1
|
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).
Collapse
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
| |
Collapse
|
2
|
Wu Z, Jia X, Lu L, Xu C, Pang Y, Peng S, Liu M, Wu Y. Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024; 36:e209-e223. [PMID: 38631974 DOI: 10.1016/j.clon.2024.03.022] [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: 11/01/2023] [Revised: 02/22/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
AIMS Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. MATERIALS AND METHODS We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. RESULTS The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. CONCLUSION AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.
Collapse
Affiliation(s)
- Z Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China
| | - X Jia
- Department of Radiotherapy, The Ninth People's Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - L Lu
- Department of Radiotherapy, Tongling People's Hospital, Anhui, PR China
| | - C Xu
- Department of Radiotherapy, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, PR China
| | - Y Pang
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - S Peng
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - M Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China.
| | - Y Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China.
| |
Collapse
|
3
|
Moore LC, Ahern F, Li L, Kallis K, Kisling K, Cortes KG, Nwachukwu C, Rash D, Yashar CM, Mayadev J, Zou J, Vasconcelos N, Meyers SM. Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models. Med Phys 2024. [PMID: 38814165 DOI: 10.1002/mp.17230] [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: 11/16/2023] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. PURPOSE The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. METHODS Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. RESULTS Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. CONCLUSIONS 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.
Collapse
Affiliation(s)
- Lance C Moore
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Fritz Ahern
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Lingyi Li
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Karoline Kallis
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Kelly Kisling
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Katherina G Cortes
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Chika Nwachukwu
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Dominique Rash
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Catheryn M Yashar
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Jyoti Mayadev
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego and Moores Cancer Center, La Jolla, California, USA
| | - Nuno Vasconcelos
- Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Sandra M Meyers
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Gao Y, Kyun Park Y, Jia X. Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy. Phys Med Biol 2024; 69:115049. [PMID: 38744304 DOI: 10.1088/1361-6560/ad4b90] [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: 01/19/2024] [Accepted: 05/14/2024] [Indexed: 05/16/2024]
Abstract
Objective.Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning, autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints.Approach.We integrated VTP with a real clinical TPS to establish a fully automated planning workflow guided by VTP. This integration allowed direct model training and evaluation using the clinical TPS. We designed the VTP network structure to approach the decision-making process in RT planning in a hierarchical manner that mirrors human planners. The VTP network was trained via theQ-learning framework. To assess the effectiveness of VTP, we conducted a prospective evaluation in the 2023 Planning Challenge organized by the American Association of Medical Dosimetrists (AAMD). We extended our evaluation to include 20 clinical H&N cancer patients, comparing the plans generated by VTP against their clinical plans.Main results.In the prospective evaluation for the AAMD Planning Challenge, VTP achieved a plan score of 139.08 in the initial phase evaluating plan quality, and 15 min of planning time with the first place ranking in the adaptive phase competing for planning efficiency while meeting all plan quality requirements. For clinical cases, VTP-generated plans achieved an average VTP score of125.33±11.12, which outperformed the corresponding clinical plans with an average score of117.76±13.56.Significance.We successfully integrated VTP with the clinical TPS to achieve a fully automated treatment planning workflow. The compelling performance of VTP demonstrated its potential in automating H&N cancer RT planning.
Collapse
Affiliation(s)
- Yin Gao
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Yang Kyun Park
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| |
Collapse
|
6
|
Zhang B, Babier A, Ruschin M, Chan TCY. Knowledge-based planning for Gamma Knife. Med Phys 2024; 51:3207-3219. [PMID: 38598107 DOI: 10.1002/mp.17058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Current methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge-based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created for GK. PURPOSE To develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK. METHODS Data were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out-of-sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dose predictions. RESULTS Across all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 ± $\pm$ 0.158 and 3.356 ± $\pm$ 1.030 respectively, compared to 0.713 ± $\pm$ 0.124 and 3.452 ± $\pm$ 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans. CONCLUSIONS Plans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal human intervention.
Collapse
Affiliation(s)
- Binghao Zhang
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Aaron Babier
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Mark Ruschin
- Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Timothy C Y Chan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
7
|
Gao Y, Gonzalez Y, Nwachukwu C, Albuquerque K, Jia X. Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning. Phys Med Biol 2024; 69:095010. [PMID: 38537309 PMCID: PMC11023000 DOI: 10.1088/1361-6560/ad3880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/08/2024] [Accepted: 03/26/2024] [Indexed: 04/18/2024]
Abstract
Objective.Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).Approach.The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts organs at risk (OAR)D2ccand CTVD90%of the current fraction from the patient's current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patients anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.Main results.DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoidD2ccand CTVD90%with a relative error of 11.51% ± 6.92%, 8.23% ± 5.75%, 7.12% ± 6.00%, and 10.16% ± 10.42%, respectively. In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74.Significance.We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.
