to train a well-performing deep learning (DL) model. To mitigate this problem, in this paper, we resort to the unsupervised domain adaptation (UDA) strategy to achieve accurate dose prediction for cervical cancer (target domain) by leveraging the well-labeled high-incidence rectal cancer (source domain). Specifically, we introduce the cross-attention mechanism to learn the domain-invariant features and develop a cross-attention transformer-based encoder to align the two different cancer domains. Meanwhile, to preserve the target-specific knowledge, we employ multiple domain classifiers to enforce the network to extract more discriminative target features. In addition, we employ two independent convolutional neural network (CNN) decoders to compensate for the lack of spatial inductive bias in the pure transformer and generate accurate dose maps for both domains. Furthermore, to enhance the performance, two additional losses, i.e. a knowledge distillation loss (KDL) and a domain classification loss (DCL), are incorporated to transfer the domain-invariant features while preserving domain-specific information. Experimental results on a rectal cancer dataset and a cervical cancer dataset have demonstrated that our method achieves the best quantitative results with [Formula: see text], [Formula: see text], and HI of 1.446, 1.231, and 0.082, respectively, and outperforms other methods in terms of qualitative assessment.
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Affiliation(s)
- Jiaqi Cui
- School of Computer Science, Sichuan University, Chengdu, P. R. China
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Yun Hou
- Agile and Intelligent Computing Key Laboratory, Southwest China Institute of Electronic Technology, Chengdu, P. R. China
| | - Xi Wu
- School of Computer Science, Chengdu University of Information Technology, P. R. China
| | - Jiliu Zhou
- School of Computer Science, Sichuan University, Chengdu, P. R. China
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, P. R. China
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24
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Gronberg MP, Jhingran A, Netherton TJ, Gay SS, Cardenas CE, Chung C, Fuentes D, Fuller CD, Howell RM, Khan M, Lim TY, Marquez B, Olanrewaju AM, Peterson CB, Vazquez I, Whitaker TJ, Wooten Z, Yang M, Court LE. Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers. Med Phys 2023; 50:6639-6648. [PMID: 37706560 PMCID: PMC10947338 DOI: 10.1002/mp.16735] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics ofD 1 % ${D}_{1{\mathrm{\% }}}$ andD 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.
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Affiliation(s)
- Mary P. Gronberg
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tucker J. Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Skylar S. Gay
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
| | - Christine Chung
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - David Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Clifton D. Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Rebecca M. Howell
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Meena Khan
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tze Yee Lim
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Barbara Marquez
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Adenike M. Olanrewaju
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Christine B. Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Ivan Vazquez
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Thomas J. Whitaker
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Zachary Wooten
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of StatisticsRice UniversityHoustonTexasUSA
| | - Ming Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
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Jiao Z, Peng X, Wang Y, Xiao J, Nie D, Wu X, Wang X, Zhou J, Shen D. TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification. Med Image Anal 2023; 89:102902. [PMID: 37482033 DOI: 10.1016/j.media.2023.102902] [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: 01/07/2022] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits. Nevertheless, these methods require certain auxiliary inputs besides CT images, such as segmentation masks of the tumor and organs at risk (OARs), which limits their prediction efficiency and application potential. To address this issue, we design a novel approach named as TransDose for dose distribution prediction that treats CT images as the unique input in this paper. Specifically, instead of inputting the segmentation masks to provide the prior anatomical information, we utilize a super-pixel-based graph convolutional network (GCN) to extract category-specific features, thereby compensating the network for the necessary anatomical knowledge. Besides, considering the strong continuous dependency between adjacent CT slices as well as adjacent dose maps, we embed the Transformer into the backbone, and make use of its superior ability of long-range sequence modeling to endow input features with inter-slice continuity message. To our knowledge, this is the first network that specially designed for the task of dose prediction from only CT images without ignoring necessary anatomical structure. Finally, we evaluate our model on two real datasets, and extensive experiments demonstrate the generalizability and advantages of our method.
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Affiliation(s)
- Zhengyang Jiao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, China.
