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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.
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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.
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Caricato P, Trivellato S, Pellegrini R, Montanari G, Daniotti MC, Bordigoni B, Faccenda V, Panizza D, Meregalli S, Bonetto E, Voet P, Arcangeli S, De Ponti E. Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality. Discov Oncol 2023; 14:180. [PMID: 37775613 PMCID: PMC10541351 DOI: 10.1007/s12672-023-00800-5] [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: 05/20/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND To investigate the capability of a not-yet commercially available fully automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), to further improve the plan quality of an already-validated Wish List (WL) pushing on the organs-at-risk (OAR) sparing without compromising target coverage and plan delivery accuracy. MATERIAL AND METHODS Twenty-four mono-institutional consecutive cervical cancer Volumetric-Modulated Arc Therapy (VMAT) plans delivered between November 2019 and April 2022 (50 Gy/25 fractions) have been retrospectively selected. In mCycle the LO planning algorithm was combined with the a-priori multi-criterial optimization (MCO). Two versions of WL have been defined to reproduce manual plans (WL01), and to improve the OAR sparing without affecting minimum target coverage and plan delivery accuracy (WL02). Robust WLs have been tuned using a subset of 4 randomly selected patients. The remaining plans have been automatically re-planned by using the designed WLs. Manual plans (MP) and mCycle plans (mCP01 and mCP02) were compared in terms of dose distributions, complexity, delivery accuracy, and clinical acceptability. Two senior physicians independently performed a blind clinical evaluation, ranking the three competing plans. Furthermore, a previous defined global quality index has been used to gather into a single score the plan quality evaluation. RESULTS The WL tweaking requests 5 and 3 working days for the WL01 and the WL02, respectively. The re-planning took in both cases 3 working days. mCP01 best performed in terms of target coverage (PTV V95% (%): MP 98.0 [95.6-99.3], mCP01 99.2 [89.7-99.9], mCP02 96.9 [89.4-99.5]), while mCP02 showed a large OAR sparing improvement, especially in the rectum parameters (e.g., Rectum D50% (Gy): MP 41.7 [30.2-47.0], mCP01 40.3 [31.4-45.8], mCP02 32.6 [26.9-42.6]). An increase in plan complexity has been registered in mCPs without affecting plan delivery accuracy. In the blind comparisons, all automated plans were considered clinically acceptable, and mCPs were preferred over MP in 90% of cases. Globally, automated plans registered a plan quality score at least comparable to MP. CONCLUSIONS This study showed the flexibility of the Lexicographic approach in creating more demanding Wish Lists able to potentially minimize toxicities in RT plans.
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Affiliation(s)
- Paolo Caricato
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy.
- Department of Physics, University of Milan, Milan, Italy.
| | - Sara Trivellato
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | | | - Gianluca Montanari
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | - Martina Camilla Daniotti
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
- Department of Physics, University of Milan, Milan, Italy
| | - Bianca Bordigoni
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
- Department of Physics, University of Milano Bicocca, Milan, Italy
| | - Valeria Faccenda
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
- Department of Physics, University of Milan, Milan, Italy
| | - Denis Panizza
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
| | - Sofia Meregalli
- School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
- Department of Radiation Oncology, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | - Elisa Bonetto
- Department of Radiation Oncology, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | - Peter Voet
- Research Clinical Liaison, Elekta AB, Stockholm, Sweden
| | - Stefano Arcangeli
- School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
- Department of Radiation Oncology, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | - Elena De Ponti
- Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
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Yedekci Y, Gültekin M, Sari SY, Yildiz F. Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning. RADIATION AND ENVIRONMENTAL BIOPHYSICS 2023; 62:253-260. [PMID: 36869941 DOI: 10.1007/s00411-023-01019-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/18/2023] [Indexed: 05/18/2023]
Abstract
The aim of this study was to improve the protection of organs at risk (OARs), decrease the total planning time and maintain sufficient target doses using scripting endometrial cancer external beam radiation therapy (EBRT) planning. Computed tomography (CT) data of 14 endometrial cancer patients were included in this study. Manual and automatic planning with scripting were performed for each CT. Scripts were created in the RayStation™ (RaySearch Laboratories AB, Stockholm, Sweden) planning system using a Python code. In scripting, seven additional contours were automatically created to reduce the OAR doses. The scripted and manual plans were compared to each other in terms of planning time, dose-volume histogram (DVH) parameters, and total monitor unit (MU) values. While the mean total planning time for manual planning was 368 ± 8 s, it was only 55 ± 2 s for the automatic planning with scripting (p < 0.001). The mean doses of OARs decreased with automatic planning (p < 0.001). In addition, the maximum doses (D2% and D1%) for bilateral femoral heads and the rectum were significantly reduced. It was observed that the total MU value increased from 1146 ± 126 (manual planning) to 1369 ± 95 (scripted planning). It is concluded that scripted planning has significant time and dosimetric advantages over manual planning for endometrial cancer EBRT planning.