Collapse
Affiliation(s)
- Yin Gao
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Yesenia Gonzalez
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Chika Nwachukwu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Kevin Albuquerque
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| |
Collapse
|
8
|
Cai W, Ding S, Li H, Zhou X, Dou W, Zhou L, Song T, Li Y. Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma. Radiat Oncol 2024; 19:39. [PMID: 38509540 PMCID: PMC10956235 DOI: 10.1186/s13014-024-02401-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/09/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. METHODS A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. RESULTS The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05). CONCLUSION We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.
Collapse
Affiliation(s)
- Wenwen Cai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Shouliang Ding
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Huali Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Xuanru Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Wen Dou
- Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Ting Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Yongbao Li
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China.
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Zhang L, Holmes JM, Liu Z, Vora SA, Sio TT, Vargas CE, Yu NY, Keole SR, Schild SE, Bues M, Li S, Liu T, Shen J, Wong WW, Liu W. Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy. Med Phys 2024; 51:1484-1498. [PMID: 37748037 DOI: 10.1002/mp.16758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s. CONCLUSIONS An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.
Collapse
Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Jason M Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Zhengliang Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Carlos E Vargas
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sameer R Keole
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sheng Li
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| |
Collapse
|
12
|
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 2023:S0360-3016(23)08239-1. [PMID: 38159780 DOI: 10.1016/j.ijrobp.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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.
Collapse
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.
| |
Collapse
|
13
|
De Lamarliere MG, Lusque A, Khalifa JA, Esteyrie V, Chevreau C, Valentin T, Gangloff D, Meresse T, Courtot L, Rochaix P, Boulet B, Graulieres E, Ducassou A. Management of tumor volume changes during preoperative radiotherapy for extremity soft tissue sarcoma: a new strategy of adaptive radiotherapy. Radiol Oncol 2023; 57:507-515. [PMID: 38038424 PMCID: PMC10690743 DOI: 10.2478/raon-2023-0056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/25/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Using adaptive radiotherapy (ART), to determine objective clinical criteria that identify extremity soft tissue sarcoma (ESTS) patients requiring adaptation of their preoperative radiotherapy (RT) plan. PATIENTS AND METHODS We included 17 patients with a lower extremity ESTS treated between 2019 and 2021 with preoperative RT, using helicoidal intensity-modulated RT (IMRT) tomotherapy, before surgical resection. We collected clinical, tumor parameters and treatment data. Repositioning was ascertained by daily Megavoltage computed tomography (MVCT) imaging. Using the PreciseART technology we retrospectively manually delineated at least one MVCT for each patient per week and recorded volume and dosimetric parameters. A greater than 5% change between target volume and planned target volume (PTV) dosimetric coverage from the initial planning CT scan to at least one MVCT was defined as clinically significant. RESULTS All 17 patients experienced significant tumor volume changes during treatment; 7 tumors grew (41%) and 10 shrank (59%). Three patients (18%), all undifferentiated pleomorphic sarcomas (UPS) with increased volume changes, experienced significant reductions in tumor dose coverage. Seven patients required a plan adaptation, as determined by practical criteria applied in our departmental practice. Among these patients, only one ultimately experienced a significant change in PTV coverage. Three patients had a PTV decrease of coverage. Among them, 2 did not receive plan adaptation according our criteria. None of the patients with decreased tumor volumes had reduced target volume coverage. Monitoring volume variations by estimating gross tumor volume (GTV) on MVCT, in addition to axial and sagittal linear tumor dimensions, appeared to be most effective for detecting reductions in PTV coverage throughout treatment. CONCLUSIONS Variations in ESTS volume are evident during preoperative RT, but significant dosimetric variations are rare. Specific attention should be paid to grade 2-3 UPSs during the first 2 weeks of treatment. In the absence of dedicated software in routine clinical practice, monitoring of tumor volume changes by estimating GTV may represent a useful strategy for identifying patients whose treatment needs to be replanned.
Collapse
Affiliation(s)
- Marion Geneau De Lamarliere
- Department of Radiation Oncology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Amélie Lusque
- Statistics department, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Justine Attal Khalifa
- Department of Radiation Oncology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | | | - Christine Chevreau
- Department of Medical Oncology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Thibaud Valentin
- Department of Medical Oncology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Dimitri Gangloff
- Department of Surgery, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Thomas Meresse
- Department of Surgery, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Louis Courtot
- Department of Surgery, Pierre Paul Riquet Hospital, Toulouse, France
| | - Philippe Rochaix
- Department of Pathology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Bérénice Boulet
- Department of Imagery, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Eliane Graulieres
- Department of Engineering and Medical Physics, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse – Oncopole. Toulouse, France
| | - Anne Ducassou
- Department of Radiation Oncology, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| |
Collapse
|
14
|
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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [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.
