1
|
Aristophanous M, Aliotta E, Lichtenwalner P, Abraham S, Nehmeh M, Caringi A, Zhang P, Hu YC, Zhang P, Cervino L, Gelblum D, McBride S, Riaz N, Chen L, Yu Y, Zakeri K, Lee N. Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection. Int J Radiat Oncol Biol Phys 2024; 119:1557-1568. [PMID: 38373657 DOI: 10.1016/j.ijrobp.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 01/26/2024] [Accepted: 02/08/2024] [Indexed: 02/21/2024]
Abstract
PURPOSE The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART. METHODS AND MATERIALS Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis. RESULTS In total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10-5), parotids (-68.2 ± 197.7 cGy, P = .001), and oral cavity (-238.7 ± 206.7 cGy, P < 10-5) with the adaptive plan. Normal tissue complication probabilities for xerostomia computed from mean parotid doses also decreased significantly with the adaptive plans (P = .008). We also observed systematic intratreatment volume reductions (ΔV) for GTVs and normal tissues. Candidate triggers were identified that predicted significant improvement with ART, including parotid ΔV = 7%, neck ΔV = 2%, and nodal GTV ΔV = 29%. CONCLUSIONS Systematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.
Collapse
Affiliation(s)
- Michalis Aristophanous
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Eric Aliotta
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Phillip Lichtenwalner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Shira Abraham
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mohammad Nehmeh
- Department of Applied Physics, Columbia University, New York, New York
| | - Amanda Caringi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Peng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daphna Gelblum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Sean McBride
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Linda Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yao Yu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kaveh Zakeri
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
2
|
Pazdrowski J, Gornowicz-Porowska J, Kaźmierska J, Krajka-Kuźniak V, Polanska A, Masternak M, Szewczyk M, Golusiński W, Danczak-Pazdrowska A. Radiation-induced skin injury in the head and neck region: pathogenesis, clinics, prevention, treatment considerations and proposal for management algorithm. Rep Pract Oncol Radiother 2024; 29:373-390. [PMID: 39144266 PMCID: PMC11321788 DOI: 10.5603/rpor.100775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 05/16/2024] [Indexed: 08/16/2024] Open
Abstract
Worldwide increase of head and neck cancers ranks these malignancies among top causes of cancer in human population. Radiation induced skin injury (RISI) is one of the major side effects of radiotherapy (RT). Skin of the neck is exposed to radiation due to necessity of therapeutic or prophylactic (elective) irradiation of neck lymph nodes and target organs, including the larynx and hypopharynx. The location of the neck exposes these regions of the skin to various additional exposomes such as ultraviolet radiation (UVR), pollution and cigarette smoke. There are many controversies or inconsistencies regarding RISI, from molecular aspects and therapy to terminology. There is lack of high-quality and large-sample studies in both forms of RISI: acute (aRISI) and chronic (cRISI). Finally, no gold standards in the management of aRISI and cRISI have been established yet. In this article, the authors discuss the pathogenesis, clinical picture, prevention and clinical interventions and present a proposed treatment algorithm.
Collapse
Affiliation(s)
- Jakub Pazdrowski
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Poznan, Poland
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan, Poland
| | - Justyna Gornowicz-Porowska
- Department and Division of Practical Cosmetology and Skin Diseases Prophylaxis, Poznan University of Medical Sciences, Poznan, Poland
| | - Joanna Kaźmierska
- Department of Radiotherapy, Poznan University of Medical Sciences, Poznan, Poland
- Radiotherapy and Oncology, Greater Poland Cancer Centre, Poznan, Poland
| | - Violetta Krajka-Kuźniak
- Department of Pharmaceutical Biochemistry, Poznan University of Medical Sciences, Poznan, Poland
| | - Adriana Polanska
- Department of Dermatology and Venereology Poznan University of Medical Sciences, Poznan, Poland
| | - Michał Masternak
- Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, Florida, United States
| | - Mateusz Szewczyk
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Poznan, Poland
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan, Poland
| | - Wojciech Golusiński
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Poznan, Poland
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan, Poland
| | | |
Collapse
|
3
|
Sreejeev AT, Joseph D, Krishnan AS, Pasricha R, Gupta S, Ahuja R, Sharma N, Sikdar D, Raut S, Sasi A, Gupta M. Weekly assessment of volumetric and dosimetric changes during volumetric modulated arc therapy of locally advanced head and neck carcinoma: Implications for adaptive radiation therapy-A prospective study. Head Neck 2024; 46:1547-1556. [PMID: 38436506 DOI: 10.1002/hed.27710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Chemoradiation in head and neck carcinoma (HNC) shows significant anatomical resulting in erroneous dose deposition in the target or the organ at risk (OAR). Adaptive radiotherapy (ART) can overcome this. Timing of significant target and OAR changes with dosimetric impact; thus, most suitable time and frequency of ART is unclear. METHODS This dosimetric study used prospective weekly non-contrast CT scans in 12 HNC patients (78 scans). OARs and TVs were manually contoured after registration with simulation scan. Dose overlay done on each scan without reoptimization. Dosimetric and volumetric variations assessed. RESULTS Commonest site was oropharynx. Gross Tumor Volume (GTV) reduced from 47.5 ± 19.2 to 17.8 ± 10.7 cc. Nodal GTV reduced from 15.7 ± 18.8 to 4.7 ± 7.1 cc. Parotid showed mean volume loss of 35%. T stage moderately correlated with GTV regression. CONCLUSION Maximum GTV changes occurred after 3 weeks. Best time to do single fixed interval ART would be by the end of 3 weeks.