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Affiliation(s)
- Yagiz Yedekci
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey.
| | - Melis Gültekin
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey
| | - Sezin Yuce Sari
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey
| | - Ferah Yildiz
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey
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Lou Z, Cheng C, Mao R, Li D, Tian L, Li B, Lei H, Ge H. A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system. Phys Med 2023; 109:102586. [PMID: 37062102 DOI: 10.1016/j.ejmp.2023.102586] [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: 08/25/2022] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 04/18/2023] Open
Abstract
PURPOSE To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios. METHODS An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared. RESULTS For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA. CONCLUSIONS This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.
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Affiliation(s)
- Zhaoyang Lou
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Chen Cheng
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Ronghu Mao
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Dingjie Li
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Lingling Tian
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Bing Li
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Hongchang Lei
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.
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Wang X, Li C, Li G, Zhao J, Bai S. A Script-Based Automatic Intensity Modulated Radiation Therapy Planning Method With Robust Optimization for Craniospinal Irradiation. Pract Radiat Oncol 2023; 13:e209-e215. [PMID: 36108963 DOI: 10.1016/j.prro.2022.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/16/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022]
Abstract
This report describes a script-based automatic planning method with robust optimization for craniospinal irradiation (CSI) to reduce sensitivity to field matching errors and increase planning efficiency. The data of 10 CSI patients with planning target volume (PTV) lengths between 49.8 and 85.0 cm were retrospectively studied. Robust intensity modulated radiation therapy plans with ±5-mm longitudinal position uncertainty were generated by the automatic planning script. A simple dose prediction model and a self-adjusting method were implied in the automatic plans. The plans' robustness against setup errors was evaluated by deliberately shifting the middle beamset ±5 mm in the superior-inferior direction. Manual and nonrobust plans were also created to evaluate the automatic robust plans' quality, efficiency, and robustness. There were no significant differences between the manual and automatic plans in terms of homogeneity index; conformity index; D1%, D2%, and D98% of PTV; and average doses of organs at risk. However, the D99% of the PTV in the automatic plans was slightly inferior to that in the manual plans. Compared with the manual plans, the automatic plans greatly increased efficiency, with a reduction in planning time of approximately 48%. When ±5-mm superior-inferior errors were introduced, the average deviations of the maximum dose D1% and minimum dose D99% to the spinal cord were 4.9% (±1.1%) and -3.4% (±1.3%), respectively. However, the corresponding values of the nonrobust plans were 20.0% (±5.4%) and -21.2 (±6.3%), respectively. The script-based automatic CSI planning method, combining robust optimization and a dose prediction model, efficiently created a good-quality plan that was robust to setup errors.
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Affiliation(s)
- Xuetao Wang
- Division of Radiation Physics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Changhu Li
- Division of Radiation Physics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Guangjun Li
- Division of Radiation Physics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jianling Zhao
- Division of Radiation Physics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sen Bai
- Division of Radiation Physics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Wang H, Bai X, Wang Y, Lu Y, Wang B. An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer. Front Oncol 2023; 13:1124458. [PMID: 36816929 PMCID: PMC9936236 DOI: 10.3389/fonc.2023.1124458] [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: 12/15/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Purpose To develop and evaluate an integrated solution for automatic intensity-modulated radiation therapy (IMRT) planning in non-small-cell lung cancer (NSCLC) cases. Methods A novel algorithm named as multi-objectives adjustment policy network (MOAPN) was proposed and trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS), based on the multi-agent deep reinforcement learning (DRL) scheme. Furthermore, a three-dimensional (3D) dose prediction module was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOAPN training. 114 previously treated NSCLC cases suitable for stereotactic body radiotherapy (SBRT) were selected from the clinical database. 87 cases were used for the model training, and the remaining 27 cases for evaluating the feasibility and effectiveness of MOAPN in automatic treatment planning. Results For all tested cases, the average number of adjustment steps was 21 ± 5.9 (mean ± 1 standard deviation). Compared with the MOAPN initial plans, the actual dose of chest wall, spinal cord, heart, lung (affected side), esophagus and bronchus in the MOAPN final plans reduced by 14.5%, 11.6%, 4.7%, 16.7%, 1.6% and 7.7%, respectively. The dose result of OARs in the MOAPN final plans was similar to those in the clinical plans. The complete automatic treatment plan for a new case was generated based on the integrated solution, with about 5-6 min. Conclusion We successfully developed an integrated solution for automatic treatment planning. Using the 3D dose prediction module to obtain the patient-specific optimization objectives, MOAPN formed action-value policy can simultaneously adjust multiple objectives to obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in different regions and treatment centers. Although improvement is warranted, this proof-of-concept study has demonstrated the feasibility of this integrated solution in automatic treatment planning based on the Eclipse TPS.