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dong Nie
- Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Xi Wu
- School of Computer Science, Chengdu University of Information Technology, China
| | | | - Jiliu Zhou
- School of Computer Science, Sichuan University, Chengdu, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
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26
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Gu X, Strijbis VIJ, Slotman BJ, Dahele MR, Verbakel WFAR. Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data. Front Oncol 2023; 13:1251132. [PMID: 37829347 PMCID: PMC10565853 DOI: 10.3389/fonc.2023.1251132] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/25/2023] [Indexed: 10/14/2023] Open
Abstract
Purpose A three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigated whether dose distributions could be predicted without the need for fully segmented datasets. Materials and methods GANs were trained/validated/tested using 320/30/35 previously segmented CT datasets and treatment plans. The following input combinations were used to train and test the models: CT-scan only (C); CT+PTVboost/elective (CP); CT+PTVs+OARs+body structure (CPOB); PTVs+OARs+body structure (POB); PTVs+body structure (PB). Mean absolute errors (MAEs) for the predicted dose distribution and mean doses to individual OARs (individual salivary glands, individual swallowing structures) were analyzed. Results For the five models listed, MAEs were 7.3 Gy, 3.5 Gy, 3.4 Gy, 3.4 Gy, and 3.5 Gy, respectively, without significant differences among CP-CPOB, CP-POB, CP-PB, among CPOB-POB. Dose volume histograms showed that all four models that included PTV contours predicted dose distributions that had a high level of agreement with clinical treatment plans. The best model CPOB and the worst model PB (except model C) predicted mean dose to within ±3 Gy of the clinical dose, for 82.6%/88.6%/82.9% and 71.4%/67.1%/72.2% of all OARs, parotid glands (PG), and submandibular glands (SMG), respectively. The R2 values (0.17/0.96/0.97/0.95/0.95) of OAR mean doses for each model also indicated that except for model C, the predictions correlated highly with the clinical dose distributions. Interestingly model C could reasonably predict the dose in eight patients, but on average, it performed inadequately. Conclusion We demonstrated the influence of the CT scan, and PTV and OAR contours on dose prediction. Model CP was not statistically different from model CPOB and represents the minimum data statistically required to adequately predict the clinical dose distribution in a group of patients.
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Affiliation(s)
- Xiaojin Gu
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands
| | - Victor I. J. Strijbis
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands
| | - Ben J. Slotman
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands
| | - Max R. Dahele
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands
| | - Wilko F. A. R. Verbakel
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands
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27
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Poel R, Kamath AJ, Willmann J, Andratschke N, Ermiş E, Aebersold DM, Manser P, Reyes M. Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring. Cancers (Basel) 2023; 15:4226. [PMID: 37686501 PMCID: PMC10486555 DOI: 10.3390/cancers15174226] [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: 08/03/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process.
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Affiliation(s)
- Robert Poel
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
- ARTORG Center for Biomedical Research, University of Bern, CH-3010 Bern, Switzerland
| | - Amith J. Kamath
- ARTORG Center for Biomedical Research, University of Bern, CH-3010 Bern, Switzerland
| | - Jonas Willmann
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland
| | - Ekin Ermiş
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
| | - Daniel M. Aebersold
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
| | - Peter Manser
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
- Division of Medical Radiation Physics, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
| | - Mauricio Reyes
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
- ARTORG Center for Biomedical Research, University of Bern, CH-3010 Bern, Switzerland
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28
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Villa M, Nasr B, Benoit D, Padoy N, Visvikis D, Bert J. Fast dose calculation in x-ray guided interventions by using deep learning. Phys Med Biol 2023; 68:164001. [PMID: 37433326 DOI: 10.1088/1361-6560/ace678] [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: 01/12/2023] [Accepted: 07/11/2023] [Indexed: 07/13/2023]
Abstract
Objective.Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition. Furthermore, accurate organ dose estimation has not been proposed for these procedures. Monte Carlo simulation can accurately estimate the dose by recreating the irradiation process generated during the x-ray imaging, but at a high computation time, limiting an intra-operative application. This work presents a fast deep convolutional neural network trained with MC simulations for patient dose estimation during x-ray-guided interventions.Approach.We introduced a modified 3D U-Net that utilizes a patient's CT scan and the numerical values of imaging settings as input to produce a Monte Carlo dose map. To create a dataset of dose maps, we simulated the x-ray irradiation process for the abdominal region using a publicly available dataset of 82 patient CT scans. The simulation involved varying the angulation, position, and tube voltage of the x-ray source for each scan. We additionally conducted a clinical study during endovascular abdominal aortic repairs to validate the reliability of our Monte Carlo simulation dose maps. Dose measurements were taken at four specific anatomical points on the skin and compared to the corresponding simulated doses. The proposed network was trained using a 4-fold cross-validation approach with 65 patients, and evaluating the performance on the remaining 17 patients during testing.Main results.The clinical validation demonstrated a average error within the anatomical points of 5.1%. The network yielded test errors of 11.5 ± 4.6% and 6.2 ± 1.5% for peak and average skin doses, respectively. Furthermore, the mean errors for the abdominal region and pancreas doses were 5.0 ± 1.4% and 13.1 ± 2.7%, respectively.Significance.Our network can accurately predict a personalized 3D dose map considering the current imaging settings. A short computation time was achieved, making our approach a potential solution for dose monitoring and reporting commercial systems.
