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Alfishawy MM, Kany AI, Elshahat KM. Impact of flattening filter-free beams on remaining volume at risk in lung cancer treatment. RADIATION AND ENVIRONMENTAL BIOPHYSICS 2024; 63:455-464. [PMID: 38762614 DOI: 10.1007/s00411-024-01073-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 05/09/2024] [Indexed: 05/20/2024]
Abstract
Modern radiotherapy machines offer a new modality, like flattening filter-free beam (FFF), which is used especially in stereotactic body radiation therapy (SBRT) to reduce treatment time. The remaining volume at risk (RVR) is known as undefined normal tissue, and assists in evaluating late effects such as carcinogenesis. This study aimed to compare the effects of flattening and un-flattened beams on RVR in lung cancer treated by conventional doses using volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT). Twenty-three lung cancer patients with a prescribed dose of 60 Gy delivered in 30 fractions were selected retrospectively. Four treatment plans were generated for each case (VMAT FF, VMAT FFF, IMRT FF and IMRT FFF). Mean doses to RVR and volumes that received low doses (V15Gy, V10Gy and V5Gy) were introduced as RVR evaluation parameters. Variance percentage comparison between flattening filter (FF) and FFF for the RVR evaluation parameters gave 2.38, 1.10, 1.80 and 2.22 for VMAT, and 1.73, 1.18, 1.62 and 1.81 for IMRT. In contrast, VMAT and IMRT RVR evaluation parameters resulted in variance percentage differences of 10.29, 5.02, - 8.84 and - 4.82 for FF, and 11.18, 4.96, - 8.59 and - 4.48for FFF. It is concluded that in terms of RVR evaluation parameters, FFF is clinically beneficial compared to FF for RVR, due to the decrease in mean RVR dose and low-dose irradiated RVR volume. Furthermore, VMAT is preferred in the mean RVR dose and V15Gy, while IMRT is better in V10Gy and V5Gy for RVR.
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Affiliation(s)
| | - Amr Ismail Kany
- Radiation Physics, Faculty of Science, Al -Azhar University, Cairo, Egypt
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Zhang Y, Huang Y, Lin J, Ding S, Gong X, Liu Q, Gong C. Multi-isocenter VMAT craniospinal irradiation using feasibility dose-volume histogram-guided auto-planning technique. JOURNAL OF RADIATION RESEARCH 2023:7150737. [PMID: 37141634 DOI: 10.1093/jrr/rrad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/07/2022] [Indexed: 05/06/2023]
Abstract
This study aims to propose a novel treatment planning methodology for multi-isocenter volumetric modulated arc therapy (VMAT) craniospinal irradiation (CSI) using the special feasibility dose-volume histogram (FDVH)-guided auto-planning (AP) technique. Three different multi-isocenter VMAT -CSI plans were created, including manually based plans (MUPs), conventional AP plans (CAPs) and FDVH-guided AP plans (FAPs). The CAPs and FAPs were specially designed by combining multi-isocenter VMAT and AP techniques in the Pinnacle treatment planning system. Specially, the personalized optimization parameters for FAPs were generated using the FDVH function implemented in PlanIQ software, which provides the ideal organs at risk (OARs) sparing for the specific anatomical geometry based on the valuable assumption of the dose fall-off. Compared to MUPs, CAPs and FAPs significantly reduced the dose for most of the OARs. FAPs achieved the best homogeneity index (0.092 ± 0.013) and conformity index (0.980 ± 0.011), while CAPs were slightly inferior to the FAPs but superior to the MUPs. As opposed to MUPs, FAPs delivered a lower dose to OARs, whereas the difference between FAPs and CAPs was not statistically significant except for the optic chiasm and inner ear_L. The two AP approaches had similar MUs, which were significantly lower than the MUPs. The planning time of FAPs (145.00 ± 10.25 min) was slightly lower than that of CAPs (149.83 ± 14.37 min) and was substantially lower than that of MUPs (157.92 ± 16.11 min) with P < 0.0167. Overall, introducing the multi-isocenter AP technique into VMAT-CSI yielded positive outcomes and may play an important role in clinical CSI planning in the future.