Collapse
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
| |
Collapse
|
15
|
Sheng L, Zhuang L, Yang J, Zhang D, Chen Y, Zhang J, Wang S, Shan G, Du X, Bai X. Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients. BMC Cancer 2023; 23:988. [PMID: 37848844 PMCID: PMC10580570 DOI: 10.1186/s12885-023-11499-6] [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: 02/21/2023] [Accepted: 10/09/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP. METHODS A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models. RESULTS The deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009). CONCLUSION Based on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.
Collapse
Affiliation(s)
- Liming Sheng
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Lei Zhuang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jing Yang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Danhong Zhang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Ying Chen
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Jie Zhang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Shengye Wang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Guoping Shan
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Xianghui Du
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Xue Bai
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| |
Collapse
|
16
|
Osman AFI, Tamam NM, Yousif YAM. A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck. J Appl Clin Med Phys 2023; 24:e14015. [PMID: 37138549 PMCID: PMC10476994 DOI: 10.1002/acm2.14015] [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: 12/20/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/05/2023] Open
Abstract
PURPOSE In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. METHODS A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. RESULTS The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net. CONCLUSION All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.
Collapse
Affiliation(s)
| | - Nissren M. Tamam
- Department of PhysicsCollege of SciencePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia
| | - Yousif A. M. Yousif
- Department of Radiation OncologyNorth West Cancer Centre – Tamworth HospitalTamworthAustralia
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Zhang L, Holmes JM, Liu Z, Vora SA, Sio TT, Vargas CE, Yu NY, Keole SR, Schild SE, Bues M, Li S, Liu T, Shen J, Wong WW, Liu W. Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy. ARXIV 2023:arXiv:2305.18572v1. [PMID: 37396612 PMCID: PMC10312803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
PURPOSE To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s.
Collapse
Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jason M. Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - Sujay A. Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence T. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Carlos E. Vargas
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Sameer R. Keole
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Sheng Li
- Department of Data Science, University of Virginia, Charlottesville, VA 22903, USA
| | - Tianming Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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: 6] [Impact Index Per Article: 6.0] [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.
Collapse
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.
| |
Collapse
|
21
|
OuYang PY, Zhang BY, Guo JG, Liu JN, Li J, Peng QH, Yang SS, He Y, Liu ZQ, Zhao YN, Li A, Wu YS, Hu XF, Chen C, Han F, You KY, Xie FY. Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma. EClinicalMedicine 2023; 58:101930. [PMID: 37090437 PMCID: PMC10114519 DOI: 10.1016/j.eclinm.2023.101930] [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: 01/01/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 04/25/2023] Open
Abstract
Background Radiotherapy is the mainstay of treatment for nasopharyngeal carcinoma. Radiation-induced temporal lobe injury (TLI) can regress or resolve in the early phase, but it is irreversible at a later stage. However, no study has proposed a risk-based follow-up schedule for its early detection. Planning evaluation is difficult when dose-volume histogram (DVH) parameters are similar and optimization is terminated. Methods This multicenter retrospective study included 6065 patients between 2014 and 2018. A 3D ResNet-based deep learning model was developed in training and validation cohorts and independently tested using concordance index in internal and external test cohorts. Accordingly, the patients were stratified into risk groups, and the model-predicted risks were used to develop risk-based follow-up schedules. The schedule was compared with the Radiation Therapy Oncology Group (RTOG) recommendation (every 3 months during the first 2 years and every 6 months in 3-5 years). Additionally, the model was used to evaluate plans with similar DVH parameters. Findings Our model achieved concordance indexes of 0.831, 0.818, and 0.804, respectively, which outperformed conventional prediction models (all P < 0.001). The temporal lobes in all the cohorts were stratified into three groups with discrepant TLI-free survival. Personalized follow-up schedules developed for each risk group could detect TLI 1.9 months earlier than the RTOG recommendation. According to a higher median predicted 3-year TLI-free survival (99.25% vs. 99.15%, P < 0.001), the model identified a better plan than previous models. Interpretation The deep learning model predicted TLI more precisely. The model-determined risk-based follow-up schedule detected the TLI earlier. The planning evaluation was refined because the model identified a better plan with a lower risk of TLI. Funding The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), Medical Scientific Research Foundation of Guangdong Province (A2022367), and Guangzhou Science and Technology Program (2023A04J1788).
Collapse
Affiliation(s)
- Pu-Yun OuYang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Bao-Yu Zhang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Jian-Gui Guo
- Department of Radiation Oncology, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Jia-Ni Liu
- Department of Head and Neck Oncology, The Cancer Center of the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Jiajian Li
- CVTE Research, Guangzhou, Guangdong, China
| | - Qing-He Peng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shan-Shan Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yun He
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Zhi-Qiao Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Ya-Nan Zhao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Anwei Li
- CVTE Research, Guangzhou, Guangdong, China
| | - Yi-Shan Wu
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Xue-Feng Hu
- Department of Radiation Oncology, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Chen Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Fei Han
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Kai-Yun You
- Department of Radiation Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Fang-Yun Xie
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
- Corresponding author. Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, No. 651 Dongfeng East Road, Guangzhou, 510060, China.
| |
Collapse
|
22
|
Li F, Niu S, Han Y, Zhang Y, Dong Z, Zhu J. Multi-stage framework with difficulty-aware learning for progressive dose prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
23
|
Oh K, Gronberg MP, Netherton TJ, Sengupta B, Cardenas CE, Court LE, Ford EC. A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system. Phys Imaging Radiat Oncol 2023; 26:100440. [PMID: 37342210 PMCID: PMC10277917 DOI: 10.1016/j.phro.2023.100440] [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: 10/13/2022] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 06/22/2023] Open
Abstract
Background and purpose A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.
