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Kang Z. Using dose volume histogram (DVH) predictions to improve the plan quality of helical tomotherapy (HT). Med Dosim 2024:S0958-3947(24)00026-8. [PMID: 38890058 DOI: 10.1016/j.meddos.2024.05.002] [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: 09/04/2023] [Revised: 03/20/2024] [Accepted: 05/11/2024] [Indexed: 06/20/2024]
Abstract
This study aimed to investigate whether the RapidPlan (RP) model configured by volumetric modulated arc therapy (VMAT) plans of nasopharyngeal carcinoma (NPC) could be used to assist in the optimization of HT plans and improve their quality. An RP model was trained using 100 clinically accepted VMAT plans of NPC patients. The predicted dose constraints of the VMAT trained RP model were used to reoptimize 25 consecutive clinically accepted HT plans (HT_clinical) and perform new VMAT plans based on the same computed topography (CT). The dosimetric quality of the reoptimized HT plans (HT_reoptimized), HT_clinical, and VMAT group were compared. The minimum dose encompassing 2% target (D2%), the minimum dose encompassing 98% target (D98%), homogeneity index (HI) and conformity index (CI) were similar for most targets between the HT_clinical and HT_reoptimized plans, although certain targets in the HT_reoptimized plans had higher D2% and HI and lower D98%. The HT_reoptimized plans outperformed the HT_clinical plans in the Dmax and D1cc of the spinal cord, V40Gy of the left temporal lobe, Dmean and V30Gy of the oral cavity, Dmean of the larynx and thyroid, and the differences were statistically significant. HT plans had higher CI and HI than VMAT plans. HT plans outperformed VMAT plans in the Dmax of the spinal cord and lenses, V30Gy of the oral cavity and parotids, and V40Gy of the temporal lobes, but underperformed in the Dmax and D1cc of the brainstem, D1cc of the spinal cord and Dmean of the oral cavity. The VMAT-based RP model can be used to assist in the planning of HT plans and improve the dosimetry quality of HT plans.
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Affiliation(s)
- Zheng Kang
- Department of Radiation Oncology, The First Affiliated Hospital of Xiamen University, No.55, Zhen Hai Road, Siming District, Xiamen City, Fujian Province, China; Xiamen Key Laboratory of Radiation Oncology, No.55, Zhen Hai Road, Siming District, Xiamen City, Fujian Province, China; Teaching Hospital of Fujian Medical University, Xiamen, China.
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Mangone L, Penna D, Marinelli F, Roncaglia F, Bisceglia I, Merli F, Ruffini A, Gamberi B, Tieghi A, Valli R, Albertazzi L, Iori M, Giorgi Rossi P, Vener C, Morabito F, Neri A, Luminari S. Incidence, mortality, and survival of hematological malignancies in Northern Italian patients: an update to 2020. Front Oncol 2023; 13:1182971. [PMID: 37534259 PMCID: PMC10391155 DOI: 10.3389/fonc.2023.1182971] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023] Open
Abstract
Background Hematological malignancies (HMs) represent a heterogeneous group of diseases with diverse etiology, pathogenesis, and prognosis. HMs' accurate registration by Cancer Registries (CRs) is hampered by the progressive de-hospitalization of patients and the transition to molecular rather than microscopic diagnosis. Material and methods A dedicated software capable of automatically identifying suspected HMs cases by combining several databases was adopted by Reggio Emilia Province CR (RE-CR). Besides pathological reports, hospital discharge archives, and mortality records, RE-CR retrieved information from general and biomolecular laboratories. Incidence, mortality, and 5-year relative survival (RS) reported according to age, sex, and 4 HMs' main categories, were noted. Results Overall, 7,578 HM cases were diagnosed from 1996 to 2020 by RE-CR. HMs were more common in males and older patients, except for Hodgkin Lymphoma and Follicular Lymphoma (FL). Incidence showed a significant increase for FL (annual percent change (APC)=3.0), Myeloproliferative Neoplasms (MPN) in the first period (APC=6.0) followed by a significant decrease (APC=-7.4), and Myelodysplastic Syndromes (APC=16.4) only in the first period. Over the years, a significant increase was observed in 5-year RS for Hodgkin -, Marginal Zone -, Follicular - and Diffuse Large B-cell-Lymphomas, MPN, and Acute Myeloid Leukemia. The availability of dedicated software made it possible to recover 80% of cases automatically: the remaining 20% required direct consultation of medical records. Conclusions The study emphasizes that HM registration needs to collect information from multiple sources. The digitalization of CRs is necessary to increase their efficiency.
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Affiliation(s)
- Lucia Mangone
- Epidemiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Domenico Penna
- PhD Program in Clinical and Experimental Medicine, University of Modena and, Reggio Emilia, Italy
| | | | | | - Isabella Bisceglia
- Epidemiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Francesco Merli
- Hematology Unit, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessia Ruffini
- Gruppo Amici Dell’Ematologia Foundation-GrADE, Reggio Emilia, Italy
| | - Barbara Gamberi
- Hematology Unit, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessia Tieghi
- Hematology Unit, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Riccardo Valli
- Pathology Unit, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Laura Albertazzi
- Laboratory of Clinical Chemistry, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Paolo Giorgi Rossi
- Epidemiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Claudia Vener
- Department of Oncology and Hemato-oncology, Università di Milano, Milano, Italy
| | | | - Antonino Neri
- Scientific Directorate, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Stefano Luminari
- Hematology Unit, Azienda USL- IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Chimomo Department, University of Modena and Reggio Emilia, Reggio Emilia, Italy
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Monticelli D, Castriconi R, Tudda A, Fodor A, Deantoni C, Gisella Di Muzio N, Mangili P, Del Vecchio A, Fiorino C, Broggi S. Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol. Phys Med 2023; 110:102606. [PMID: 37196603 DOI: 10.1016/j.ejmp.2023.102606] [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: 12/12/2022] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/19/2023] Open
Abstract
PURPOSE To extend the knowledge-based (KB) automatic planning approach to CyberKnife in the case of Stereotactic Body Radiation Therapy (SBRT) for prostate cancer. METHODS Seventy-two clinical plans of patients treated according to the RTOG0938 protocol (36.25 Gy/5fr) with CyberKnife were exported from the CyberKnife system to Eclipse to train a KB-model using the Rapid Plan tool. The KB approach provided dose-volume objectives for specific OARs only and not PTV. Bladder, rectum and femoral heads were considered in the model. The KB-model was successfully trained on 51 plans and then validated on 20 new patients. A KB-based template was tuned in the Precision system for both sequential optimization (SO) and VOLO optimization algorithms. Plans of the validation group were re-optimized (KB-TP) using both algorithms without any operator intervention and compared against the original plans (TP) in terms of OARs/PTV dose-volume parameters. Paired Wilcoxon signed-rank tests were performed to assess statistically significant differences (p < 0.05). RESULTS Regarding SO, automatic KB-TP plans were generally better than or equivalent to TP plans. PTVs V95% was slightly worse while OARs sparing for KB-TP was significantly improved. Regarding VOLO optimization, the PTVs coverage was significantly better for KB-TP while there was a limited worsening in the rectum. A significant improvement was observed in the bladder in the range of low-intermediate doses. CONCLUSIONS An extension of the KB optimization approach to the CyberKnife system has been successfully developed and validated in the case of SBRT prostate cancer.