Collapse
Affiliation(s)
| | - Deepa Joseph
- Department of Radiation Oncology, AIIMS, Rishikesh, India
| | - Ajay S Krishnan
- Department of Radiation Oncology, Mahamana Pandit Madan Mohan Malviya Cancer Centre, Varanasi, India
| | | | - Sweety Gupta
- Department of Radiation Oncology, AIIMS, Rishikesh, India
| | - Rachit Ahuja
- Department of Radiation Oncology, Shri Mahant Indiresh Hospital, Dehradun, India
| | - Nidhi Sharma
- Department of Radiation Oncology, AIIMS, Rishikesh, India
| | | | - Sagar Raut
- Department of Radiation Oncology, AIIMS, Rishikesh, India
| | | | - Manoj Gupta
- Department of Radiation Oncology, AIIMS, Rishikesh, India
| |
Collapse
|
4
|
Simopoulou F, Kyrgias G, Georgakopoulos I, Avgousti R, Armpilia C, Skarlos P, Softa V, Theodorou K, Kouloulias V, Zygogianni A. Does adaptive radiotherapy for head and neck cancer favorably impact dosimetric, clinical, and toxicity outcomes?: A review. Medicine (Baltimore) 2024; 103:e38529. [PMID: 38941415 DOI: 10.1097/md.0000000000038529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
PURPOSE The current review aims to summarize the international experience of the impact of adaptive radiotherapy on dosimetry and clinical and toxicity outcomes. Additionally, it might trigger Radiation Oncologists to use ART and evaluate whether ART improves target volume coverage and/or normal tissue sparing and, consequently, therapeutic results. MATERIALS AND METHODS We conducted an electronic literature search of PubMed/MEDLINE and ScienceDirect from January 2007 to January 2023. The search adhered to the PRISMA guidelines and employed keywords such as ART, HNC, parotid gland, and target volume. Furthermore, we examined the reference lists for studies pertinent to the present review. This study included both retrospective and prospective studies that were considered for inclusion. CONCLUSION ART replanning appears to be a sustainable strategy to minimize toxicity by improving normal tissue sparing. Furthermore, it can enhance target volume coverage by correctly determining the specific dose to be delivered to the tumor. In conclusion, this review confirmed that ART benefits dosimetric, clinical/therapeutic, and toxicity outcomes.
Collapse
Affiliation(s)
- Foteini Simopoulou
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieion University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| | - George Kyrgias
- Radiation Oncology Department, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Ioannis Georgakopoulos
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieion University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| | - Rafaela Avgousti
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieion University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieion University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| | - Pantelis Skarlos
- Radiation Oncology Department, Metropolitan Hospital, Piraeus, Greece
| | - Vasiliki Softa
- Medical Physics Department, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Kiki Theodorou
- Medical Physics Department, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieion University Hospital, Medical School, National and Kapodistrian University of Athens (NKUOA), Athens, Greece
| |
Collapse
|
5
|
Amstutz F, D'Almeida PG, Wu X, Albertini F, Bachtiary B, Weber DC, Unkelbach J, Lomax AJ, Zhang Y. Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments. Phys Med 2024; 122:103386. [PMID: 38805762 DOI: 10.1016/j.ejmp.2024.103386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 03/11/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024] Open
Abstract
PURPOSE Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties. METHODS For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneous-integrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC. RESULTS Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within ±5 % to the reference dose uncertainty across all patients. CONCLUSIONS DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.
Collapse
Affiliation(s)
- Florian Amstutz
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Peter G D'Almeida
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | - Xin Wu
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | | | | | - Damien C Weber
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Radiation Oncology, University Hospital Zurich, Switzerland; Department of Radiation Oncology, University Hospital Bern, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland.
| |
Collapse
|
6
|
Beckert R, Schiff JP, Morris E, Samson P, Kim H, Laugeman E. The impact of an Advanced Practice Radiation Therapist contouring for a CBCT-based adaptive radiotherapy program. Tech Innov Patient Support Radiat Oncol 2024; 30:100242. [PMID: 38495830 PMCID: PMC10940769 DOI: 10.1016/j.tipsro.2024.100242] [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: 11/17/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/19/2024] Open
Abstract
We successfully implemented an APRT specializing in CBCT-guided online adaptive contouring. These data show statistical improvements in contouring time with APRT-led vs non-APRT led ART contouring, suggesting that an APRT specifically trained to manage the ART process may reduce physician workload and patient treatment time.