Collapse
Affiliation(s)
- Kyuhak Oh
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bishwambhar Sengupta
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama, Birmingham, AL 35233, USA
| | - Laurence E. Court
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eric C. Ford
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
| |
Collapse
|
24
|
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.
Collapse
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.
| |
Collapse
|
25
|
Koike Y, Takegawa H, Anetai Y, Ohira S, Nakamura S, Tanigawa N. Patient-specific three-dimensional dose distribution prediction via deep learning for prostate cancer therapy: Improvement with the structure loss. Phys Med 2023; 107:102544. [PMID: 36774846 DOI: 10.1016/j.ejmp.2023.102544] [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: 10/06/2022] [Revised: 01/18/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
PURPOSE Deep learning (DL)-based dose distribution prediction can potentially reduce the cost of inverse planning process. We developed and introduced a structure-focused loss (Lstruct) for 3D dose prediction to improve prediction accuracy. This study investigated the influence of Lstruct on DL-based dose prediction for patients with prostate cancer. The proposed Lstruct, which is similar in concept to dose-volume histogram (DVH)-based optimization in clinical practice, has the potential to provide more interpretable and accurate DL-based optimization. METHODS This study involved 104 patients who underwent prostate radiotherapy. We used 3D U-Net-based architecture to predict dose distributions from computed tomography and contours of the planning target volume and organs-at-risk. We trained two models using different loss functions: L2 loss and Lstruct. Predicted doses were compared in terms of dose-volume parameters and the Dice similarity coefficient of isodose volume. RESULTS DVH analysis showed that the Lstruct model had smaller errors from the ground truth than the L2 model. The Lstruct model achieved more consistent dose distributions than the L2 model, with errors close to zero. The isodose Dice score of the Lstruct model was greater than that of the L2 model by >20% of the prescribed dose. CONCLUSIONS We developed Lstruct using labels of inputted contours for DL-based dose prediction for prostate radiotherapy. Lstruct can be generalized to any DL architecture, thereby enhancing the dose prediction accuracy.
Collapse
Affiliation(s)
- Yuhei Koike
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan.
| | - Hideki Takegawa
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
| | - Yusuke Anetai
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Satoaki Nakamura
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
| | - Noboru Tanigawa
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
| |
Collapse
|
26
|
Zhang B, Babier A, Chan TCY, Ruschin M. 3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification. Phys Med 2023; 106:102533. [PMID: 36724551 DOI: 10.1016/j.ejmp.2023.102533] [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: 09/26/2022] [Revised: 12/19/2022] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
PURPOSE To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape. METHODS Data from 322 GK treatment plans was modified by isolating and cropping the contoured MRI and clinical dose distributions based on tumor location, then scaling the resulting tumor spaces to a standard size. An accompanying 3D tensor was created for each instance to account for tumor size. The modified dataset for 272 patients was used to train both a generative adversarial network (GAN-GK) and a 3D U-Net model (U-Net-GK). Unmodified data was used to train equivalent baseline models. All models were used to predict the dose distribution of 50 out-of-sample patients. Prediction accuracy was evaluated using gamma, with criteria of 4 %/2mm, 3 %/3mm, 3 %/1mm and 1 %/1mm. Prediction quality was assessed using coverage, selectivity, and conformity indices. RESULTS The predictions resulting from GAN-GK and U-Net-GK were similar to their clinical counterparts, with average gamma (4 %/2mm) passing rates of 84.9 ± 15.3 % and 83.1 ± 17.2 %, respectively. In contrast, the gamma passing rate of baseline models were significantly worse than their respective GK-specific models (p < 0.001) at all criterion levels. The quality of GK-specific predictions was also similar to that of clinical plans. CONCLUSION Deep learning models can use GK-specific data modification to predict 3D dose distributions for GKRS plans with a large range in size, shape, or number of targets. Standard deep learning models applied to unmodified GK data generated poorer predictions.
Collapse
Affiliation(s)
- Binghao Zhang
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.
| | - Aaron Babier
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Timothy C Y Chan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| |
Collapse
|
27
|
Gronberg MP, Beadle BM, Garden AS, Skinner H, Gay S, Netherton T, Cao W, Cardenas CE, Chung C, Fuentes DT, Fuller CD, Howell RM, Jhingran A, Lim TY, Marquez B, Mumme R, Olanrewaju AM, Peterson CB, Vazquez I, Whitaker TJ, Wooten Z, Yang M, Court LE. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans. Pract Radiat Oncol 2023; 13:e282-e291. [PMID: 36697347 DOI: 10.1016/j.prro.2022.12.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. CONCLUSIONS Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.