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Affiliation(s)
- Davide Monticelli
- Università degli Studi di Milano, Milano, Italy; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Roberta Castriconi
- Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy.
| | - Alessia Tudda
- Università degli Studi di Milano, Milano, Italy; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Andrei Fodor
- Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Nadia Gisella Di Muzio
- Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Paola Mangili
- Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | | | - Claudio Fiorino
- Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy
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Jayarathna S, Shen X, Chen RC, Li HH, Guida K. The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT. J Appl Clin Med Phys 2023:e13940. [PMID: 36827178 DOI: 10.1002/acm2.13940] [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: 10/10/2022] [Revised: 12/21/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.
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Affiliation(s)
- Sandun Jayarathna
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinglei Shen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Ronald C Chen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - H Harold Li
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Kenny Guida
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
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RapidPlan models for prostate radiotherapy treatment planning with 10-MV photon beams. JOURNAL OF RADIOTHERAPY IN PRACTICE 2022. [DOI: 10.1017/s1460396922000267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
Introduction:
The RapidPlan is a radiotherapy planning tool that uses a dataset of approved plans to predict the dose distribution and automatically generates the dose–volume constraints for optimisation of the new plan. This study compares three strategies of model building for the treatment of prostate cancer with the 10-MV photon beam.
Methods:
Three models for prostate treatment were compared: Model 6X, Model10X and Model6Xrefined. Model6X is already used in our department and was trained on treatment plans based on the 6-MV photon beam. Model10X was trained on treatment plans based on the 10-MV photon beam and manually optimised by an experienced medical physicist. Finally, Model6Xrefined was trained on plans automatically created by the Model6X, but using the 10-MV photon beam. The three models were used to generate 25 new plans with the 10-MV photon beam.
Results:
Model10X generated plans with 2 Gy lower mean dose to bladder-PTV and rectum-PTV volumes and 8% lower V15Gy at bladder and rectum volumes, although the number of monitor units increased by 170 on average.
Conclusions:
The model trained on manually optimised plans generated plans with higher normal tissue sparing. However, model building is a time-consuming process, so a cost–benefit balance should be performed.
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Esposito PG, Castriconi R, Mangili P, Broggi S, Fodor A, Pasetti M, Tudda A, Di Muzio NG, del Vecchio A, Fiorino C. Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy. Phys Imaging Radiat Oncol 2022; 23:54-59. [PMID: 35814259 PMCID: PMC9256826 DOI: 10.1016/j.phro.2022.06.009] [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: 12/30/2021] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 12/01/2022] Open
Abstract
Background/Purpose Tomotherapy may deliver high-quality whole breast irradiation at static angles. The aim of this study was to implement Knowledge-Based (KB) automatic planning for left-sided whole breast using this modality. Materials/Methods Virtual volumetric plans were associated to the dose distributions of 69 Tomotherapy (TT) clinical plans of previously treated patients, aiming to train a KB-model using a commercial tool completely implemented in our treatment planning system. An individually optimized template based on the resulting KB-model was generated for automatic plan optimization. Thirty patients of the training set and ten new patients were considered for internal/external validation. Fully-automatic plans (KB-TT) were generated and compared using the same geometry/number of fields of the corresponding clinical plans. Results KB-TT plans were successfully generated in 26/30 and 10/10 patients of the internal/external validation sets; for 4 patients whose original plans used only two fields, the manual insertion of one/two fields before running the automatic template was sufficient to obtain acceptable plans. Concerning internal validation, planning target volume V95%/D1%/dose distribution standard deviation improved by 0.9%/0.4Gy/0.2Gy (p < 0.05) against clinical plans; Organs at risk mean doses were also slightly improved (p < 0.05) by 0.07/0.4/0.2/0.01 Gy for left lung/heart/right breast/right lung respectively. Similarly satisfactory results were replicated in the external validation set. The resulting treatment duration was 8 ± 1 min, consistent with our clinical experience. The active planner time per patient was 5–10 minutes. Conclusion Automatic TT left-sided breast KB-plans are comparable to or slightly better than clinical plans and can be obtained with limited planner time. The approach is currently under clinical implementation.
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Khalifa A, Winter J, Navarro I, McIntosh C, Purdie TG. Domain adaptation of automated treatment planning from computed tomography to magnetic resonance. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac72ec] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Machine learning (ML) based radiation treatment planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of magnetic resonance (MR) only treatment planning workflows, we sought to determine if an ML based treatment planning model, trained on computed tomography (CT) imaging, could be applied to MR through domain adaptation. Methods. In this study, MR and CT imaging was collected from 55 prostate cancer patients treated on an MR linear accelerator. ML based plans were generated for each patient on both CT and MR imaging using a commercially available model in RayStation 8B. The dose distributions and acceptance rates of MR and CT based plans were compared using institutional dose-volume evaluation criteria. The dosimetric differences between MR and CT plans were further decomposed into setup, cohort, and imaging domain components. Results. MR plans were highly acceptable, meeting 93.1% of all evaluation criteria compared to 96.3% of CT plans, with dose equivalence for all evaluation criteria except for the bladder wall, penile bulb, small and large bowel, and one rectum wall criteria (p < 0.05). Changing the input imaging modality (domain component) only accounted for about half of the dosimetric differences observed between MR and CT plans. Anatomical differences between the ML training set and the MR linac cohort (cohort component) were also a significant contributor. Significance. We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed. Future work should focus on combining this framework with atlas selection metrics to create an interpretable quality assurance QA framework for ML based treatment planning.
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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: 12] [Impact Index Per Article: 6.0] [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.
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Affiliation(s)
| | - Nissren M Tamam
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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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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Pallotta S, Marrazzo L, Calusi S, Castriconi R, Fiorino C, Loi G, Fiandra C. Implementation of automatic plan optimization in Italy: Status and perspectives. Phys Med 2021; 92:86-94. [PMID: 34875426 DOI: 10.1016/j.ejmp.2021.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/20/2021] [Accepted: 11/24/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE To investigate and report on the diffusion and clinical use of automated radiotherapy planning systems in Italy and to assess the perspectives of the community of Italian medical physicists involved in radiotherapy on the use of these tools. MATERIALS AND METHODS A survey of medical physicists (one per Institute) of 175 radiotherapy centers in Italy was conducted between February 21st and April 1st, 2021. The information collected included the institute's characteristics, plan activity, availability/use of automatic tools and related issues regarding satisfaction, criticisms, expectations, and perceived professional modifications. Responses were analysed, including the impact of a few variables such as the institute type and experience. RESULTS 125 of the centers (71%) answered the survey, with regional variability (range: 47%-100%); among these, 49% have a TPS with some automatic option. Clinical use of automatic planning is present in 33% of the centers, with 13% applying it in >50% of their plans. Among the 125 responding centres the most used systems are Pinnacle (16%), Raystation (9%) and Eclipse (4%). The majority of participants consider the use of automated techniques to be beneficial, while only 1% do not see any advantage; 83% of respondents see the possibility of enriching their professional role as a potential benefit, while 3% see potential threats. CONCLUSIONS Our survey shows that 49% of the responding centres have an automatic planning solution although clinically used in only 33% of the cases. Most physicists consider the use of automated techniques to be beneficial and show a prevalently positive attitude.