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Affiliation(s)
- Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
| | - Yuling Huang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
| | - Jiafan Lin
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
| | - Shenggou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
| | - Xiaochang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, 999 Xuefu Dadao, Honggutan District, Nanchang 330031, China
| | - Changfei Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 519 East Beijing Road, Qingshanhu District, Nanchang 330029, China
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3
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Visak J, Inam E, Meng B, Wang S, Parsons D, Nyugen D, Zhang T, Moon D, Avkshtol V, Jiang S, Sher D, Lin MH. Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation. J Appl Clin Med Phys 2023:e13950. [PMID: 36877668 DOI: 10.1002/acm2.13950] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/02/2023] [Accepted: 02/06/2023] [Indexed: 03/07/2023] Open
Abstract
PURPOSE Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART). METHODS Twenty previously treated patients treated on C-arm/Ring-mounted were retroactively re-planned in the Ethos planning system using a fixed 18-beam intensity-modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in-house deep-learning 3D-dose predictor (AI-Guided) (2) commercial knowledge-based planning (KBP) model with universal RTOG-based population criteria (KBP-RTOG) and (3) an RTOG-based constraint template only (RTOG) for in-depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH-estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high-impact organs-at-risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two-tailed student t-test. RESULTS AI-guided plans were superior to both KBP-RTOG and RTOG-only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI-guided plans versus benchmark, while they increased with KBP-RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP-RTOG, AI-Guided, RTOG and benchmark plans, respectively. CONCLUSION AI-guided plans were the highest quality. Both KBP-enabled and RTOG-only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria.
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Affiliation(s)
- Justin Visak
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Enobong Inam
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Boyu Meng
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Siqiu Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - David Parsons
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Dan Nyugen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tingliang Zhang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Dominic Moon
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Vladimir Avkshtol
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - David Sher
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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4
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Liu H, Sintay B, Wiant D. A two-step treatment planning strategy incorporating knowledge-based planning for head-and-neck radiotherapy. J Appl Clin Med Phys 2023:e13939. [PMID: 36826845 DOI: 10.1002/acm2.13939] [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: 08/12/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
PURPOSE There has been much research interest in automated head-and-neck (HN) planning with the goal of reducing planning time and inter-planner variability while improving plan quality. However, clinical uses are still limited and institution-dependent due to the plan complexity. This work aims to investigate whether the use of a novel semi-automated two-step optimization method (TSP) can improve the quality and efficiency of planning while providing a simple framework that other institutions can follow. METHODS AND MATERIALS Forty patients (two and three prescription isodose levels) were retrospectively studied. Plans were generated by TSP which incorporates a knowledge-based planning solution. Comparisons were performed for plan conformity and selected dose-volume indices between clinical plan (CP) and TSP. Blind reviews were carried out by 15 clinicians to determine preference between the CP and TSP, as well as clinical suitability. RESULTS For majority of patients studied, TSP had similar or slightly better conformity for the high-dose PTV, and better conformity for the low-dose PTV and 45 Gy isodose lines compared to CP. The only statistically significant difference observed for the serial organs was a reduction of the spinal cord maximum dose with TSP. Except for left parotid gland (Dmean and V30 for both 2R× and 3R× groups) and oral cavity (Dmean for 3R× group), TSP had significant dose reductions for all parallel organs compared to CP. Blind reviewers either showed preference/no preference for 57.2%/21.7% (2R×) and 57.5%/27.8% (3R×) of TSP compared with CP. Excluding no preference votes, 60% of TSP were preferred. TSP was selected majority of the time when looking at the vote distribution for each patient individually. CONCLUSION Our TSP allows plans to be created within 90-min time frame while offering improvements in plan quality and less inter-planner variability as compared to traditional planning techniques.
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Affiliation(s)
- Han Liu
- Department of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA
| | - Benjamin Sintay
- Department of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA
| | - David Wiant
- Department of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA
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5
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DiBartolo D, Carpenter T, Santoro JP, Lischalk JW, Ebling D, Haas JA, Witten M, Rybstein M, Vaezi A, Repka MC. Novel VMAT planning technique improves dosimetry for head and neck cancer patients undergoing definitive chemoradiotherapy. Acta Oncol 2023; 62:189-193. [PMID: 36790072 DOI: 10.1080/0284186x.2023.2177973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Affiliation(s)
- David DiBartolo
- Department of Medical Physics, Perlmutter Cancer Center at NYU Long Island, Mineola, NY
| | - Todd Carpenter
- Department of Radiation Oncology, Perlmutter Cancer Center at NYU Long Island, Mineola, NY
| | - Joseph P Santoro
- Department of Medical Physics, Perlmutter Cancer Center at NYU Long Island, Mineola, NY
| | - Jonathan W Lischalk
- Department of Radiation Oncology, Perlmutter Cancer Center at NYU Long Island, Mineola, NY.,NYCyberKnife at Perlmutter Cancer Center Manhattan, New York, NY, USA
| | - David Ebling
- Department of Radiation Oncology, Perlmutter Cancer Center at NYU Long Island, Mineola, NY
| | - Jonathan A Haas
- Department of Radiation Oncology, Perlmutter Cancer Center at NYU Long Island, Mineola, NY.,NYCyberKnife at Perlmutter Cancer Center Manhattan, New York, NY, USA
| | - Matthew Witten
- Department of Medical Physics, Perlmutter Cancer Center at NYU Long Island, Mineola, NY
| | - Marissa Rybstein
- Division of Hematology/Oncology, Perlmutter Cancer Center at NYU Long Island, Mineola, NY, USA
| | - Alec Vaezi