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Affiliation(s)
| | - Bahaa Nasr
- LaTIM, INSERM UMR1101, Brest, France
- Brest University Hospital, France
| | | | - Nicolas Padoy
- ICube, Strasbourg University, CNRS, Strasbourg, France
- IHU Strasbourg, France
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29
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Benzazon N, Colnot J, de Kermenguy F, Achkar S, de Vathaire F, Deutsch E, Robert C, Diallo I. Analytical models for external photon beam radiotherapy out-of-field dose calculation: a scoping review. Front Oncol 2023; 13:1197079. [PMID: 37228501 PMCID: PMC10203488 DOI: 10.3389/fonc.2023.1197079] [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: 03/30/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
A growing body of scientific evidence indicates that exposure to low dose ionizing radiation (< 2 Gy) is associated with a higher risk of developing radio-induced cancer. Additionally, it has been shown to have significant impacts on both innate and adaptive immune responses. As a result, the evaluation of the low doses inevitably delivered outside the treatment fields (out-of-field dose) in photon radiotherapy is a topic that is regaining interest at a pivotal moment in radiotherapy. In this work, we proposed a scoping review in order to identify evidence of strengths and limitations of available analytical models for out-of-field dose calculation in external photon beam radiotherapy for the purpose of implementation in clinical routine. Papers published between 1988 and 2022 proposing a novel analytical model that estimated at least one component of the out-of-field dose for photon external radiotherapy were included. Models focusing on electrons, protons and Monte-Carlo methods were excluded. The methodological quality and potential limitations of each model were analyzed to assess their generalizability. Twenty-one published papers were selected for analysis, of which 14 proposed multi-compartment models, demonstrating that research efforts are directed towards an increasingly detailed description of the underlying physical phenomena. Our synthesis revealed great inhomogeneities in practices, in particular in the acquisition of experimental data and the standardization of measurements, in the choice of metrics used for the evaluation of model performance and even in the definition of regions considered out-of-the-field, which makes quantitative comparisons impossible. We therefore propose to clarify some key concepts. The analytical methods do not seem to be easily suitable for massive use in clinical routine, due to the inevitable cumbersome nature of their implementation. Currently, there is no consensus on a mathematical formalism that comprehensively describes the out-of-field dose in external photon radiotherapy, partly due to the complex interactions between a large number of influencing factors. Out-of-field dose calculation models based on neural networks could be promising tools to overcome these limitations and thus favor a transfer to the clinic, but the lack of sufficiently large and heterogeneous data sets is the main obstacle.
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Affiliation(s)
- Nathan Benzazon
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Julie Colnot
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- THERYQ, PMB-Alcen, Peynier, France
| | - François de Kermenguy
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Samir Achkar
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Florent de Vathaire
- Unité Mixte de Recherche (UMR) 1018 Centre de Recherche en épidémiologie et Santé des Populations (CESP), Radiation Epidemiology Team, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Ibrahima Diallo
- Unité Mixte de Recherche (UMR) 1030 Radiothérapie Moléculaire et Innovation Thérapeutique, ImmunoRadAI, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
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Currie GM, Rohren EM. Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks. Semin Nucl Med 2023; 53:457-466. [PMID: 36379728 DOI: 10.1053/j.semnuclmed.2022.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/14/2022]
Abstract
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment with scope to adjust dosing for optimal target and non-target tissue doses is consistent with striving for improved the outcomes of precision medicine. The combination of artificial intelligence and production of digital twins could provide an avenue for an individualised therapy doses and enhanced outcomes in theranostics. While there are barriers to overcome, the maturity of individual technologies (i.e. radiation dosimetry, artificial intelligence, theranostics and digital twins) places these approaches within reach.