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Affiliation(s)
- Stefania Pallotta
- University of Florence, Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Florence, Italy; Medical Physics Unit, AOU Careggi, Florence, Italy.
| | | | - Silvia Calusi
- University of Florence, Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Florence, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Gianfranco Loi
- Medical Physics, AOU Maggiore della Carità, Novara, Italy
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Sheng Y, Zhang J, Ge Y, Li X, Wang W, Stephens H, Yin FF, Wu Q, Wu QJ. Artificial intelligence applications in intensity modulated radiation treatment planning: an overview. Quant Imaging Med Surg 2021; 11:4859-4880. [PMID: 34888195 PMCID: PMC8611458 DOI: 10.21037/qims-21-208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/02/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.
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Affiliation(s)
- Yang Sheng
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jiahan Zhang
- Department of Radiation Oncology, Emory University Hospital, Atlanta, GA, USA
| | - Yaorong Ge
- Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xinyi Li
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Wentao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Hunter Stephens
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Qiuwen Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Q. Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Xu Y, Brovold N, Cyriac J, Bossart E, Padgett K, Butkus M, Diwanj T, King A, Dal Pra A, Abramowitz M, Pollack A, Dogan N. Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy. Int J Part Ther 2021; 8:62-72. [PMID: 34722812 PMCID: PMC8489488 DOI: 10.14338/ijpt-20-00088.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/07/2021] [Indexed: 02/03/2023] Open
Abstract
Purpose To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results The resulting R 2 (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ2 test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (-1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (-0.67Gy ± 2.17Gy) and lower integral dose (-0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (-1.13 ± 2.34). Conclusions The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites.
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Affiliation(s)
- Yihang Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nellie Brovold
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Jonathan Cyriac
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Elizabeth Bossart
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kyle Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael Butkus
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tejan Diwanj
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adam King
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matt Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
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Xu Y, Cyriac J, De Ornelas M, Bossart E, Padgett K, Butkus M, Diwanji T, Samuels S, Samuels MA, Dogan N. Knowledge-Based Planning for Robustly Optimized Intensity-Modulated Proton Therapy of Head and Neck Cancer Patients. Front Oncol 2021; 11:737901. [PMID: 34737954 PMCID: PMC8561780 DOI: 10.3389/fonc.2021.737901] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/27/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To assess the performance of a proton-specific knowledge-based planning (KBP) model in the creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of advanced head and neck (HN) cancer patients. METHODS Seventy-three patients diagnosed with advanced HN cancer previously treated with volumetric modulated arc therapy (VMAT) were selected and replanned with robustly optimized IMPT. A proton-specific KBP model, RapidPlanPT (RPP), was generated using 53 patients (20 unilateral cases and 33 bilateral cases). The remaining 20 patients (10 unilateral and 10 bilateral cases) were used for model validation. The model was validated by comparing the target coverage and organ at risk (OAR) sparing in the RPP-generated IMPT plans with those in the expert plans. To account for the robustness of the plan, all uncertainty scenarios were included in the analysis. RESULTS All the RPP plans generated were clinically acceptable. For unilateral cases, RPP plans had higher CTV_primary V100 (1.59% ± 1.24%) but higher homogeneity index (HI) (0.7 ± 0.73) than had the expert plans. In addition, the RPP plans had better ipsilateral cochlea Dmean (-5.76 ± 6.11 Gy), with marginal to no significant difference between RPP plans and expert plans for all other OAR dosimetric indices. For the bilateral cases, the V100 for all clinical target volumes (CTVs) was higher for the RPP plans than for the expert plans, especially the CTV_primary V100 (5.08% ± 3.02%), with no significant difference in the HI. With respect to OAR sparing, RPP plans had a lower spinal cord Dmax (-5.74 ± 5.72 Gy), lower cochlea Dmean (left, -6.05 ± 4.33 Gy; right, -4.84 ± 4.66 Gy), lower left and right parotid V20Gy (left, -6.45% ± 5.32%; right, -6.92% ± 3.45%), and a lower integral dose (-0.19 ± 0.19 Gy). However, RPP plans increased the Dmax in the body outside of CTV (body-CTV) (1.2 ± 1.43 Gy), indicating a slightly higher hotspot produced by the RPP plans. CONCLUSION IMPT plans generated by a broad-scope RPP model have a quality that is, at minimum, comparable with, and at times superior to, that of the expert plans. The RPP plans demonstrated a greater robustness for CTV coverage and better sparing for several OARs.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
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Apaza Blanco OA, Almada MJ, Garcia Andino AA, Zunino S, Venencia D. Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer. J Med Phys 2021; 46:334-340. [PMID: 35261504 PMCID: PMC8853452 DOI: 10.4103/jmp.jmp_51_21] [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: 03/30/2021] [Revised: 07/19/2021] [Accepted: 07/21/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose: To create and to validate knowledge-based volumetric modulated arc therapy (VMAT) models for breast cancer treatments without lymph node irradiation. Materials and Methods: One hundred VMAT-based breast plans (manual plans [MP]) were selected to create two knowledge-based VMAT models (breast left and breast right) using RapidPlan™. The plans were generated on Eclipse v15.5 (Varian Medical Systems, Palo Alto, CA) with 6 MV of a Novalis Tx equipped with a high-resolution multileaf collimator. The models were verified based on goodness-of-fit statistics using the coefficients of determination (R2) and Chi-square (χ2), and the goodness-of-estimation statistics through the mean square error (MSE). Geometrical and dosimetrical constraints were identified and removed from the RP models using statistical evaluation metrics and plots. For validation, 20 plans that integrate the models and 20 plans that do not were reoptimized with RP (closed and opened validation). Dosimetrical parameters of interest were used to compare MP versus RP plans for the Heart, Homolateral_Lung, Contralateral_Lung, and Contralateral_Breast. Optimization planning time and user independency were also analyzed. Results: The most unfavorable results of R2 in both models for the organs at risk were as follows: for Contralateral_Lung 0.51 in RP right breast (RP_RB) and for Heart 0.60 in RP left breast (RP_LB). The most unfavorable results of χ2 test were: for Contralateral_Breast 1.02 in RP_RB and for Heart 1.03 in RP_LB. These goodness-of-fit results show that no overfitting occurred in either of the models. There were no unfavorable results of mean square error (MSE, all < 0.05) in any of the two models. These goodness-of-estimation results show that the models have good estimation power. For closed validation, significant differences were found in RP_RB for Homolateral_Lung (all P ≤ 0.001), and in the RP_LB differences were found for the heart (all P ≤ 0.04) and for Homolateral_Lung (all P ≤ 0.022). For open validation, no statistically significant differences were obtained in either of the models. RP models had little impact on reducing optimization planning times for expert planners; nevertheless, the result showed a 30% reduction time for beginner planners. The use of RP models generates high-quality plans, without differences from the planner experience. Conclusion: Two RP models for breast cancer treatment using VMAT were successfully implemented. The use of RP models for breast cancer reduces the optimization planning time and improves the efficiency of the treatment planning process while ensuring high-quality plans.