Collapse
Affiliation(s)
- Mary P Gronberg
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas.
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Adam S Garden
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Heath Skinner
- Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Tucker Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Wenhua Cao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Christine Chung
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David T Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Clifton D Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tze Yee Lim
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Barbara Marquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adenike M Olanrewaju
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine B Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ivan Vazquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Thomas J Whitaker
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Zachary Wooten
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Statistics, Rice University, Houston, Texas
| | - Ming Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| |
Collapse
|
28
|
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.
Collapse
|
29
|
Pera Ó, Martínez Á, Möhler C, Hamans B, Vega F, Barral F, Becerra N, Jimenez R, Fernandez-Velilla E, Quera J, Algara M. Clinical Validation of Siemens' Syngo.via Automatic Contouring System. Adv Radiat Oncol 2023; 8:101177. [PMID: 36865668 PMCID: PMC9972393 DOI: 10.1016/j.adro.2023.101177] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023] Open
Abstract
Purpose The manual delineation of organs at risk is a process that requires a great deal of time both for the technician and for the physician. Availability of validated software tools assisted by artificial intelligence would be of great benefit, as it would significantly improve the radiation therapy workflow, reducing the time required for segmentation. The purpose of this article is to validate the deep learning-based autocontouring solution integrated in syngo.via RT Image Suite VB40 (Siemens Healthineers, Forchheim, Germany). Methods and Materials For this purpose, we have used our own specific qualitative classification system, RANK, to evaluate more than 600 contours corresponding to 18 different automatically delineated organs at risk. Computed tomography data sets of 95 different patients were included: 30 patients with lung, 30 patients with breast, and 35 male patients with pelvic cancer. The automatically generated structures were reviewed in the Eclipse Contouring module independently by 3 observers: an expert physician, an expert technician, and a junior physician. Results There is a statistically significant difference between the Dice coefficient associated with RANK 4 compared with the coefficient associated with RANKs 2 and 3 (P < .001). In total, 64% of the evaluated structures received the maximum score, 4. Only 1% of the structures were classified with the lowest score, 1. The time savings for breast, thorax, and pelvis were 87.6%, 93.5%, and 82.2%, respectively. Conclusions Siemens' syngo.via RT Image Suite offers good autocontouring results and significant time savings.
Collapse
Affiliation(s)
- Óscar Pera
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain,Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain,Corresponding author: Óscar Pera, MSc
| | - Álvaro Martínez
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain
| | | | | | | | | | - Nuria Becerra
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain
| | - Rafael Jimenez
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain
| | - Enric Fernandez-Velilla
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain,Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Jaume Quera
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain,Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Manuel Algara
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain,Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain,Autonomous University of Barcelona, Barcelona, Spain
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/04/2022] [Indexed: 11/15/2022] Open
|
32
|
A plan verification platform for online adaptive proton therapy using deep learning-based Monte–Carlo denoising. Phys Med 2022; 103:18-25. [DOI: 10.1016/j.ejmp.2022.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
|
33
|
Nguyen D, Lin MH, Sher D, Lu W, Jia X, Jiang S. Advances in Automated Treatment Planning. Semin Radiat Oncol 2022; 32:343-350. [PMID: 36202437 PMCID: PMC9851906 DOI: 10.1016/j.semradonc.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.
Collapse
Affiliation(s)
- Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX.
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - David Sher
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Weiguo Lu
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Xun Jia
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| |
Collapse
|
34
|
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]
|
35
|
van Dijk RHW, Staut N, Wolfs CJA, Verhaegen F. A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/22/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline. Approach. A deep learning model is proposed that denoises low precision (∼106 simulated particles) dose distributions to produce high precision (109 simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well. Main results. The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average. Significance. This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.
Collapse
|
36
|
Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
Collapse
Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
| |
Collapse
|
37
|
A Survey on Deep Learning for Precision Oncology. Diagnostics (Basel) 2022; 12:diagnostics12061489. [PMID: 35741298 PMCID: PMC9222056 DOI: 10.3390/diagnostics12061489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
Collapse
|
38
|
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.