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Affiliation(s)
- Geoffrey M Currie
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX.
| | - Eric M Rohren
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Mashayekhi M, McBeth R, Nguyen D, Yen A, Trivedi Z, Moon D, Avkshtol V, Vo D, Sher D, Jiang S, Lin MH. Artificial Intelligence Guided Physician Directive Improves Head and Neck Planning Quality and Practice Uniformity: A Prospective Study. Clin Transl Radiat Oncol 2023; 40:100616. [PMID: 36968578 PMCID: PMC10034417 DOI: 10.1016/j.ctro.2023.100616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023] Open
Abstract
•AI dose predictor was fully integrated with treatment planning system and used as a physicain decision support tool to improve uniformity of practice.•Model was trained based on our standard of practice, but implemented at the time of expansion with 3 new physicians join the practice.•Phase 1 retrospective evaluation demonstrated the non-uniform practice among 3 MDs and only 52.9% frequency planner can achieve physicians' directives.•Significant improvement in practice uniformity of practice was observed after utilizing AI as DST and 80.4% frequency clinical plan can achieve AI-guided physician directives.
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Affiliation(s)
- Maryam Mashayekhi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rafe McBeth
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Allen Yen
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Dominic Moon
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Vlad Avkshtol
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Dat Vo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - David Sher
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Corresponding author at: 2280 Inwood Rd, Dallas, TX 75390, USA.
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Buchanan L, Hamdan S, Zhang Y, Chen X, Li XA. Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study. Front Oncol 2023; 13:939951. [PMID: 36741025 PMCID: PMC9889647 DOI: 10.3389/fonc.2023.939951] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Purpose Fast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART. Methods A conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics. Results The average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU. Conclusion We have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.
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Chen X, Zhu J, Yang B, Chen D, Men K, Dai J. Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning. Front Oncol 2023; 13:1041769. [PMID: 36925918 PMCID: PMC10012276 DOI: 10.3389/fonc.2023.1041769] [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: 09/11/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
Purpose Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance. Materials and methods One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation. Results The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model. Conclusions The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Can the use of knowledge-based planning systems improve stereotactic radiotherapy planning? A systematic review. JOURNAL OF RADIOTHERAPY IN PRACTICE 2023. [DOI: 10.1017/s1460396922000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Abstract
Introduction:
This study aimed to systematically review the literature to synthesise and summarise whether using knowledge-based planning (KBP) can improve the planning of stereotactic radiotherapy treatments.
Methods:
A systematic literature search was carried out using Medline, Scopus and Cochrane databases to evaluate the use of KBP planning in stereotactic radiotherapy. Three hundred twenty-five potential studies were identified and screened to find 25 relevant studies.
Results:
Twenty-five studies met the inclusion criteria. Where a commercial KBP was used, 72.7% of studies reported a quality improvement, and 45.5% reported a reduction in planning time. There is evidence that when used as a quality control tool, KBP can highlight stereotactic plans that need revision. In studies that use KBP as the starting point for radiotherapy planning optimisation, the radiotherapy plans generated are typically equal to or superior to those planned manually.
Conclusions:
There is evidence that KBP has the potential to improve the quality and speed of stereotactic radiotherapy planning. Further research is required to accurately quantify such systems’ quality improvements and time savings. Notably, there has been little research into their use for prostate, spinal or liver stereotactic radiotherapy, and research in these areas would be desirable. It is recommended that future studies use the ICRU 91 level 2 reporting format and that blinded physician review could add a qualitative assessment of KBP system performance.
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Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-75. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.
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Automation of pencil beam scanning proton treatment planning for intracranial tumours. Phys Med 2023; 105:102503. [PMID: 36529006 DOI: 10.1016/j.ejmp.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To evaluate the feasibility of comprehensive automation of an intra-cranial proton treatment planning. MATERIALS AND METHODS Class solution (CS) beam configuration selection allows the user to identify predefined beam configuration based on target localization; automatic CS (aCS) will then explore all the possible CS beam geometries. Ten patients, already used for the evaluation of the automatic selection of the beam configuration, have been also employed to training an algorithm based on the computation of a benchmark dose exploit automatic general planning solution (GPS) optimization with a wish list approach for the planning optimization. An independent cohort of ten patients has been then used for the evaluation step between the clinical and the GPS plan in terms of dosimetric quality of plans and the time needed to generate a plan. RESULTS The definition of a beam configuration requires on average 22 min (range 9-29 min). The average time for GPS plan generation is 18 min (range 7-26 min). Median dose differences (GPS-Manual) for each OAR constraints are: brainstem -1.60 Gy, left cochlea -1.22 Gy, right cochlea -1.42 Gy, left eye 0.55 Gy, right eye -2.33 Gy, optic chiasm -1.87 Gy, left optic nerve -4.45 Gy, right optic nerve -2.48 Gy and optic tract -0.31 Gy. Dosimetric CS and aCS plan evaluation shows a slightly worsening of the OARs values except for the optic tract and optic chiasm for both CS and aCS, where better results have been observed. CONCLUSION This study has shown the feasibility and implementation of the automatic planning system for intracranial tumors. The method developed in this work is ready to be implemented in a clinical workflow.