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Affiliation(s)
- Oscar Abel Apaza Blanco
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - María José Almada
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Albin Ariel Garcia Andino
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Silvia Zunino
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Daniel Venencia
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
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Castriconi R, Esposito PG, Tudda A, Mangili P, Broggi S, Fodor A, Deantoni CL, Longobardi B, Pasetti M, Perna L, Del Vecchio A, Di Muzio NG, Fiorino C. Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy. Front Oncol 2021; 11:712423. [PMID: 34504790 PMCID: PMC8423088 DOI: 10.3389/fonc.2021.712423] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/02/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose To implement Knowledge Based (KB) automatic planning for right and left-sided whole breast treatment through a new volumetric technique (ViTAT, Virtual Tangential-fields Arc Therapy) mimicking conventional tangential fields (TF) irradiation. Materials and Method A total of 193 clinical plans delivering TF with wedged or field-in-field beams were selected to train two KB-models for right(R) and left(L) sided breast cancer patients using the RapidPlan (RP) tool implemented in the Varian Eclipse system. Then, a template for ViTAT optimization, incorporating individual KB-optimized constraints, was interactively fine-tuned. ViTAT plans consisted of four arcs (6 MV) with start/stop angles consistent with the TF geometry variability within our population; the delivery was completely blocked along the arcs, apart from the first and last 20° of rotation for each arc. Optimized fine-tuned KB templates for automatic plan optimization were generated. Validation tests were performed on 60 new patients equally divided in R and L breast treatment: KB automatic ViTAT-plans (KB-ViTAT) were compared against the original TF plans in terms of OARs/PTVs dose-volume parameters. Wilcoxon-tests were used to assess the statistically significant differences. Results KB models were successfully generated for both L and R sides. Overall, 1(3%) and 7(23%) out of 30 automatic KB-ViTAT plans were unacceptable compared to TF for R and L side, respectively. After the manual refinement of the start/stop angles, KB-ViTAT plans well fitted TF-performances for these patients as well. PTV coverage was comparable, while PTV D1% was improved with KB-ViTAT by R:0.4/L:0.2 Gy (p < 0.05); ipsilateral OARs Dmean were similar with a slight (i.e., few % volume) improvement/worsening in the 15–35 Gy/2–15 Gy range, respectively. KB-ViTAT better spared contralateral OARs: Dmean of contralateral OARs was 0.1 Gy lower (p < 0.05); integral dose was R:5%/L:8% lower (p < 0.05) than TF. The overall time for the automatic plan optimization and final dose calculation was 12 ± 2 minutes. Conclusions Fully automatic KB-optimization of ViTAT can efficiently replace manually optimized TF planning for whole breast irradiation. This approach was clinically implemented in our institute and may be suggested as a large-scale strategy for efficiently replacing manual planning with large sparing of time, elimination of inter-planner variability and of, seldomly occurring, sub-optimal manual plans.
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Affiliation(s)
| | | | - Alessia Tudda
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Paola Mangili
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Andrei Fodor
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | | | | | | | - Lucia Perna
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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Dosimetric comparison of RapidPlan and manually optimised volumetric modulated arc therapy plans in prostate cancer. JOURNAL OF RADIOTHERAPY IN PRACTICE 2021. [DOI: 10.1017/s1460396920000345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractPurpose:The aim of this study was to evaluate whether RapidPlan (RP) could generate clinically acceptable prostate volumetric modulated arc therapy (VMAT) plans.Methods:The in-house RP model was used to generate VMAT plans for 50 previously treated prostate cancer patients, with no additional optimisation being performed. The VMAT plans that were generated using the RP model were compared with the patients’ previous, manually optimised clinical plans (MP), none of which had been used for the development of the in-house RP prostate model. Differences between RP and MP in planning target volume (PTV) doses, organs at risk (OAR) sparing, monitor units (MU) and planning time required to produce treatment plans were analysed. Assessment of PTV doses was based on the conformation number (CN), homogeneity index (HI), D2%, D99% and the mean dose of the PTV. The OAR doses evaluated were the rectal V50 Gy, V65 Gy, V70 Gy and the mean dose, the bladder V65 Gy, V70 Gy and the mean dose, and the mean dose to both femurs.Results:D99% and mean dose of the PTV were lower for RP than for MP (p = 0·006 and p = 0·040, respectively).V50 Gy, V65 Gy and the mean dose to rectum were lower in RP than in MP (p < 0·001). V65 Gy, V70 Gy and the mean dose to bladder were lower in RP than in MP (p < 0·001). RP had enhanced the sparing of both femurs (p < 0·001) and significantly reduced the planning time to less than 5% of the time taken with MP. MU in RP was significantly higher than MP by an average of 52·5 MU (p < 0·001) and 46 out of the 50 RP plans were approved by the radiation oncologist.Conclusion:This study has demonstrated that VMAT plans generated using an in-house RP prostate model in a single optimisation for prostate patients were clinically acceptable with comparable or better plan quality compared to MP. RP can add value and improve treatment planning efficiency in a high-throughput radiotherapy department through reduced plan optimisation time while maintaining consistency in the plan quality.
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Deep learning method for prediction of patient-specific dose distribution in breast cancer. Radiat Oncol 2021; 16:154. [PMID: 34404441 PMCID: PMC8369791 DOI: 10.1186/s13014-021-01864-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 07/19/2021] [Indexed: 11/10/2022] Open
Abstract
Background Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Methods Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. Results The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. Conclusions In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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Hirashima H, Nakamura M, Mukumoto N, Ashida R, Fujii K, Nakamura K, Nakajima A, Sakanaka K, Yoshimura M, Mizowaki T. Reducing variability among treatment machines using knowledge-based planning for head and neck, pancreatic, and rectal cancer. J Appl Clin Med Phys 2021; 22:245-254. [PMID: 34151503 PMCID: PMC8292706 DOI: 10.1002/acm2.13316] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose This study aimed to assess dosimetric indices of RapidPlan model‐based plans for different energies (6, 8, 10, and 15 MV; 6‐ and 10‐MV flattening filter‐free), multileaf collimator (MLC) types (Millennium 120, High Definition 120, dual‐layer MLC), and disease sites (head and neck, pancreatic, and rectal cancer) and compare these parameters with those of clinical plans. Methods RapidPlan models in the Eclipse version 15.6 were used with the data of 28, 42, and 20 patients with head and neck, pancreatic, and rectal cancer, respectively. RapidPlan models of head and neck, pancreatic, and rectal cancer were created for TrueBeam STx (High Definition 120) with 6 MV, TrueBeam STx with 10‐MV flattening filter‐free, and Clinac iX (Millennium 120) with 15 MV, respectively. The models were used to create volumetric‐modulated arc therapy plans for a 10‐patient test dataset using all energy and MLC types at all disease sites. The Holm test was used to compare multiple dosimetric indices in different treatment machines and energy types. Results The dosimetric indices for planning target volume and organs at risk in RapidPlan model‐based plans were comparable to those in the clinical plan. Furthermore, no dose difference was observed among the RapidPlan models. The variability among RapidPlan models was consistent regardless of the treatment machines, MLC types, and energy. Conclusions Dosimetric indices of RapidPlan model‐based plans appear to be comparable to the ones based on clinical plans regardless of energies, MLC types, and disease sites. The results suggest that the RapidPlan model can generate treatment plans independent of the type of treatment machine.