- Department of Otolaryngology-Head and Neck Surgery, Perlmutter Cancer Center at NYU Long Island, Mineola, NY, USA
| | - Michael C Repka
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
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6
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Baroudi H, Brock KK, Cao W, Chen X, Chung C, Court LE, El Basha MD, Farhat M, Gay S, Gronberg MP, Gupta AC, Hernandez S, Huang K, Jaffray DA, Lim R, Marquez B, Nealon K, Netherton TJ, Nguyen CM, Reber B, Rhee DJ, Salazar RM, Shanker MD, Sjogreen C, Woodland M, Yang J, Yu C, Zhao Y. Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'? Diagnostics (Basel) 2023; 13:diagnostics13040667. [PMID: 36832155 PMCID: PMC9955359 DOI: 10.3390/diagnostics13040667] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/21/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
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Affiliation(s)
- Hana Baroudi
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kristy K. Brock
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenhua Cao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xinru Chen
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Mohammad D. El Basha
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Maguy Farhat
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Skylar Gay
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Aashish Chandra Gupta
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soleil Hernandez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kai Huang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - David A. Jaffray
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca Lim
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Barbara Marquez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kelly Nealon
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Callistus M. Nguyen
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Brandon Reber
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ramon M. Salazar
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mihir D. Shanker
- The University of Queensland, Saint Lucia 4072, Australia
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos Sjogreen
- Department of Physics, University of Houston, Houston, TX 77004, USA
| | - McKell Woodland
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Jinzhong Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cenji Yu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Zhao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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Lavigne D, Ng SP, O’Sullivan B, Nguyen-Tan PF, Filion E, Létourneau-Guillon L, Fuller CD, Bahig H. Magnetic Resonance-Guided Radiation Therapy for Head and Neck Cancers. Curr Oncol 2022; 29:8302-8315. [PMID: 36354715 PMCID: PMC9689607 DOI: 10.3390/curroncol29110655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/25/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Despite the significant evolution of radiation therapy (RT) techniques in recent years, many patients with head and neck cancer still experience significant toxicities during and after treatments. The increased soft tissue contrast and functional sequences of magnetic resonance imaging (MRI) are particularly attractive in head and neck cancer and have led to the increasing development of magnetic resonance-guided RT (MRgRT). This approach refers to the inclusion of the additional information acquired from a diagnostic or planning MRI in radiation treatment planning, and now extends to online high-quality daily imaging generated by the recently developed MR-Linac. MRgRT holds numerous potentials, including enhanced baseline and planning evaluations, anatomical and functional treatment adaptation, potential for hypofractionation, and multiparametric assessment of response. This article offers a structured review of the current literature on these established and upcoming roles of MRI for patients with head and neck cancer undergoing RT.
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Affiliation(s)
- Danny Lavigne
- Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Sweet Ping Ng
- Department of Radiation Oncology, Olivia Newton-John Cancer Centre, Austin Health, Melbourne, VI 3084, Australia
| | - Brian O’Sullivan
- Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Phuc Felix Nguyen-Tan
- Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Edith Filion
- Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Laurent Létourneau-Guillon
- Department of Radiology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Clifton D. Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA
| | - Houda Bahig
- Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC H2X 3E4, Canada
- Correspondence:
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8
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Yan C, Guo B, Tendulkar R, Xia P. Contour similarity and its implication on inverse prostate SBRT treatment planning. J Appl Clin Med Phys 2022; 24:e13809. [PMID: 36300837 PMCID: PMC9924104 DOI: 10.1002/acm2.13809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 08/01/2022] [Accepted: 09/13/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Success of auto-segmentation is measured by the similarity between auto and manual contours that is often quantified by Dice coefficient (DC). The dosimetric impact of contour variability on inverse planning has been rarely reported. The main aim of this study is to investigate whether automatically generated organs-at-risk (OARs) could be used in inverse prostate stereotactic body radiation therapy (SBRT) planning and whether the dosimetric parameters are still clinically acceptable after radiation oncologists modify the OARs. METHODS AND MATERIALS Planning computed tomography images from 10 patients treated with SBRT for prostate cancer were selected and automatically segmented by commercially available atlas-based software. The automatically generated OAR contours were compared with the manually drawn contours. Two volumetric modulated arc therapy (VMAT) plans, autoRec-VMAT (where only automatically generated rectums were used in optimization) and autoAll-VMAT (where automatically generated OARs were used in inverse optimization) were generated. Dosimetric parameters based on the manually drawn PTV and OARs were compared with the clinically approved plans. RESULTS The DCs for the rectum contours varied from 0.55 to 0.74 with a mean value of 0.665. Differences of D95 of the PTV between autoRec-VMAT and manu-VMAT plans varied from 0.03% to -2.85% with a mean value of -0.64%. Differences of D0.03cc of manual rectum between the two plans varied from -0.86% to 9.94% with a mean value of 2.71%. D95 of PTV between autoAll-VMAT and manu-VMAT plans varied from 0.28% to -2.9% with a mean value -0.83%. Differences of D0.03cc of manual rectum between the two plans varied from -0.76% to 6.72% with a mean value of 2.62%. CONCLUSION Our study implies that it is possible to use unedited automatically generated OARs to perform initial inverse prostate SBRT planning. After radiation oncologists modify/approve the OARs, the plan qualities based on the manually drawn OARs are still clinically acceptable, and a re-optimization may not be needed.