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Mentzel F, Kröninger K, Lerch M, Nackenhorst O, Rosenfeld A, Tsoi AC, Weingarten J, Hagenbuchner M, Guatelli S. Small beams, fast predictions: a comparison of machine learning dose prediction models for proton minibeam therapy. Med Phys 2022; 49:7791-7801. [PMID: 36309820 DOI: 10.1002/mp.16066] [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: 07/26/2022] [Revised: 09/10/2022] [Accepted: 10/04/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Dose calculations for novel radiotherapy cancer treatments such as proton minibeam radiation therapy is often done using full Monte Carlo (MC) simulations. As MC simulations can be very time consuming for this kind of application, deep learning models have been considered to accelerate dose estimation in cancer patients. PURPOSE This work systematically evaluates the dose prediction accuracy, speed and generalization performance of three selected state-of-the-art deep learning models for dose prediction applied to the proton minibeam therapy. The strengths and weaknesses of those models are thoroughly investigated, helping other researchers to decide on a viable algorithm for their own application. METHODS The following recently published models are compared: first, a 3D U-Net model trained as a regression network, second, a 3D U-Net trained as a generator of a generative adversarial network (GAN) and third, a dose transformer model which interprets the dose prediction as a sequence translation task. These models are trained to emulate the result of MC simulations. The dose depositions of a proton minibeam with a diameter of 800μm and an energy of 20-100 MeV inside a simple head phantom calculated by full Geant4 MC simulations are used as a case study for this comparison. The spatial resolution is 0.5 mm. Special attention is put on the evaluation of the generalization performance of the investigated models. RESULTS Dose predictions with all models are produced in the order of a second on a GPU, the 3D U-Net models being fastest with an average of 130 ms. An investigated 3D U-Net regression model is found to show the strongest performance with overall 61.0 % ± $\%\pm$ 0.5% of all voxels exhibiting a deviation in energy deposition prediction of less than 3% compared to full MC simulations with no spatial deviation allowed. The 3D U-Net models are observed to show better generalization performance for target geometry variations, while the transformer-based model shows better generalization with regard to the proton energy. CONCLUSIONS This paper reveals that (1) all studied deep learning models are significantly faster than non-machine learning approaches predicting the dose in the order of seconds compared to hours for MC, (2) all models provide reasonable accuracy, and (3) the regression-trained 3D U-Net provides the most accurate predictions.
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Affiliation(s)
- F Mentzel
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - K Kröninger
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - M Lerch
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - O Nackenhorst
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - A Rosenfeld
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - A C Tsoi
- School of Computing and Information Technology, University of Wollongong, Wollongong, New South Wales, Australia
| | - J Weingarten
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - M Hagenbuchner
- School of Computing and Information Technology, University of Wollongong, Wollongong, New South Wales, Australia
| | - S Guatelli
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
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Zhang G, Jiang Z, Zhu J, Wang L. Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results. Radiat Oncol 2022; 17:179. [PMID: 36372897 PMCID: PMC9655866 DOI: 10.1186/s13014-022-02155-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/04/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Gongsen Zhang
- Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Zejun Jiang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, No. 440, Jiyan Road, Huaiyin District, Jinan, Shandong, China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Linlin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, No. 440, Jiyan Road, Huaiyin District, Jinan, Shandong, China.