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Affiliation(s)
- Hideaki Hirashima
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Division of Medical Physics, Department of Information Technology and Medical Engineering, Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobutaka Mukumoto
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryo Ashida
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kota Fujii
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kiyonao Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Aya Nakajima
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Katsuyuki Sakanaka
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Michio Yoshimura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Costa E, Richir T, Robilliard M, Bragard C, Logerot C, Kirova Y, Fourquet A, De Marzi L. Assessment of a conventional volumetric-modulated arc therapy knowledge-based planning model applied to the new Halcyon© O-ring linac in locoregional breast cancer radiotherapy. Phys Med 2021; 86:32-43. [PMID: 34051551 DOI: 10.1016/j.ejmp.2021.05.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/31/2021] [Accepted: 05/13/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION The aim of this study was to evaluate the performance of a knowledge-based planning (KBP) model for breast cancer trained on plans performed on a conventional linac with 6 MV FF (flattening filter) beams and volumetric-modulated arc therapy (VMAT) for plans performed on the new jawless Halcyon© system with 6 MV FFF (flattening filter-free) beams. MATERIALS AND METHODS Based on the RapidPlan© (RP) KBP optimization engine, a DVH Estimation Model was first trained using 56 VMAT left-sided breast cancer treatment plans performed on a conventional linac, and validated on another 20 similar cases (without manual intervention). To determine the capacity of the model for Halcyon©, an additional cohort of 20 left-sided breast cancer plans was generated with RP and analyzed for both TrueBeam© and Halcyon© machines. Plan qualities between manual vs RP (followed by manual intervention) Halcyon© plans set were compared qualitatively by blinded review by radiation oncologists for 10 new independent plans. RESULTS Halcyon© plans generated with the VMAT model trained with conventional linac plans showed comparable target dose distribution compared to TrueBeam© plans. Organ sparingwas comparable between the 2 devices with a slight decrease in heart dose for Halcyon© plans. Nine out of ten automatically generated Halcyon© plans were preferentially chosen by the radiation oncologists over the manually generated Halcyon© plans. CONCLUSION A VMAT KBP model driven by plans performed on a conventional linac with 6 MV FF beams provides high quality plans performed with 6 MV FFF beams on the new Halcyon© linac.
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Affiliation(s)
- Emilie Costa
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France.
| | - Thomas Richir
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Magalie Robilliard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christel Bragard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christelle Logerot
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Youlia Kirova
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Alain Fourquet
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Ludovic De Marzi
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France; Institut Curie, University Paris Saclay, PSL Research University, Inserm LITO, Orsay, France
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Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021; 85:175-191. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022] Open
Abstract
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
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Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Olga Green
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Görkem Güngör
- Acıbadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
| | - Nick Reynaert
- Department of Medical Physics, Institut Jules Bordet, Belgium
| | - Ruggero Ruggieri
- Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany
| | - Poonam Yadav
- Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA
| | - Yingli Yang
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Dirk Verellen
- Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
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Shao Y, Zhang X, Wu G, Gu Q, Wang J, Ying Y, Feng A, Xie G, Kong Q, Xu Z. Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients With Asymmetric Network. IEEE J Biomed Health Inform 2021; 25:1120-1127. [PMID: 32966222 DOI: 10.1109/jbhi.2020.3025712] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input information and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy dataset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can accurately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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Castriconi R, Cattaneo GM, Mangili P, Esposito P, Broggi S, Cozzarini C, Deantoni C, Fodor A, Di Muzio NG, Vecchio AD, Fiorino C. Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy. Pract Radiat Oncol 2020; 11:e236-e244. [PMID: 33039673 DOI: 10.1016/j.prro.2020.09.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/07/2020] [Accepted: 09/24/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation. METHODS AND MATERIALS One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose-volume parameters and by blinded clinical evaluation of 3 expert clinicians. RESULTS KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum-PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P < .05). CONCLUSIONS The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.
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Affiliation(s)
| | | | - Paola Mangili
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | - Chiara Deantoni
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Andrei Fodor
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | | | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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Xia W, Han F, Chen J, Miao J, Dai J. Personalized setting of plan parameters using feasibility dose volume histogram for auto-planning in Pinnacle system. J Appl Clin Med Phys 2020; 21:119-127. [PMID: 32363757 PMCID: PMC7386185 DOI: 10.1002/acm2.12897] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/26/2020] [Accepted: 04/09/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose The personalized setting of plan parameters in the Auto‐Planning module of the Pinnacle treatment planning system (TPS) using the PlanIQ feasibility tool was evaluated for lung cancer conventional fractionated radiotherapy (CFRT). Materials and method We reviewed the records of ten patients with lung cancer who were treated with volumetric modulated arc therapy (VMAT). Three plans were designed for each patient: the clinically accepted manual plan (MP) and two automatic plans including one generated using the generic plan parameters in technique script (AP1) and the other generated using personalized plan parameters derived based on feasibility dose volume histogram (FDVH) in PlanIQ (AP2). The plans were assessed according to the dosimetric parameters, monitor units, and planning time. A plan quality metric (PQM) was defined according to the clinical requirements for plan assessment. Results AP2 achieved better lung sparing than AP1 and MP. The PQM value of AP2 (52.5 ± 14.3) was higher than those of AP1 (49.2 ± 16.2) and MP (44.8 ± 16.9) with P < 0.05. The monitor units of AP2 (585.9 ± 142.9 MU) was higher than that of AP1 (511.1 ± 136.5 MU) and lower than that of MP (632.8 ± 143.8 MU) with p < 0.05. The planning time of AP2 (33.2 ± 4.8 min) was slightly higher than that of AP1 (28.2 ± 4.0 min) and substantially lower than that of MP (72.9 ± 28.5 min) with P < 0.05. Conclusions The Auto‐Planning module of the Pinnacle system using personalized plan parameters suggested by the PlanIQ Feasibility tool provides superior quality for lung cancer plans, especially in terms of lung sparing. The time consumption of Auto‐Planning was slightly higher with the personalized parameters compared to that with the generic parameters, but significantly lower than that for the manual plan.
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Affiliation(s)
- Wenlong Xia
- 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, 100021, China
| | - Fei Han
- 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, 100021, China
| | - Jiayun Chen
- 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, 100021, China
| | - Junjie Miao
- 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, 100021, China
| | - Jianrong Dai
- 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, 100021, China
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Ray X, Kaderka R, Hild S, Cornell M, Moore KL. Framework for Evaluation of Automated Knowledge-Based Planning Systems Using Multiple Publicly Available Prostate Routines. Pract Radiat Oncol 2020; 10:112-124. [DOI: 10.1016/j.prro.2019.11.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 09/18/2019] [Accepted: 11/13/2019] [Indexed: 11/25/2022]
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Castriconi R, Fiorino C, Passoni P, Broggi S, Di Muzio NG, Cattaneo GM, Calandrino R. Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer. Phys Med 2020; 70:58-64. [PMID: 31982788 DOI: 10.1016/j.ejmp.2020.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/10/2020] [Accepted: 01/15/2020] [Indexed: 01/02/2023] Open
Abstract
PURPOSE To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer. MATERIAL AND METHODS The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA). RESULTS The resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V30Gy and V40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel. CONCLUSIONS The KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.