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Affiliation(s)
- Chenyu Yan
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Bingqi Guo
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Rahul Tendulkar
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Ping Xia
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
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9
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Ahmed S, Liu C, LaHurd D, Murray E, Kolar M, Joshi N, Woody N, Koyfman S, Xia P. Using feasibility dose-volume histograms to reduce intercampus plan quality variability for head-and-neck cancer. J Appl Clin Med Phys 2022; 24:e13749. [PMID: 35962566 PMCID: PMC9859985 DOI: 10.1002/acm2.13749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 04/12/2022] [Accepted: 07/21/2022] [Indexed: 01/26/2023] Open
Abstract
The purpose of this work is to objectively assess variability of intercampus plan quality for head-and-neck (HN) cancer and to test utility of a priori feasibility dose-volume histograms (FDVHs) as planning dose goals. In this study, 109 plans treated from 2017 to 2019 were selected, with 52 from the main campus and 57 from various regional centers. For each patient, the planning computed tomography images and contours were imported into a commercial program to generate FDVHs with a feasibility value (f-value) ranging from 0.0 to 0.5. For 10 selected organs-at-risk (OARs), we used the Dice similarity coefficient (DSC) to quantify the overlaps between FDVH and clinically achieved DVH of each OAR and determined the f-value associated with the maximum DSC (labeled as f-max). Subsequently, 10 HN plans from the regional centers were replanned with planning dose goals guided by FDVHs. The clinical and feasibility-guided auto-planning (FgAP) plans were evaluated using our institutional criteria. Among plans from the main campus and regional centers, the median f-max values were statistically significantly different (p < 0.05) for all OARs except for the left parotid (p = 0.622), oral cavity (p = 0.057), and mandible (p = 0.237). For the 10 FgAP plans, the median values of f-max were 0.21, compared to 0.37 from the clinical plans. With comparable dose coverage to the tumor volumes, the significant differences (p < 0.05) in the median f-max and corresponding dose reduction (shown in parenthesis) for the spinal cord, larynx, supraglottis, trachea, and esophagus were 0.27 (8.5 Gy), 0.3 (7.6 Gy), 0.19 (5.9 Gy), 0.19 (8.9 Gy), and 0.12 (4.0 Gy), respectively. In conclusion, the FDVH prediction is an objective quality assurance tool to evaluate the intercampus plan variability. This tool can also provide guideline in planning dose goals to further improve plan quality.
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Affiliation(s)
- Saeed Ahmed
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Chieh‐Wen Liu
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Danielle LaHurd
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Eric Murray
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Matthew Kolar
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Nikhil Joshi
- Department of Radiation OncologyRush University Medical CenterChicagoIllinoisUSA
| | - Neil Woody
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Shlomo Koyfman
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
| | - Ping Xia
- Department of Radiation Oncology, Taussig Cancer CenterCleveland Clinic FoundationClevelandOhioUSA
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10
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Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer. Adv Radiat Oncol 2022; 8:101029. [PMID: 36578278 PMCID: PMC9791598 DOI: 10.1016/j.adro.2022.101029] [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: 12/21/2021] [Accepted: 07/10/2022] [Indexed: 12/31/2022] Open
Abstract
Purpose Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality. Methods and Materials An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization. Results Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01). Conclusions AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.
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11
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Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers. J Clin Med 2022; 11:jcm11051388. [PMID: 35268479 PMCID: PMC8911481 DOI: 10.3390/jcm11051388] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/16/2022] Open
Abstract
Radiotherapy is an integral component of head/neck squamous cell carcinomas (HNSCCs) treatment, and technological developments including advances in image-guided radiotherapy over the past decades have offered improvements in the technical treatment of these cancers. Integration of magnetic resonance imaging (MRI) into image guidance through the development of MR-guided radiotherapy (MRgRT) offers further potential for refinement of the techniques by which HNSCCs are treated. This article provides an overview of the literature supporting the current use of MRgRT for HNSCC, challenges with its use, and developing research areas.