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Niu T, Tsui T, Zhao W. AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery. Semin Radiat Oncol 2022; 32:365-376. [DOI: 10.1016/j.semradonc.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Babier A, Mahmood R, Zhang B, Alves VGL, Barragán-Montero AM, Beaudry J, Cardenas CE, Chang Y, Chen Z, Chun J, Diaz K, Eraso HD, Faustmann E, Gaj S, Gay S, Gronberg M, Guo B, He J, Heilemann G, Hira S, Huang Y, Ji F, Jiang D, Giraldo JCJ, Lee H, Lian J, Liu S, Liu KC, Marrugo J, Miki K, Nakamura K, Netherton T, Nguyen D, Nourzadeh H, Osman AFI, Peng Z, Muñoz JDQ, Ramsl C, Rhee DJ, Rodriguez JD, Shan H, Siebers JV, Soomro MH, Sun K, Hoyos AU, Valderrama C, Verbeek R, Wang E, Willems S, Wu Q, Xu X, Yang S, Yuan L, Zhu S, Zimmermann L, Moore KL, Purdie TG, McNiven AL, Chan TCY. OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines. Phys Med Biol 2022; 67:10.1088/1361-6560/ac8044. [PMID: 36093921 PMCID: PMC10696540 DOI: 10.1088/1361-6560/ac8044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 07/11/2022] [Indexed: 11/12/2022]
Abstract
Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.
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Affiliation(s)
- Aaron Babier
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Rafid Mahmood
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Binghao Zhang
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Victor G L Alves
- Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America
| | | | - Joel Beaudry
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Yankui Chang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, People’s Republic of China
| | - Zijie Chen
- Shenying Medical Technology Co., Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kelly Diaz
- Department of Physics, National University of Colombia, Medellín, Colombia
| | - Harold David Eraso
- Department of Physics, National University of Colombia, Medellín, Colombia
| | - Erik Faustmann
- Atominstitut, Vienna University of Technology, Vienna, Austria
| | - Sibaji Gaj
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States of America
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Mary Gronberg
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Bingqi Guo
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States of America
| | - Junjun He
- Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Gerd Heilemann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Sanchit Hira
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Yuliang Huang
- Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China
| | - Fuxin Ji
- Department of Electrical Engineering and Automation, Anhui University, Hefei, People’s Republic of China
| | - Dashan Jiang
- Department of Electrical Engineering and Automation, Anhui University, Hefei, People’s Republic of China
| | | | - Hoyeon Lee
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
| | - Jun Lian
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Shuolin Liu
- Department of Electrical Engineering and Automation, Anhui University, Hefei, People’s Republic of China
| | - Keng-Chi Liu
- Department of Medical Imaging, Taiwan AI Labs, Taipei, Taiwan
| | - José Marrugo
- Department of Physics, National University of Colombia, Medellín, Colombia
| | - Kentaro Miki
- Department Of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kunio Nakamura
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States of America
| | - Tucker Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Hamidreza Nourzadeh
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States of America
| | | | - Zhao Peng
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, People’s Republic of China
| | | | - Christian Ramsl
- Atominstitut, Vienna University of Technology, Vienna, Austria
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | | | - Hongming Shan
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
| | - Jeffrey V Siebers
- Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America
| | - Mumtaz H Soomro
- Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America
| | - Kay Sun
- Studio Vodels, Atlanta, GA, United States of America
| | - Andrés Usuga Hoyos
- Department of Physics, National University of Colombia, Medellín, Colombia
| | - Carlos Valderrama
- Department of Physics, National University of Colombia, Medellín, Colombia
| | - Rob Verbeek
- Department Computer Science, Aalto University, Espoo, Finland
| | - Enpei Wang
- Shenying Medical Technology Co., Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Siri Willems
- Department of Electrical Engineering, KULeuven, Leuven, Belgium
| | - Qi Wu
- Department of Electrical Engineering and Automation, Anhui University, Hefei, People’s Republic of China
| | - Xuanang Xu
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - Sen Yang
- Tencent AI Lab, Shenzhen, Guangdong, People’s Republic of China
| | - Lulin Yuan
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, United States of America
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States of America
| | - Lukas Zimmermann
- Faculty of Health, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria
- Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria
| | - Kevin L Moore
- Department of Radiation Oncology, University of California, San Diego, La Jolla, CA, United States of America
| | - Thomas G Purdie
- Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Techna Institute for the Advancement of Technology for Health, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Andrea L McNiven
- Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Timothy C Y Chan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Techna Institute for the Advancement of Technology for Health, Toronto, ON, Canada
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Ni Y, Chen S, Hibbard L, Voet P. Fast VMAT planning for prostate radiotherapy: dosimetric validation of a deep learning-based initial segment generation method. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac80e5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 07/13/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. To develop and evaluate a deep learning based fast volumetric modulated arc therapy (VMAT) plan generation method for prostate radiotherapy. Approach. A customized 3D U-Net was trained and validated to predict initial segments at 90 evenly distributed control points of an arc, linked to our research treatment planning system (TPS) for segment shape optimization (SSO) and segment weight optimization (SWO). For 27 test patients, the VMAT plans generated based on the deep learning prediction (VMATDL) were compared with VMAT plans generated with a previously validated automated treatment planning method (VMATref). For all test cases, the deep learning prediction accuracy, plan dosimetric quality, and the planning efficiency were quantified and analyzed. Main results. For all 27 test cases, the resulting plans were clinically acceptable. The V
95% for the PTV2 was greater than 99%, and the V
107% was below 0.2%. Statistically significant difference in target coverage was not observed between the VMATref and VMATDL plans (P = 0.3243 > 0.05). The dose sparing effect to the OARs between the two groups of plans was similar. Small differences were only observed for the Dmean of rectum and anus. Compared to the VMATref, the VMATDL reduced 29.3% of the optimization time on average. Significance. A fully automated VMAT plan generation method may result in significant improvement in prostate treatment planning efficiency. Due to the clinically acceptable dosimetric quality and high efficiency, it could potentially be used for clinical planning application and real-time adaptive therapy application after further validation.