Collapse
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
| |
Collapse
|
39
|
Osman AFI, Tamam NM. Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer. J Appl Clin Med Phys 2022; 23:e13630. [PMID: 35533234 PMCID: PMC9278691 DOI: 10.1002/acm2.13630] [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: 01/17/2022] [Accepted: 04/20/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Deep learning-based knowledge-based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high-quality plans. This paper presents a novel KBP model using an attention-gating mechanism and a three-dimensional (3D) U-Net for intensity-modulated radiation therapy (IMRT) 3D dose distribution prediction in head-and-neck cancer. METHODS A total of 340 head-and-neck cancer plans, representing the OpenKBP-2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention-gated 3D U-Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean-squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U-Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning-based dose prediction models. RESULTS The proposed attention-gated 3D U-Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground-truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U-Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U-Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U-Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U-Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U-Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U-Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U-Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U-Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U-Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the D m a x ${D_{max}}$ (brainstem, spinal cord, and mandible) and D m e a n ${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground-truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground-truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real-time applications. CONCLUSIONS The attention-gated 3D U-Net model demonstrated a capability in predicting accurate 3D dose distributions for head-and-neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U-Net model, and it was also competitive to the performance of the best state-of-the-art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision-making before planning, quality assurance of planning, and guiding-automated planning for improved plan consistency, quality, and planning efficiency.
Collapse
Affiliation(s)
| | - Nissren M Tamam
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| |
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Shen G, Jin X, Sun C, Li Q. Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network. Front Public Health 2022; 10:813135. [PMID: 35493368 PMCID: PMC9051073 DOI: 10.3389/fpubh.2022.813135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Objective:Precise segmentation of human organs and anatomic structures (especially organs at risk, OARs) is the basis and prerequisite for the treatment planning of radiation therapy. In order to ensure rapid and accurate design of radiotherapy treatment planning, an automatic organ segmentation technique was investigated based on deep learning convolutional neural network.MethodA deep learning convolutional neural network (CNN) algorithm called BCDU-Net has been modified and developed further by us. Twenty two thousand CT images and the corresponding organ contours of 17 types delineated manually by experienced physicians from 329 patients were used to train and validate the algorithm. The CT images randomly selected were employed to test the modified BCDU-Net algorithm. The weight parameters of the algorithm model were acquired from the training of the convolutional neural network.ResultThe average Dice similarity coefficient (DSC) of the automatic segmentation and manual segmentation of the human organs of 17 types reached 0.8376, and the best coefficient reached up to 0.9676. It took 1.5–2 s and about 1 h to automatically segment the contours of an organ in an image of the CT dataset for a patient and the 17 organs for the CT dataset with the method developed by us, respectively.ConclusionThe modified deep neural network algorithm could be used to automatically segment human organs of 17 types quickly and accurately. The accuracy and speed of the method meet the requirements of its application in radiotherapy.
Collapse
Affiliation(s)
- Guosheng Shen
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaodong Jin
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chao Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qiang Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Qiang Li
| |
Collapse
|
42
|
Peng Y, Liu Y, Chen Z, Zhang G, Ma C, Xu S, Yin Y. Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection. Front Oncol 2022; 12:808580. [PMID: 35311133 PMCID: PMC8927290 DOI: 10.3389/fonc.2022.808580] [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: 11/03/2021] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models. Materials and Methods A total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”. Results The model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%). Conclusions A consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.
Collapse
Affiliation(s)
- Yiru Peng
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yaoying Liu
- School of Physics, Beihang University, Beijing, China
| | | | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, China
| | - Changsheng Ma
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shouping Xu
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yin
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| |
Collapse
|
43
|
Evaluation of auto-planning in VMAT for locally advanced nasopharyngeal carcinoma. Sci Rep 2022; 12:4167. [PMID: 35264614 PMCID: PMC8907235 DOI: 10.1038/s41598-022-07519-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 02/04/2022] [Indexed: 11/12/2022] Open
Abstract
The aim of this study is to demonstrate the feasibility of a commercially available Auto-Planning module for the radiation therapy treatment planning for locally advanced nasopharyngeal carcinoma (NPC). 22 patients with locally advanced NPC were included in this study. For each patient, volumetric modulated arc therapy (VMAT) plans were generated both manually by an experienced physicist and automatically by the Auto-Planning module. The dose distribution, dosimetric parameters, monitor units and planning time were compared between automatic plans (APs) and manual plans (MPs). Meanwhile, the overall stage of disease was factored into the evaluation. The target dose coverage of APs was comparable to that of MPs. For the organs at risk (OARs) except spinal cord, the dose parameters of APs were superior to that of MPs. The Dmax and V50 of brainstem were statistically lower by 1.0 Gy and 1.32% respectively, while the Dmax of optic nerves and chiasm were also lower in the APs (p < 0.05). The APs provided a similar or superior quality to MPs in most cases, except for several patients with stage IV disease. The dose differences for most OARs were similar between the two types of plans regardless of stage while the APs provided better brainstem sparing for patients with stage III and improved the sparing of the parotid glands for stage IV patients. The total monitor units and planning time were significantly reduced in the APs. Auto-Planning is feasible for the VMAT treatment planning for locally advanced NPC.