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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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13
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Ouyang Z, Zhuang T, Marwaha G, Kolar MD, Qi P, Videtic GM, Stephans KL, Xia P. Evaluation of Automated Treatment Planning and Organ Dose Prediction for Lung Stereotactic Body Radiotherapy. Cureus 2021; 13:e18473. [PMID: 34754638 PMCID: PMC8569686 DOI: 10.7759/cureus.18473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSES To evaluate whether the auto-planning (AP) module can achieve clinically acceptable treatment plans for lung stereotactic body radiotherapy (SBRT) and to evaluate the effectiveness of a dose prediction model. METHODS Twenty lung SBRT cases planned manually with 50 Gy in five fractions were replanned using the Pinnacle (Philips Radiation Oncology Systems, Fitchburg, WI) AP module according to the dose constraint tables from the Radiation Therapy Oncology Group (RTOG) 0813 protocol. Doses to the organs at risk (OAR) were compared between the manual and AP plans. Using a dose prediction model from a commercial product, PlanIQ (Sun Nuclear Corporation, Melbourne, FL), we also compared OAR doses from AP plans with predicted doses. RESULTS All manual and AP plans achieved clinically required dose coverage to the target volumes. The AP plans achieved equal or better OAR sparing when compared to the manual plans, most noticeable in the maximum doses of the spinal cord, ipsilateral brachial plexus, esophagus, and trachea. Predicted doses to the heart, esophagus, and trachea were highly correlated with the doses of these OARs from the AP plans with the highest correlation coefficient of 0.911, 0.823, and 0.803, respectively. CONCLUSION Auto-planning for lung SBRT improved OAR sparing while keeping the same dose coverage to the tumor. The dose prediction model can provide useful planning dose guidance.
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Affiliation(s)
- Zi Ouyang
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | - Tingliang Zhuang
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | - Gaurav Marwaha
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | - Matthew D Kolar
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | - Peng Qi
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | | | - Kevin L Stephans
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, USA
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14
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Li Y, Bai H, Huang D, Chen F, Xia Y. Evaluation of Auto-Planning for Left-Side Breast Cancer After Breast-Conserving Surgery Based on Geometrical Relationship. Technol Cancer Res Treat 2021; 20:15330338211033050. [PMID: 34355592 PMCID: PMC8358503 DOI: 10.1177/15330338211033050] [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] [Indexed: 12/24/2022] Open
Abstract
PURPOSE This study aimed to evaluate (1) the performance of the Auto-Planning module embedded in the Pinnacle treatment planning system (TPS) with 30 left-side breast cancer plans and (2) the dose-distance correlations between dose-based patients and overlap volume histogram-based (OVH) patients. METHOD A total of 30 patients with left-side breast cancer after breast-conserving surgery were enrolled in this study. The clinical manual-planning (MP) and the Auto-Planning (AP) plans were generated by Monaco and by the Auto-Planning module in Pinnacle respectively. The geometric information between organ at risk (OAR) and planning target volume (PTV) of each patient was described by the OVH. The AP and MP plans were ranked to compare with the geometry-based patients from OVH. The Pearson product-moment correlation coefficient (R) was used to describe the correlations between dose-based patients (APs and MPs) and geometry-based patients (OVH). Dosimetric differences between MP and AP plans were evaluated with statistical analysis. RESULT The correlation coefficient (mean R = 0.71) indicated that the AP plans have a high correlation with geometry-based patients from OVH, whereas the correlation coefficient (mean R = 0.48) shows a weak correlation between MP plans and geometry-based patients. The dosimetric comparison revealed a statistically significant improvement in the ipsilateral lung V5Gy and V10Gy, and in the heart V5Gy of AP plans compared to MP plans, while statistical reduction was seen in PTV V107% for MP plans compared to AP plans. CONCLUSION The overall results of AP plans were superior to MP plans. The dose distribution in AP plans was more consistent with the distance-dose relationship described by OVH. After eliminating the interference of human factors, the AP was able to provide more stable and objective plans for radiotherapy patients.
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Affiliation(s)
- Yijiang Li
- Department of Radiation Oncology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Danju Huang
- Department of Radiation Oncology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Feihu Chen
- Department of Radiation Oncology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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15
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Olanrewaju A, Court LE, Zhang L, Naidoo K, Burger H, Dalvie S, Wetter J, Parkes J, Trauernicht CJ, McCarroll RE, Cardenas C, Peterson CB, Benson KRK, du Toit M, van Reenen R, Beadle BM. Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant. Pract Radiat Oncol 2021; 11:177-184. [PMID: 33640315 DOI: 10.1016/j.prro.2020.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. METHODS AND MATERIALS The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion. RESULTS Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable. CONCLUSIONS Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.