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Affiliation(s)
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Paolo Passoni
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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Fogliata A, Cozzi L, Reggiori G, Stravato A, Lobefalo F, Franzese C, Franceschini D, Tomatis S, Scorsetti M. RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies. Radiat Oncol 2019; 14:187. [PMID: 31666094 PMCID: PMC6822368 DOI: 10.1186/s13014-019-1403-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/21/2019] [Indexed: 01/23/2023] Open
Abstract
Purpose To determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance. Methods Clinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models. Results The CL-2 model presented an improvement relative to CL-1, with higher R2 values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy. Conclusion A refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.
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Affiliation(s)
- A Fogliata
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
| | - L Cozzi
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Milan, Rozzano, Italy
| | - G Reggiori
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - A Stravato
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - F Lobefalo
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - C Franzese
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - D Franceschini
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - S Tomatis
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - M Scorsetti
- Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Milan, Rozzano, Italy
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Kajikawa T, Kadoya N, Ito K, Takayama Y, Chiba T, Tomori S, Nemoto H, Dobashi S, Takeda K, Jingu K. A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients. JOURNAL OF RADIATION RESEARCH 2019; 60:685-693. [PMID: 31322704 PMCID: PMC6805973 DOI: 10.1093/jrr/rrz051] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/06/2019] [Indexed: 06/10/2023]
Abstract
The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose-volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.
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Affiliation(s)
- Tomohiro Kajikawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yoshiki Takayama
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takahito Chiba
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Seiji Tomori
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Radiology, National Hospital Organization Sendai Medical Center, Sendai, Japan
| | - Hikaru Nemoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Bai X, Shan G, Chen M, Wang B. Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer. Biomed Eng Online 2019; 18:101. [PMID: 31619263 PMCID: PMC6796412 DOI: 10.1186/s12938-019-0721-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 10/09/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) are standard physical technologies of stereotactic body radiotherapy (SBRT) that are used for patients with non-small-cell lung cancer (NSCLC). The treatment plan quality depends on the experience of the planner and is limited by planning time. An automated planning process can save time and ensure a high-quality plan. This study aimed to introduce and demonstrate an automated planning procedure for SBRT for patients with NSCLC based on machine-learning algorithms. The automated planning was conducted in two steps: (1) determining patient-specific optimized beam orientations; (2) calculating the organs at risk (OAR) dose achievable for a given patient and setting these dosimetric parameters as optimization objectives. A model was developed using data of historical expertise plans based on support vector regression. The study cohort comprised patients with NSCLC who were treated using SBRT. A training cohort (N = 125) was used to calculate the beam orientations and dosimetric parameters for the lung as functions of the geometrical feature of each case. These plan-geometry relationships were used in a validation cohort (N = 30) to automatically establish the SBRT plan. The automatically generated plans were compared with clinical plans established by an experienced planner. RESULTS All 30 automated plans (100%) fulfilled the dose criteria for OARs and planning target volume (PTV) coverage, and were deemed acceptable according to evaluation by experienced radiation oncologists. An automated plan increased the mean maximum dose for ribs (31.6 ± 19.9 Gy vs. 36.6 ± 18.1 Gy, P < 0.05). The minimum, maximum, and mean dose; homogeneity index; conformation index to PTV; doses to other organs; and the total monitor units showed no significant differences between manual plans established by experts and automated plans (P > 0.05). The hands-on planning time was reduced from 40-60 min to 10-15 min. CONCLUSION An automated planning method using machine learning was proposed for NSCLC SBRT. Validation results showed that the proposed method decreased planning time without compromising plan quality. Plans generated by this method were acceptable for clinical use.
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Affiliation(s)
- Xue Bai
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, People's Republic of China
| | - Guoping Shan
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, People's Republic of China
| | - Ming Chen
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, People's Republic of China
| | - Binbing Wang
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, People's Republic of China.
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Lee YK, Munawar I, Mashouf S, Sahgal A, Ruschin M. Dosimetric comparison of two treatment planning systems for spine SBRT. Med Dosim 2019; 45:77-84. [PMID: 31376988 DOI: 10.1016/j.meddos.2019.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 06/13/2019] [Accepted: 07/02/2019] [Indexed: 10/26/2022]
Abstract
Two commonly used treatment planning systems (TPS) are compared for the planning of spine stereotactic body radiotherapy (SBRT). The main purpose is to highlight relative advantages and disadvantages of each system and propose a methodologic approach for comparisons. Twenty clinical plans were inversely planned with step-and-shoot intensity-modulated radiotherapy (IMRT) each using 9 to 11 beams, referred to as IMRT_P. The prescription dose was 24 Gy in 2 fractions, and the plans were generated following our institutional protocol using the Pinnacle3 v9.2. Each case was replanned using a 2-arc volumetric modulated arc therapy (VMAT) approach, referred as VMAT_P. CT and structure sets were DICOM exported to Monaco v5.10 and planned in 2 different ways: IMRT (IMRT_M) and VMAT (VMAT_M) using the same prescription dose. Dose volume histograms (DVH) and other dose statistics of planning target volumes (PTV) and organ-at-risk (OAR) were analyzed and compared between plans. The gradient index (GI = ratio of 50% isodose volume to prescribed isodose volume) was used to measure dose fall-off outside of the PTV. Another metric - Gradient Index Inner (GIinner = the rate (in Gy/mm) - at which the dose changes from the level of the spinal cord/thecal sac toward the prescription dose) was developed and compared. All plans were considered clinically acceptable by institutional guidelines and achieved all of the OAR dose constraints. VMAT_M and IMRT_M showed comparable dose statistics for the PTV when compared to VMAT_P and IMRT_P, respectively. For IMRT plans, the median GIinner was 1.88 Gy/mm vs 1.52 Gy/mm for IMRT_M and IMRT_P respectively (p< 0.001). All other IMRT metrics were statistically similar except for the PTV maximum dose (Dmax), which was higher for IMRT_M than IMRT_P (median 30.7 Gy vs 29.0 Gy, p< 0.001). For VMAT plans, only PTV Dmin showed a statistical different between VMAT_M and VMAT_P of median 12.7 Gy vs 9.7 Gy (p< 0.001). In terms of beam sequencing parameters, the number of monitor units was statistically higher for VMAT_P compared to VMAT_M (median = 6764 vs 5376) whereas the number of segments for IMRT_M was statistically greater than IMRT_P (median = 155 vs 73). We were able to generate clinically acceptable plans for different types of spine SBRT using 2 different TPS. We used an evaluation strategy involving coverage, conformity, and dose gradient that can compared between TPS.