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Falahatpour Z, Geramifar P, Mahdavi SR, Abdollahi H, Salimi Y, Nikoofar A, Ay MR. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys 2022; 23:e13696. [PMID: 35699200 PMCID: PMC9512354 DOI: 10.1002/acm2.13696] [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: 11/13/2021] [Revised: 04/20/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.
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Affiliation(s)
- Zahra Falahatpour
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Rabie Mahdavi
- Department of Medical Physics, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Yazdan Salimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Nikoofar
- Department of Radiation Oncology, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
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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.
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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.
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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
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Huang C, Yang Y, Panjwani N, Boyd S, Xing L. Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface. IEEE Trans Biomed Eng 2021; 68:2907-2917. [PMID: 33523802 PMCID: PMC8526351 DOI: 10.1109/tbme.2021.3055822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front. METHODS Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM). RESULTS On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk. CONCLUSION Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners. SIGNIFICANCE Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.
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Huang C, Yang Y, Xing L. Fully automated noncoplanar radiation therapy treatment planning. Med Phys 2021; 48:7439-7449. [PMID: 34519064 DOI: 10.1002/mp.15223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To perform fully automated noncoplanar (NC) treatment planning, we propose a method called NC-POPS to produce NC plans using the Pareto optimal projection search (POPS) algorithm. METHODS NC radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. Our NC-POPS algorithm extends the original POPS algorithm to the NC setting with potential applications to both intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The proposed algorithm consists of two main parts: (1) NC beam angle optimization (BAO) and (2) fully automated inverse planning using the POPS algorithm. RESULTS We evaluate the performance of NC-POPS by comparing between various NC and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity. CONCLUSIONS Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of NC IMRT cases with the potential to substantially improve treatment planning workflow and plan quality.
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Affiliation(s)
- Charles Huang
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, Stanford, California, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, California, USA
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Wang H, Wang R, Liu J, Zhang J, Yao K, Yue H, Zhang Y, You J, Wu H. Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning. Br J Radiol 2021; 94:20210214. [PMID: 34111955 DOI: 10.1259/bjr.20210214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE To develop and evaluate a practical automatic treatment planning method for intensity-modulated radiation therapy (IMRT) in cervical cancer cases. METHODS A novel algorithm named as Optimization Objectives Tree Search Algorithm (OOTSA) was proposed to emulate the planning optimization process and achieve a progressively improving IMRT plan, based on the Eclipse Scripting Application Programming Interface (ESAPI). 30 previously treated cervical cancer cases were selected from the clinical database and comparison was made between the OOTSA-generated plans and clinical treated plans and RapidPlan-based (RP) plans. RESULTS In clinical evaluation, compared with plan scores of the clinical plans and the RP plans, 22 and 26 of the OOTSA plans were considered as clinically improved in terms of plan quality, respectively. The average conformity index (CI) for the PTV in the OOTSA plans was 0.86 ± 0.01 (mean ± 1 standard deviation), better than those in the RP plans (0.83 ± 0.02) and the clinical plans (0.71 ± 0.11). Compared with the clinical plans, the mean doses of femoral head, rectum, spinal cord and right kidney in the OOTSA plans were reduced by 2.34 ± 2.87 Gy, 1.67 ± 2.10 Gy, 4.12 ± 6.44 Gy and 1.15 ± 2.67 Gy. Compared with the RP plans, the mean doses of femoral head, spinal cord, right kidney and small intestine in the OOTSA plans were reduced by 3.31 ± 1.55 Gy, 4.25 ± 3.69 Gy, 1.54 ± 2.23 Gy and 3.33 ± 1.91 Gy, respectively. In the OOTSA plans, the mean dose of bladder was slightly increased, with 2.33 ± 2.55 Gy (versus clinical plans) and 1.37 ± 1.74 Gy (vs RP plans). The average elapsed time of OOTSA and clinical planning were 59.2 ± 3.47 min and 76.53 ± 5.19 min. CONCLUSION The plans created by OOTSA have been shown marginally better than the manual plans, especially in preserving OARs. In addition, the time of automatic treatment planning has shown a reduction compared to a manual planning process, and the variation of plan quality was greatly reduced. Although improvement on the algorithm is warranted, this proof-of-concept study has demonstrated that the proposed approach can be a practical solution for automatic planning. ADVANCES IN KNOWLEDGE The proposed method is novel in the emulation strategy of the physicists' iterative operation during the planning process. Based on the existing optimizers, this method can be a simple yet effective solution for automated IMRT treatment planning.