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Affiliation(s)
- Adenike Olanrewaju
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lifei Zhang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Komeela Naidoo
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Hester Burger
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Sameera Dalvie
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Julie Wetter
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Jeannette Parkes
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Christoph J Trauernicht
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Rachel E McCarroll
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine B Peterson
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kathryn R K Benson
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Monique du Toit
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Ricus van Reenen
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California.
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Xia X, Wang J, Li Y, Peng J, Fan J, Zhang J, Wan J, Fang Y, Zhang Z, Hu W. An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer. Front Oncol 2021; 10:616721. [PMID: 33614500 PMCID: PMC7886996 DOI: 10.3389/fonc.2020.616721] [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: 10/13/2020] [Accepted: 12/22/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND AND PURPOSE To develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy. MATERIALS AND METHODS A full-process solution that integrates autosegmentation and automatic treatment planning was developed under a single deep-learning framework. A convolutional neural network (CNN) was used to generate segmentations of the target and the organs at risk (OAR) as well as dose distribution. A script in Pinnacle that simulates the treatment planning process was used to execute plan optimization. A total of 172 rectal cancer patients were used for model training, and 18 patients were used for model validation. Another 40 rectal cancer patients were used for an end-to-end evaluation for both autosegmentation and treatment planning. The PTV and OAR segmentation was compared with manual segmentation. The planning results was evaluated by both objective and subjective assessment. RESULTS The total time for full-process planning without contour modification was 7 min, and an additional 15 min may require for contour modification and re-optimization. The PTV DICE similarity coefficient was greater than 0.85 for all 40 patients in the evaluation dataset while the DICE indices of the OARs also indicated good performance. There were no significant differences between the auto plans and manual plans. The physician accepted 80% of the auto plans without any further operation. CONCLUSION We developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.
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Affiliation(s)
- Xiang Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yujiao Li
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jiayuan Peng
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Juefeng Wan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yingtao Fang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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17
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Cilla S, Deodato F, Romano C, Ianiro A, Macchia G, Re A, Buwenge M, Boldrini L, Indovina L, Valentini V, Morganti AG. Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy? Phys Med 2021; 82:7-16. [PMID: 33508633 DOI: 10.1016/j.ejmp.2020.12.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/04/2020] [Accepted: 12/19/2020] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To perform a comprehensive dosimetric and clinical evaluation of the new Pinnacle Personalized automated planning system for complex head-and-neck treatments. METHODS Fifteen consecutive head-neck patients were enrolled. Radiotherapy was prescribed using VMAT with simultaneous integrated boost strategy. Personalized planning integrates the Feasibility engine able to supply an "a priori" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually-generated (MP) and automated (AP) plans was performed using dose-volume histograms and a blinded clinical evaluation by two radiation oncologists. Planning time between MP and AP was compared. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. RESULTS For similar targets coverage, AP plans reported less irradiation of healthy tissue, with significant dose reduction for spinal cord, brainstem and parotids. On average, the mean dose to parotids and maximal doses to spinal cord and brainstem were reduced by 13-15% (p < 0.001), 9% (p < 0.001) and 16% (p < 0.001), respectively. The integral dose was reduced by 16% (p < 0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p < 0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30 min for automated optimization. All AP plans passed the 3%/2 mm γ-analysis by more than 95%. CONCLUSION Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy.
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Anna Ianiro
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Alessia Re
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
| | - Luca Boldrini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Luca Indovina
- Medical Physics Unit, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Alessio G Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
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Radiation-Induced Salivary Gland Dysfunction: Mechanisms, Therapeutics and Future Directions. J Clin Med 2020; 9:jcm9124095. [PMID: 33353023 PMCID: PMC7767137 DOI: 10.3390/jcm9124095] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/17/2020] [Accepted: 12/17/2020] [Indexed: 12/14/2022] Open
Abstract
Salivary glands sustain collateral damage following radiotherapy (RT) to treat cancers of the head and neck, leading to complications, including mucositis, xerostomia and hyposalivation. Despite salivary gland-sparing techniques and modified dosing strategies, long-term hypofunction remains a significant problem. Current therapeutic interventions provide temporary symptom relief, but do not address irreversible glandular damage. In this review, we summarize the current understanding of mechanisms involved in RT-induced hyposalivation and provide a framework for future mechanistic studies. One glaring gap in published studies investigating RT-induced mechanisms of salivary gland dysfunction concerns the effect of irradiation on adjacent non-irradiated tissue via paracrine, autocrine and direct cell-cell interactions, coined the bystander effect in other models of RT-induced damage. We hypothesize that purinergic receptor signaling involving P2 nucleotide receptors may play a key role in mediating the bystander effect. We also discuss promising new therapeutic approaches to prevent salivary gland damage due to RT.