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Affiliation(s)
- Young Kyung Lee
- Department of Medical Physics, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON M4N 3M5, Canada.
| | - Iram Munawar
- Department of Medical Physics, Trillium Health Partners, the Credit Valley Hospital, Mississauga, Canada
| | - Shahram Mashouf
- Department of Medical Physics, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Arjun Sahgal
- Department of Radiation Oncology, University of Toronto, Toronto, ON M4N 3M5, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Mark Ruschin
- Department of Medical Physics, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON M4N 3M5, Canada
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Panettieri V, Ball D, Chapman A, Cristofaro N, Gawthrop J, Griffin P, Herath S, Hoyle S, Jukes L, Kron T, Markham C, Marr L, Moloney P, Nelli F, Ramachandran P, Smith A, Hornby CJ. Development of a multicentre automated model to reduce planning variability in radiotherapy of prostate cancer. Phys Imaging Radiat Oncol 2019; 11:34-40. [PMID: 33458275 PMCID: PMC7807596 DOI: 10.1016/j.phro.2019.07.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/19/2019] [Accepted: 07/19/2019] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND AND PURPOSE Inter-institutional studies highlighted correlation between consistent radiotherapy quality and improved overall patient survival. In treatment planning automation has the potential to address differences due to user-experience and training, promoting standardisation. The aim of this study was to evaluate implementation and clinical effect of a multicentre collaboratively-developed automated planning model for Intensity-Modulated Radiation Therapy/Volumetric-Modulated Arc Therapy of prostate. The model was built using a variety of public institutions' clinical plans, incorporating different contouring and dose protocols, aiming at minimising their variation. METHODS AND MATERIALS A model using 110 clinically approved and treated prostate plans provided by different radiotherapy centres was built with RapidPlan (RP), for use on intact and post-prostatectomy prostate cases. The model was validated, distributed and introduced into clinical practice in all institutions. To investigate its impact a total of 126 patients, originally manually inverse planned (OP), were replanned using RP without additional planner manual intervention. Target and organ-at-risk (OAR) metrics were statistically compared between original and automated plans. RESULTS For all centres combined and individually, RP provided plans comparable or superior to OP for all dose metrics. Statistically significant reductions with RP were found in bladder (V40Gy) and rectal (V50Gy) low doses (within 2.3% and 3.4% for combined and 4% and 10% individually). No clinically significant changes were seen for the PTV, independently of seminal vesicle inclusion. CONCLUSION This project showed it is feasible to develop, share and implement RP models created with plans from different institutions treated with a variety of techniques and dose protocols, with the potential of improving treatment planning results and/or efficiency despite the original variability.
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Affiliation(s)
- Vanessa Panettieri
- Alfred Health Radiation Oncology, The Alfred Hospital, Melbourne, Australia
| | - David Ball
- Peter MacCallum Cancer Centre, Parkville, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | - Adam Chapman
- Dept Health & Human Services, Victoria, Australia
| | | | | | - Peter Griffin
- Alfred Health Radiation Oncology, The Alfred Hospital, Melbourne, Australia
| | | | - Susan Hoyle
- Peter MacCallum Cancer Centre, Box Hill, Australia
| | - Liam Jukes
- South-West Regional Cancer Centre, Warrnambool, Australia
| | - Tomas Kron
- Peter MacCallum Cancer Centre, Parkville, Australia
| | | | - Loretta Marr
- South-West Regional Cancer Centre, Warrnambool, Australia
| | | | | | | | - Amanda Smith
- Peter MacCallum Cancer Centre, Moorabbin, Australia
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Ge Y, Wu QJ. Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches. Med Phys 2019; 46:2760-2775. [PMID: 30963580 PMCID: PMC6561807 DOI: 10.1002/mp.13526] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 01/15/2019] [Accepted: 03/26/2019] [Indexed: 12/20/2022] Open
Abstract
Purpose Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation. Conclusions The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.
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Affiliation(s)
- Yaorong Ge
- Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
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Castriconi R, Fiorino C, Broggi S, Cozzarini C, Di Muzio N, Calandrino R, Cattaneo GM. Comprehensive Intra-Institution stepping validation of knowledge-based models for automatic plan optimization. Phys Med 2018; 57:231-237. [PMID: 30545736 DOI: 10.1016/j.ejmp.2018.12.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/23/2018] [Accepted: 12/05/2018] [Indexed: 11/28/2022] Open
Abstract
PURPOSE To develop and apply a stepping approach for the validation of Knowledge-based (KB) models for planning optimization: the method was applied to the case of concomitant irradiation of pelvic nodes and prostate + seminal-vesicles bed irradiation in post-prostatectomy patients. METHODS The clinical VMAT plans of 52 patients optimized by two reference planners were selected to generate a KB-model (RapidPlan, v.13.5 Varian). A stepping-validation approach was followed by comparing KB-generated plans (with and without planner-interaction, RP and only-RP respectively) against delivered clinical plans (RA). The validation followed three steps, gradually extending its generalization: 20 patients used to develop the model (closed-loop); 20 new patients, same planners (open-loop); 20 new patients, different planners (wide-loop). All plans were compared, in terms of relevant dose-volume parameters and generalized equivalent uniform dose (gEUD). RESULTS KB-plans were generally better than or equivalent to clinical plans. For RPvsRA, PTVs coverage was comparable, for OARs RP was always better. Comparing only-RPvsRA, PTVs coverage was always better; bowel\bladder V50Gy and D1%, bowel\bladder\rectum Dmean, femoral heads V40Gy and penile bulb V50Gy were significantly improved. For RPvsRA gEUD reduction >1 Gy was seen in 80% of plans for rectum, bladder and bowel; for only-RPvsRA, this was found in 50% for rectum/bladder and in 70% for bowel. CONCLUSION An extensive stepping validation approach of KB-model for planning optimization showed better or equal performances of automatically generated KB-plan compared to clinical plans. The interaction of a planner further improved planning performances.
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Affiliation(s)
- R Castriconi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - C Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - S Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - C Cozzarini
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - N Di Muzio
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - R Calandrino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - G M Cattaneo
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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Cagni E, Botti A, Wang Y, Iori M, Petit SF, Heijmen BJM. Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction. Phys Med 2018; 55:98-106. [PMID: 30471826 DOI: 10.1016/j.ejmp.2018.11.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 10/01/2018] [Accepted: 11/03/2018] [Indexed: 01/22/2023] Open
Affiliation(s)
- Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; School of Engineering, Cardiff University, Cardiff, Wales, UK.