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Affiliation(s)
- Hanlin Wang
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Ruoxi Wang
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Jiacheng Liu
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Jian Zhang
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Kaining Yao
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Haizhen Yue
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Yibao Zhang
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China.,Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Jing You
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China
| | - Hao Wu
- Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital &Institute, Beijing, China.,Institute of Medical Technology, Peking University Health Science Center, Beijing, China
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Li F, Wang L, Zhang Y, Feng W, Ju T, Liu Z, Wang Z, Du X. A Retrospective Study on Using a Novel Single Needle Cone Puncture Approach for the Iodine-125 Seed Brachytherapy in Treating Patients With Thoracic Malignancy. Front Oncol 2021; 11:640131. [PMID: 34136382 PMCID: PMC8200774 DOI: 10.3389/fonc.2021.640131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 05/06/2021] [Indexed: 12/21/2022] Open
Abstract
Background Patients with progressive thoracic malignancy characterized by large irregular tumors with necrosis and life-threatening symptoms lack effective treatments. We set out to develop a single needle cone puncture method for the Iodine-125 seed (SNCP-125I) brachytherapy, and aim to report the initial results. Methods 294 patients with advanced thoracic malignancy were treated with local SNCP-125I brachytherapy between March 2009 and July 2020, followed by thorough evaluation of clinical outcome, overall survival (OS), progression-free survival (PFS) and procedure-related complications after treatment. Results The overall response rate (ORR) among the treated patients was 81.0% (238/294). Life-threatening symptoms due to tumor oppression, hemoptysis and large irregular tumor with necrosis were successfully alleviated after the SNCP-125I treatment with a remission rate at 91% to 94%. The median OS and PFS were 13.6 months and 5.8 months, respectively. Procedure-related side effects including pneumothorax (32/294), blood-stained sputum (8/294), subcutaneous emphysema (10/294), puncture site bleeding (16/294) and chest pain (6/294) were observed. Patients who were able to follow with chemotherapy or immunotherapy experienced extended OS and PFS, as compared with patients who opted to receive hospice care (16.5 months Vs. 11.2 months). Further pathological and immunological analysis showed that SNCP-125I induced tumor lymphocytes infiltration and long-term tumor necrosis. Conclusion SNCP-125I brachytherapy effectively eliminates life-threatening symptoms due to local tumor oppression, hemoptysis and large irregular and necrotic tumors in patients with unresectable chest malignancy and significantly induces local tumor regression. SNCP-125I brachytherapy combines with chemotherapy significantly prolong OS and PFS compare with SNCP-125I brachytherapy alone.
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Affiliation(s)
- Fenge Li
- Department of Oncology, Tianjin Beichen Hospital, Tianjin, China.,Department of Melanoma Oncology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Liping Wang
- Department of Oncology, Weifang People's Hospital, Weifang, China
| | - Yixiang Zhang
- Pulmonary Medicine, Weifang People's Hospital, Weifang, China
| | - Weihong Feng
- Department of Oncology, Tianjin Beichen Hospital, Tianjin, China
| | - Tao Ju
- Department of Oncology, Tianjin Beichen Hospital, Tianjin, China
| | - Zaiping Liu
- Department of Pathology and Laboratory Medicine, IWK Women's and Children's Health Center, Halifax, NS, Canada
| | - Zhenglu Wang
- Pathology Department, Tianjin First Central Hospital, Tianjin, China
| | - Xueming Du
- Department of Oncology, Tianjin Beichen Hospital, Tianjin, China
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