Collapse
|
44
|
Qilin Z, Peng B, Ang Q, Weijuan J, Ping J, Hongqing Z, Bin D, Ruijie Y. The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer. J Appl Clin Med Phys 2022; 23:e13583. [PMID: 35262273 PMCID: PMC9195039 DOI: 10.1002/acm2.13583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/23/2021] [Accepted: 02/19/2022] [Indexed: 11/09/2022] Open
Abstract
Purpose To develop a 3D‐Unet dose prediction model to predict the three‐dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization. Methods One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D‐Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (Dmean), the received dose of 2 cm3 (D2cc), the percentage volume of receiving 40 Gy dose of organs‐at‐risk (V40), planning target volume (PTV) D98%, and homogeneity index (HI), (d) dose–volume histograms (DVHs). Results The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference. Conclusion A 3D‐Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.
Collapse
Affiliation(s)
- Zhang Qilin
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Bao Peng
- Center for Data ScienceAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
| | - Qu Ang
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Jiang Weijuan
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Jiang Ping
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Zhuang Hongqing
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Dong Bin
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Yang Ruijie
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| |
Collapse
|
45
|
Mashayekhi M, Tapia IR, Balagopal A, Zhong X, Barkousaraie AS, McBeth R, Lin MH, Jiang S, Nguyen D. Site-agnostic 3D dose distribution prediction with deep learning neural networks. Med Phys 2022; 49:1391-1406. [PMID: 35037276 PMCID: PMC9870295 DOI: 10.1002/mp.15461] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 11/23/2021] [Accepted: 12/20/2021] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three-dimensional dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset. METHODS This study uses two separate datasets/treatment sites: data from patients with prostate cancer treated with intensity-modulated radiation therapy (source data), and data from patients with head-and-neck cancer treated with volumetric-modulated arc therapy (target data). We first developed a source model with 3D UNet architecture, trained from random initial weights on the source data. We evaluated the performance of this model on the source data. We then studied the generalizability of the model to the new target dataset via transfer learning. To do this, we built three more models, all with the same 3D UNet architecture: target model, adapted model, and combined model. The source and target models were trained on the source and target data from random initial weights, respectively. The adapted model fine-tuned the source model to the target domain by using the target data. Finally, the combined model was trained from random initial weights on a combined data pool consisting of both target and source datasets. We tested all four models on the target dataset and evaluated quantitative dose-volume histogram metrics for the planning target volume (PTV) and organs at risk (OARs). RESULTS When tested on the source treatment site, the source model accurately predicted the dose distributions with average (mean, max) absolute dose errors of (0.32%±0.14, 2.37%±0.93) (PTV) relative to the prescription dose, and highest mean dose error of 1.68%±0.76, and highest max dose error of 5.47%± 3.31 for femoral head right. The error in PTV dose coverage prediction is 3.21%±1.51 for D98 , 3.04%±1.69 for D95 , and 1.83%±1.01 for D02 . Averaging across all OARs, the source model predicted the OAR mean dose within 1.38% and the OAR max dose within 3.64%. For the target treatment site, the target model average (mean, max) absolute dose errors relative to the prescription dose for the PTV were (1.08%±0.95, 2.90%±1.35). Left cochlea had the highest mean and max dose errors of 5.37%±5.82 and 8.33%±8.88, respectively. The errors in PTV dose coverage prediction for D98 and D95 were 2.88%±1.59 and 2.55%±1.28, respectively. The target model can predict the OAR mean dose within 2.43% and the OAR max dose within 4.33% on average across all OARs. CONCLUSION We developed a site-agnostic model for three-dimensional dose prediction and tested its adaptability to a new target treatment site via transfer learning. Our proposed model can make accurate predictions with limited training data.
Collapse
Affiliation(s)
- Maryam Mashayekhi
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Itzel Ramirez Tapia
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Anjali Balagopal
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Xinran Zhong
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Azar Sadeghnejad Barkousaraie
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Rafe McBeth
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Steve Jiang
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
- Author to whom any correspondence should be addressed.