| | - Andrea Botti
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Yibing Wang
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Mauro Iori
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Steven F Petit
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Ben J M Heijmen
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
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36
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Bossart E, Duffy M, Simpson G, Abramowitz M, Pollack A, Dogan N. Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy. J Appl Clin Med Phys 2018; 19:209-216. [PMID: 30338911 PMCID: PMC6236860 DOI: 10.1002/acm2.12483] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 09/13/2018] [Accepted: 09/27/2018] [Indexed: 11/11/2022] Open
Abstract
Knowledge‐based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter‐patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi‐institutional clinical trials. Training of a KBP library is a complex and difficult process, and models must be validated prior to their clinical use. The purpose of this work is to assess the quality of the treatment plans generated using a specific versus combined purpose model KBP library for prostate cancer. Seven KBP model libraries were created from a set of patients treated on various Institutional Review Board (IRB) approved protocols. All KBP libraries were validated using an independent set of twenty patients (half treated Pr: Prostate alone half treated PLN: prostate plus pelvic lymph nodes). Two models were tested on the Pr patients only, four tested on PLN patients only, and one tested on all patients. All plans were normalized such that at least 95% of the prostate planning target volume received 100% of the planned dose. The plans based on different model libraries were compared to each other and the expert clinical plan. For Pr plans there were almost no statistically significant differences (P < 0.008) between the plans types except conformity index (CI) with library plans better than the expert. For PLN plans, all model libraries in generally showed femur doses and CI better than the expert plans (P < 0.003). This study demonstrated that no large differences were observed between specific versus combined KBP model libraries in dosimetry of prostate cancer patients. This would allow for a fewer specific plans to be needed to create a model library. Further studies are needed to evaluate benefits of combined purpose model libraries for planning of complex sites such as head and neck cancer.
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Affiliation(s)
- Elizabeth Bossart
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
| | - Melissa Duffy
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
| | - Garrett Simpson
- Department of Biomedical Engineering, University of Miami, Coral Gables, Florida
| | - Matthew Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
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Hussein M, Heijmen BJM, Verellen D, Nisbet A. Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations. Br J Radiol 2018; 91:20180270. [PMID: 30074813 DOI: 10.1259/bjr.20180270] [Citation(s) in RCA: 142] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.
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Affiliation(s)
- Mohammad Hussein
- 1 Metrology for Medical Physics Centre, National Physical Laboratory , Teddington , UK
| | - Ben J M Heijmen
- 2 Division of Medical Physics, Erasmus MC Cancer Institute , Rotterdam , The Netherlands
| | - Dirk Verellen
- 3 Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel (VUB) , Brussels , Belgium.,4 Radiotherapy Department, Iridium Kankernetwerk , Antwerp , Belgium
| | - Andrew Nisbet
- 5 Department of Medical Physics, Royal Surrey County Hospital NHS Foundation Trust , Guildford , UK.,6 Department of Physics, University of Surrey , Guildford , UK
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Scaggion A, Fusella M, Roggio A, Bacco S, Pivato N, Rossato MA, Peña LMA, Paiusco M. Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution. Phys Med 2018; 53:86-93. [PMID: 30241759 DOI: 10.1016/j.ejmp.2018.08.016] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 08/01/2018] [Accepted: 08/15/2018] [Indexed: 02/02/2023] Open
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Wang M, Li S, Huang Y, Yue H, Li T, Wu H, Gao S, Zhang Y. An interactive plan and model evolution method for knowledge-based pelvic VMAT planning. J Appl Clin Med Phys 2018; 19:491-498. [PMID: 29984464 PMCID: PMC6123168 DOI: 10.1002/acm2.12403] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 05/07/2018] [Accepted: 06/05/2018] [Indexed: 12/21/2022] Open
Abstract
Purpose To test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process. Methods and materials Eighty‐one manual plans (P0) that were used to configure an initial rectal RapidPlan model (M0) were reoptimized using M0 (closed‐loop), yielding 81 P1 plans. The 75 improved P1 (P1+) and the remaining 6 P0 were used to configure model M1. The 81 training plans were reoptimized again using M1, producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+). Hence, the knowledge base of model M2 composed of 6 P0, 52 P1+, and 23 P2+. Models were tested dosimetrically on 30 VMAT validation cases (Pv) that were not used for training, yielding Pv(M0), Pv(M1), and Pv(M2) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv(M0). Results Based on comparable target dose coverage, the first closed‐loop reoptimization significantly (P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0. However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1) and 0.91 Gy/7.46% (M2) than using M0. The overfitting problem was relieved by applying model M2_new. Conclusions The RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.
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Affiliation(s)
- Meijiao Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China
| | - Sha Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China.,Department of Medical Physics, Institute of Medical Humanities, Peking University, Beijing, China
| | - Yuliang Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China
| | - Haizhen Yue
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China
| | - Tian Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China
| | - Song Gao
- Department of Medical Physics, Institute of Medical Humanities, Peking University, Beijing, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Institute of Heavy Ion Physics, Peking University, Beijing, China
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Gallio E, Giglioli FR, Girardi A, Guarneri A, Ricardi U, Ropolo R, Ragona R, Fiandra C. Evaluation of a commercial automatic treatment planning system for liver stereotactic body radiation therapy treatments. Phys Med 2018. [PMID: 29519402 DOI: 10.1016/j.ejmp.2018.01.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Automated treatment planning is a new frontier in radiotherapy. The Auto-Planning module of the Pinnacle3 treatment planning system (TPS) was evaluated for liver stereotactic body radiation therapy treatments. METHODS Ten cases were included in the study. Six plans were generated for each case by four medical physics experts. The first two planned with Pinnacle TPS, both with manual module (MP) and Auto-Planning one (AP). The other two physicists generated two plans with Monaco TPS (VM). Treatment plan comparisons were then carried on the various dosimetric parameters of target and organs at risk, monitor units, number of segments, plan complexity metrics and human resource planning time. The user dependency of Auto-Planning was also tested and the plans were evaluated by a trained physician. RESULTS Statistically significant differences (Anova test) were observed for spinal cord doses, plan average beam irregularity, number of segments, monitor units and human planning time. The Fisher-Hayter test applied to these parameters showed significant statistical differences between AP e MP for spinal cord doses and human planning time; between MP and VM for monitor units, number of segments and plan irregularity; for all those between AP and VM. The two plans created by different planners with AP were similar to each other. CONCLUSIONS The plans created with Auto-Planning were comparable to the manually generated plans. The time saved in planning enables the planner to commit more resources to more complex cases. The independence of the planner enables to standardize plan quality.
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Affiliation(s)
- Elena Gallio
- Medical Physics Unit, A.O.U. Città della Salute e della Scienza, corso Bramante 88/90, 10126 Turin, Italy.
| | - Francesca Romana Giglioli
- Medical Physics Unit, A.O.U. Città della Salute e della Scienza, corso Bramante 88/90, 10126 Turin, Italy
| | - Andrea Girardi
- Radiation Oncology Department, University Hospital Brussels, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Alessia Guarneri
- Oncology Department, Radiotherapy Unit, A.O.U. Città della Salute e della Scienza, corso Bramante 88/90, 10126 Turin, Italy
| | - Umberto Ricardi
- Oncology Department, Radiotherapy Unit, University of Study of Turin, via Genova 3, 10126 Turin, Italy
| | - Roberto Ropolo
- Medical Physics Unit, A.O.U. Città della Salute e della Scienza, corso Bramante 88/90, 10126 Turin, Italy
| | - Riccardo Ragona
- Oncology Department, Radiotherapy Unit, University of Study of Turin, via Genova 3, 10126 Turin, Italy
| | - Christian Fiandra
- Oncology Department, Radiotherapy Unit, University of Study of Turin, via Genova 3, 10126 Turin, Italy
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