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19
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Cilla S, Ianiro A, Romano C, Deodato F, Macchia G, Viola P, Buwenge M, Cammelli S, Pierro A, Valentini V, Morganti AG. Automated treatment planning as a dose escalation strategy for stereotactic radiation therapy in pancreatic cancer. J Appl Clin Med Phys 2020; 21:48-57. [PMID: 33063456 PMCID: PMC7700933 DOI: 10.1002/acm2.13025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 07/17/2020] [Accepted: 08/07/2020] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To assess the feasibility of automated stereotactic volumetric modulated arc therapy (SBRT-VMAT) planning using a simultaneous integrated boost (SIB) approach as a dose escalation strategy for SBRT in pancreatic cancer. METHODS Twelve patients with pancreatic cancer were retrospectively replanned. Dose prescription was 30 Gy to the planning target volume (PTV) and was escalated up to 50 Gy to the boost target volume (BTV) using a SIB technique in 5 fractions. All plans were generated by Pinnacle3 Autoplanning using 6MV dual-arc VMAT technique for flattened (FF) and flattening filter-free beams (FFF). An overlap volume (OLV) between the PRV duodenum and the PTV was defined to correlate with the ability to boost the BTV. Dosimetric metrics for BTV and PTV coverage, maximal doses for serial OARs, integral dose, conformation numbers, and dose contrast indexes were used to analyze the dosimetric results. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. Differences between FF and FFF plans were quantified using the Wilcoxon matched-pair signed rank. RESULTS Full prescription doses to the 95% of PTV and BTV can be delivered to patients with no OLV. BTV mean dose was >90% of the prescribed doses for all patients at all dose levels. Compared to FF plans, FFF plans showed significant reduced integral doses, larger number of MUs, and reduced beam-on-times up to 51% for the highest dose level. Despite plan complexity, pre-treatment verification reported a gamma pass-rate greater than the acceptance threshold of 95% for all FF and FFF plans for 3%-2 mm criteria. CONCLUSIONS The SIB-SBRT strategy with Autoplanning was dosimetrically feasible. Ablative doses up to 50 Gy in 5 fractions can be delivered to the BTV for almost all patients respecting all the normal tissue constraints. A prospective clinical trial based on SBRT strategy using SIB-VMAT technique with FFF beams seems to be justified.
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Affiliation(s)
- Savino Cilla
- Medical Physics UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Anna Ianiro
- Medical Physics UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Carmela Romano
- Medical Physics UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Francesco Deodato
- Radiation Oncology UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Gabriella Macchia
- Radiation Oncology UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Pietro Viola
- Medical Physics UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Milly Buwenge
- Radiation Oncology DepartmentDIMES Università di Bologna ‐ Ospedale S.Orsola MalpighiBolognaItaly
| | - Silvia Cammelli
- Radiation Oncology DepartmentDIMES Università di Bologna ‐ Ospedale S.Orsola MalpighiBolognaItaly
| | - Antonio Pierro
- Radiology DepartmentGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
| | - Vincenzo Valentini
- Radiation Oncology UnitGemelli Molise Hospital ‐ Università Cattolica del Sacro CuoreCampobassoItaly
- Radiation Oncology DepartmentFondazione Policlinico Universitario A. Gemelli ‐ Università Cattolica del Sacro Cuore ‐ RomaItaly
| | - Alessio G. Morganti
- Radiation Oncology DepartmentDIMES Università di Bologna ‐ Ospedale S.Orsola MalpighiBolognaItaly
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Bai H, Zhu S, Wu X, Liu X, Chen F, Yan J. Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS). Radiat Oncol 2020; 15:161. [PMID: 32631380 PMCID: PMC7336463 DOI: 10.1186/s13014-020-01603-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023] Open
Abstract
Objective To explore the efficacy and sensitivity of 3D gamma analysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size (DCGS). Methods 17 patients’ plans for cervical cancer were enrolled (Pinnacle TPS, VMAT), and the DCGS was changed from 2.0 mm to 5.0 mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0 mm as the “reference” data set (RDS), the dose distribution calculated by the rest DCGS as the“measurement”data set (MDS), the 3D gamma passing rates and the (N) TCPs of the all structures under different DCGS were obtained, and then analyze the ability of 3D gamma analysis and (N) TCP model in detecting dose changes and what factors affect this ability. Results The effect of DCGS on planned dose was obvious. When the gamma standard was 1.0 mm, 1.0 and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3D gamma analysis (all p value < 0.05). With the decline of the standard, 3D gamma analysis’ ability to detect this difference shows weaker. When the standard was 1.0 mm, 3.0 and 10.0%, the p value of > 0.05 accounted for the majority. With DCGS = 2.0 mm being RDS, ∆gamma-passing-rate presented the same trend with ∆(N) TCPs of all structures except for the femurs only when the 1.0 mm, 1.0 and 10.0% standards were adopted for the 3D gamma analysis. Conclusions The 3D gamma analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former’s detection ability has a lot to do with the designed standard, and the latter’s capability is related to the parameters and calculated accuracy instrinsically.