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Cui J, Jiao Z, Wei Z, Hu X, Wang Y, Xiao J, Peng X. CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network. Front Oncol 2022; 12:875661. [PMID: 35924164 PMCID: PMC9341484 DOI: 10.3389/fonc.2022.875661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically. Materials and Methods We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients. Results Experimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas). Conclusion The proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT
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Affiliation(s)
- Jiaqi Cui
- School of Computer Science, Sichuan University, Chengdu, China
| | - Zhengyang Jiao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Zhigong Wei
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolin Hu
- West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
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Kearney VP, Yansane AIM, Brandon RG, Vaderhobli R, Lin GH, Hekmatian H, Deng W, Joshi N, Bhandari H, Sadat AS, White JM. A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level. J Dent 2022; 123:104211. [PMID: 35760207 DOI: 10.1016/j.jdent.2022.104211] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/16/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022] Open
Abstract
OBJECTIVES Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy. METHODS Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05. RESULTS Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04 mm and 1.50 mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89. CONCLUSIONS This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images. CLINICAL SIGNIFICANCE Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard.
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Affiliation(s)
- Vasant P Kearney
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Alfa-Ibrahim M Yansane
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ryan G Brandon
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ram Vaderhobli
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Guo-Hao Lin
- Department of Orofacial Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Hamid Hekmatian
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Wenxiang Deng
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Neha Joshi
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Harsh Bhandari
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ali S Sadat
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Joel M White
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105.
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Wang W, Chang Y, Liu Y, Liang Z, Liao Y, Qin B, Liu X, Yang Z. Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer. Med Phys 2022; 49:5451-5463. [PMID: 35543109 DOI: 10.1002/mp.15702] [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: 02/07/2022] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a 5-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the Peak-MU model, this work develops the Multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and MC dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The Monte-Carlo dropout was also added to the network to quantify the uncertainty of the model. RESULTS Compared to the Peak-MU model, the Multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (CTV D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the Multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the Multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the PB analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yilin Liu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030-3722, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Zhikai Liang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yicheng Liao
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Bin Qin
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xu Liu
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
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Pastor-Serrano O, Perkó Z. Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy. Phys Med Biol 2022; 67. [PMID: 35447605 DOI: 10.1088/1361-6560/ac692e] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/21/2022] [Indexed: 11/12/2022]
Abstract
Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.