| |
Collapse
|
46
|
Yuan Z, Wang Y, Hu P, Zhang D, Yan B, Lu H, Zhang H, Yang Y. Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy. Med Phys 2022; 49:2631-2641. [PMID: 35157337 DOI: 10.1002/mp.15530] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Zengtai Yuan
- Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China
| | - Yuxiang Wang
- Hefei Ion Medical Center the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 231283 China
| | - Panpan Hu
- Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China
- Department of Radiation Oncology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 China
| | - Duoer Zhang
- Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China
| | - Bing Yan
- Department of Radiation Oncology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 China
| | - Hsiao‐Ming Lu
- Hefei Ion Medical Center the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 231283 China
| | - Hongyan Zhang
- Hefei Ion Medical Center the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 231283 China
- Department of Radiation Oncology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 China
| | - Yidong Yang
- Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China
- Department of Radiation Oncology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 China
| |
Collapse
|
47
|
Fu Y, Zhang H, Morris ED, Glide-Hurst CK, Pai S, Traverso A, Wee L, Hadzic I, Lønne PI, Shen C, Liu T, Yang X. Artificial Intelligence in Radiation Therapy. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:158-181. [PMID: 35992632 PMCID: PMC9385128 DOI: 10.1109/trpms.2021.3107454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
Collapse
Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Hao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Eric D. Morris
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Carri K. Glide-Hurst
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Suraj Pai
- Maastricht University Medical Centre, Netherlands
| | | | - Leonard Wee
- Maastricht University Medical Centre, Netherlands
| | | | - Per-Ivar Lønne
- Department of Medical Physics, Oslo University Hospital, PO Box 4953 Nydalen, 0424 Oslo, Norway
| | - Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
48
|
Ma M, Kidd E, Fahimian BP, Han B, Niedermayr TR, Hristov D, Xing L, Yang Y. Dose Prediction for Cervical Cancer Brachytherapy Using 3-D Deep Convolutional Neural Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3098507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
49
|
Adabi S, Tsen TC, Yuan Y. Predicting 3D dose distribution with scale attention network for prostate cancer radiotherapy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12034:1203417. [PMID: 36147747 PMCID: PMC9491520 DOI: 10.1117/12.2611769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The growing demand for radiation therapy to treat cancer has been directed to focus on improving treatment planning flow for patients. Accurate dose prediction, therefore, plays a prominent role in this regard. In this study, we propose a framework based on our newly developed scale attention networks (SA-Net) to attain voxel-wise dose prediction. Our network 's dynamic scale attention model incorporates low-level details with high-level semantics from feature maps at different scales. To achieve more accurate results, we used distance data between each local voxel and the organ surfaces instead of binary masks of organs at risk as well as CT image as input of the network. The proposed method is tested on prostate cancer treated with Volumetric Modulated Arc Therapy (VMAT), where the model was training with 120 cases and tested on 20 cases. The average dose difference between the predicted dose and the clinical planned dose was 0.94 Gy, which is equivalent to 2.1% as compared to the prescription dose of 45 Gy. We also compared the performance of SA-Net dose prediction framework with different input format, the signed distance map vs. binary mask and showed the signed distance map was a better format as input to the model training. These findings show that our deep learning-based strategy of dose prediction is effectively feasible for automating the treatment planning in prostate cancer radiography.
Collapse
Affiliation(s)
- Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, NYC, USA, 10029
| | - Tzu-Chi Tsen
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, NYC, USA, 10029
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, NYC, USA, 10029
| |
Collapse
|
50
|
Sun Z, Xia X, Fan J, Zhao J, Zhang K, Wang J, Hu W. A hybrid optimization strategy for deliverable Intensity-modulated radiotherapy plan generation using deep learning-based dose prediction. Med Phys 2022; 49:1344-1356. [PMID: 35043971 DOI: 10.1002/mp.15462] [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: 10/27/2021] [Revised: 12/08/2021] [Accepted: 12/22/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy (IMRT) including dose prediction via a deep learning and voxel-based optimization strategy. MATERIALS AND METHODS The dose distribution of patients was predicted using a U-Net-based deep learning network based on the patient's anatomy information. One hundred seventeen patients with nasopharyngeal cancer (NPC) and 200 patients with rectal cancer were enrolled in this study. For NPC cases, 94 cases were included in the training dataset, 13 in the validation dataset and 10 in the testing dataset. For rectal cancer cases, 172 cases were included in the training set, 18 in the validation set and 10 in the testing set. A voxel-based optimization strategy, "Voxel", was proposed to achieve treatment planning optimization by dividing body voxels into two parts: inside planning target volumes (PTVs) and outside PTVs. Fixed dose-volume objectives were attached to the total objective function to realize individualized planning intended as the "hybrid" optimizing strategy. Automatically generated plans were compared with clinically approved plans to evaluate clinical gains, according to dosimetric indices and dose-volume histograms (DVHs). RESULTS Similarities were found between the DVH of the predicted dose and clinical plan, although significant differences were found in some organs at risk (OARs). Better organ sparing and suboptimal PTV coverage were shown using the voxel strategy; however, the deviation in homogeneity indices (HI) and conformity indices (CI) of the PTV between automatically generated plans and manual plans were reduced by the hybrid strategy ((Manual plans)/(Voxel plans)/(Hybrid plans): HI of PTV70 (1.06/1.12/1.02), CI of PTV70 (0.79/0.58/0.76)). The optimization time for each patient was within 1 minute and included fluence map optimization, leaf sequencing and control point optimization. All the generated plans (voxel & hybrid strategy) could be delivered on uRT-linac 506c (United Imaging Health care, Shanghai, China). CONCLUSION Deliverable plans can be generated by incorporating a voxel-based optimization strategy into a commercial TPS. The hybrid optimization method shows the benefit and clinical feasibility in generating clinically acceptable plans. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Zihan Sun
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Xiang Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, 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 Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Jun Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Kang Zhang
- United Imaging Healthcare, Shanghai, 201807, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| |
Collapse
|