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Affiliation(s)
- Han Bai
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China
| | - Sijin Zhu
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China
| | - Xingrao Wu
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China.
| | - Xuhong Liu
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China
| | - Feihu Chen
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China
| | - Jiawen Yan
- Department of Radiation Oncology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University, No.519 Kunzhou, Road, Xishan District, Kunming, Yunnan, China
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Zhang Q, Ou L, Peng Y, Yu H, Wang L, Zhang S. Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma. Strahlenther Onkol 2020; 197:177-187. [PMID: 32488293 DOI: 10.1007/s00066-020-01631-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/04/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE This study aimed to evaluate the quality of locally advanced nasopharyngeal carcinoma (NPC) radiotherapy plans generated by the automated planning module of a commercial treatment planning system (TPS). METHODS Data of 30 patients with locally advanced NPC were retrospectively investigated. For each patient, volumetric modulated arc therapy (VMAT) plans with double arcs were generated manually by experienced physicists and automatically in the Pinnacle3 Auto-Planning module (Philips Medical Systems, Fitchburg, WI, USA). The anatomic distance between the second clinical target volume (CTV2) and the pons of the brainstem, and the T category of disease were factored into the evaluation. Dosimetric verification was evaluated in terms of gamma pass rate. Target coverage, sparing of organs at risk (OARs), and monitor units were evaluated and compared between the manual and automatic VMAT plans. RESULTS Not all treatment plans fully met the dose objectives for planning target volumes (PTVs) and OARs, particularly in T4 patients. Overall, automatic VMAT provides a comparable or superior plan quality to manual VMAT in most cases. In stratified analysis, plan quality is mainly independent on T category but is also affected by anatomic distance. If the anatomic distance is less than 5 mm, the automatic VMAT plan quality is equal or even inferior to manual VMAT performed by experienced physicists. Conversely, if the anatomic distance is greater than 5 mm, the automatic VMAT plan quality is superior to manual VMAT. Gamma pass rates for quality assurance are similar between manual and automatic VMAT plans for the former case, but significantly higher in automatic VMAT for the latter. CONCLUSION The selection of manual versus automatic VMAT planning in locally advanced NPC should be made individually based on the anatomic distance, rather than blindly and habitually, since automatic VMAT is not good enough to completely replace manual VMAT.
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Affiliation(s)
- Quanbin Zhang
- Radiotherapy center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Liya Ou
- Guangzhou Medical University, Guangzhou, China.
| | - Yingying Peng
- Radiotherapy center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Hui Yu
- Radiotherapy center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Linjing Wang
- Radiotherapy center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Shuxu Zhang
- Radiotherapy center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
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Ling C, Han X, Zhai P, Xu H, Chen J, Wang J, Hu W. A hybrid automated treatment planning solution for esophageal cancer. Radiat Oncol 2019; 14:232. [PMID: 31856866 PMCID: PMC6923830 DOI: 10.1186/s13014-019-1443-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 12/11/2019] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer. METHODS In order to fully investigate the advantages of HAP, three planning strategies were implemented in the present study: HAP, KBP, and full manual planning. Each method was applied to 20 patients. For HAP and KBP, the objective functions for plan optimization were generated from a dose-volume histogram (DVH) estimation model, which was based on 70 esophageal patients. Script-based automated planning was used for HAP, while the regular IMRT inverse planning method was used for KBP. For full manual planning, clinical standards were applied to create the plans. Paired t-tests were performed to compare the differences in dose-volume indices among the three planning methods. RESULTS Among the three planning strategies, HAP exhibited the best performance in all dose-volume indices, except for PTV dose homogeneity and lung V5. PTV conformity and spinal cord sparing were significantly improved in HAP (P < 0.001). Compared to KBP, HAP improved all indices, except for lung V5. Furthermore, the OAR sparing and target coverage between HAP and full manual planning were similar. Moreover, HAP had the shortest average planning time (57 min), when compared to KBP (63 min) and full manual planning (118 min). CONCLUSION HAP is an effective planning strategy for obtaining a high quality treatment plan for esophageal cancer.
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Affiliation(s)
- Chifang Ling
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China
| | - Xu Han
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China
| | - Peng Zhai
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China
| | - Hao Xu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China
| | - Jiayan Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China.
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Shanghai, 200032, China.
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