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Affiliation(s)
- Oscar Pastor-Serrano
- Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands
| | - Zoltán Perkó
- Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands
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Kaderka R, Liu KC, Liu L, VanderStraeten R, Liu TL, Lee KM, Tu YCE, MacEwan I, Simpson D, Urbanic J, Chang C. Toward automatic beam angle selection for pencil-beam scanning proton liver Treatments: A deep learning-based approach. Med Phys 2022; 49:4293-4304. [PMID: 35488864 DOI: 10.1002/mp.15676] [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: 01/09/2022] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning however poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods. PURPOSE A deep learning-based approach to automatic beam angle selection is proposed for proton pencil-beam scanning treatment planning of liver lesions. METHODS We cast beam-angle selection as a multi-label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance based regularization and multi-label circular-smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post-processing to improve the output of the model. Forty-nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI-selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge-based treatment planning. RESULTS For 7 of the 11 cases in the test set, AI-selected beam angles agreed with those chosen by human planners to within 20 degrees (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10 degrees of the human-selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI- and human-selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ-at-risk sparing. CONCLUSIONS This pilot study demonstrated the feasibility of a novel deep learning-based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human-selected beam angles. In tandem with auto-contouring and knowledge-based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Robert Kaderka
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,Department of Radiation Oncology, University of Miami, Miami, FL, 33136
| | | | - Lawrence Liu
- California Protons Cancer Therapy Center, San Diego, CA, 92121
| | | | | | | | | | - Iain MacEwan
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
| | - Daniel Simpson
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121
| | - James Urbanic
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
| | - Chang Chang
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
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Tzanis E, Damilakis J. A novel methodology to train and deploy a machine learning model for personalized dose assessment in head CT. Eur Radiol 2022; 32:6418-6426. [PMID: 35384458 DOI: 10.1007/s00330-022-08756-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/28/2022] [Accepted: 03/19/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To propose a machine learning-based methodology for the creation of radiation dose maps and the prediction of patient-specific organ/tissue doses associated with head CT examinations. METHODS CT data were collected retrospectively for 343 patients who underwent standard head CT examinations. Patient-specific Monte Carlo (MC) simulations were performed to determine the radiation dose distribution to patients' organs/tissues. The collected CT images and the MC-produced dose maps were processed and used for the training of the deep neural network (DNN) model. For the training and validation processes, data from 231 and 112 head CT examinations, respectively, were used. Furthermore, a software tool was developed to produce dose maps from head CT images using the trained DNN model and to automatically calculate the dose to the brain and cranial bones. RESULTS The mean (range) percentage differences between the doses predicted from the DNN model and those provided by MC simulations for the brain, eye lenses, and cranial bones were 4.5% (0-17.7%), 5.7% (0.2-19.0%), and 5.2% (0.1-18.9%), respectively. The graphical user interface of the software offers a user-friendly way for radiation dose/risk assessment. The implementation of the DNN allowed for a 97% reduction in the computational time needed for the dose estimations. CONCLUSIONS A novel methodology that allows users to develop a DNN model for patient-specific CT dose prediction was developed and implemented. The approach demonstrated herein allows accurate and fast radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be used in everyday clinical practice. KEY POINTS • The methodology presented herein allows fast and accurate radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be implemented in everyday clinical practice. • The scripts developed in the current study will allow users to train models for the acquisition protocols of their CT scanners, generate dose maps, estimate the doses to the brain and cranial bones, and estimate the lifetime attributable risk of radiation-induced brain cancer.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, 71003, Heraklion, Crete, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, 71003, Heraklion, Crete, Greece.
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Bedford JL, Hanson IM. A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry. Phys Imaging Radiat Oncol 2022; 22:36-43. [PMID: 35493850 PMCID: PMC9048084 DOI: 10.1016/j.phro.2022.03.004] [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: 12/21/2021] [Revised: 03/04/2022] [Accepted: 03/28/2022] [Indexed: 11/18/2022] Open
Abstract
Background and purpose Real-time portal dosimetry compares measured images with predicted images to detect delivery errors as the radiotherapy treatment proceeds. This work aimed to investigate the performance of a recurrent neural network for processing image metrics so as to detect delivery errors as early as possible in the treatment. Materials and methods Volumetric modulated arc therapy (VMAT) plans of six prostate patients were used to generate sequences of predicted portal images. Errors were introduced into the treatment plans and the modified plans were delivered to a water-equivalent phantom. Four different metrics were used to detect errors. These metrics were applied to a threshold-based method to detect the errors as soon as possible during the delivery, and also to a recurrent neural network consisting of four layers. A leave-two-out approach was used to set thresholds and train the neural network then test the resulting systems. Results When using a combination of metrics in conjunction with optimal thresholds, the median segment index at which the errors were detected was 107 out of 180. When using the neural network, the median segment index for error detection was 66 out of 180, with no false positives. The neural network reduced the rate of false negative results from 0.36 to 0.24. Conclusions The recurrent neural network allowed the detection of errors around 30% earlier than when using conventional threshold techniques. By appropriate training of the network, false positive alerts could be prevented, thereby avoiding unnecessary disruption to the patient workflow.
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Affiliation(s)
- James L. Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
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Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108324] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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