Collapse
Affiliation(s)
- Robbie Beckert
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| | - Joshua P Schiff
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| | - Eric Morris
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| | - Pamela Samson
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| | - Hyun Kim
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| | - Eric Laugeman
- Department of Radiation Oncology Washington University School of Medicine in St. Louis, 4921 Parkview Place, MSC: 35-LL-8224, St. Louis, MO 63110, United States
| |
Collapse
|
7
|
McCullum LB, Karagoz A, Dede C, Garcia R, Nosrat F, Hemmati M, Hosseinian S, Schaefer AJ, Fuller CD. Markov models for clinical decision-making in radiation oncology: A systematic review. J Med Imaging Radiat Oncol 2024. [PMID: 38766899 DOI: 10.1111/1754-9485.13656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/03/2024] [Indexed: 05/22/2024]
Abstract
The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.
Collapse
Affiliation(s)
- Lucas B McCullum
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Aysenur Karagoz
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Cem Dede
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raul Garcia
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Fatemeh Nosrat
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Mehdi Hemmati
- School of Industrial and Systems Engineering, The University of Oklahoma, Norman, Oklahoma, USA
| | | | - Andrew J Schaefer
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| |
Collapse
|
8
|
Gan Y, Langendijk JA, Oldehinkel E, Lin Z, Both S, Brouwer CL. Optimal timing of re-planning for head and neck adaptive radiotherapy. Radiother Oncol 2024; 194:110145. [PMID: 38341093 DOI: 10.1016/j.radonc.2024.110145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND AND PURPOSE Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART. MATERIALS AND METHODS A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. The K-nearest-neighbour method was used for missing data imputation of weekly Dmean. A dose deviation map was built using the planning Dmean and weekly Dmean values and then used to simulate different ART scenarios consisting of 1 to 6 re-plannings. The difference between accumulated Dmean and planning Dmean before re-planning (ΔDmean_acc_noART) and after re-planning (ΔDmean_acc_ART) were evaluated and compared. RESULTS Among all the OARs, supraglottic showed the largest ΔDmean_acc_noART (1.23 ± 3.13 Gy) and most cases of ΔDmean_acc_noART > 3 Gy (26 patients). The 3rd week is suggested in the optimal timing of re-planning for 10 OARs. For all the organs except arytenoid, 2 re-plannings were able to guarantee the ΔDmean_acc_ART below 3 Gy while the average |ΔDmean_acc_ART| was below 1 Gy. ART scenarios of 2_4, 3_4, 3_5 (week of re-planning separated with "_") were able to guarantee ΔDmean_acc_ART of 99 % of patients below 3 Gy simultaneously for 19 OARs. CONCLUSIONS The optimal timing of re-planning was suggested for different organs at risk in head and neck adaptive radiotherapy. Generic scenarios of timing and frequency for re-planning can be applied to guarantee the increase of accumulated mean dose within 3 Gy simultaneously for multiple organs.
Collapse
Affiliation(s)
- Yong Gan
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands; Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China.
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Edwin Oldehinkel
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Zhixiong Lin
- Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China
| | - Stefan Both
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Charlotte L Brouwer
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| |
Collapse
|
9
|
Amstutz F, Krcek R, Bachtiary B, Weber DC, Lomax AJ, Unkelbach J, Zhang Y. Treatment planning comparison for head and neck cancer between photon, proton, and combined proton-photon therapy - From a fixed beam line to an arc. Radiother Oncol 2024; 190:109973. [PMID: 37913953 DOI: 10.1016/j.radonc.2023.109973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 09/25/2023] [Accepted: 10/26/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND PURPOSE This study investigates whether combined proton-photon therapy (CPPT) improves treatment plan quality compared to single-modality intensity-modulated radiation therapy (IMRT) or intensity-modulated proton therapy (IMPT) for head and neck cancer (HNC) patients. Different proton beam arrangements for CPPT and IMPT are compared, which could be of specific interest concerning potential future upright-positioned treatments. Furthermore, it is evaluated if CPPT benefits remain under inter-fractional anatomical changes for HNC treatments. MATERIAL AND METHODS Five HNC patients with a planning CT and multiple (4-7) repeated CTs were studied. CPPT with simultaneously optimized photon and proton fluence, single-modality IMPT, and IMRT treatment plans were optimized on the planning CT and then recalculated and reoptimized on each repeated CT. For CPPT and IMPT, plans with different degrees of freedom for the proton beams were optimized. Fixed horizontal proton beam line (FHB), gantry-like, and arc-like plans were compared. RESULTS The target coverage for CPPT without adaptation is insufficient (average V95%=88.4 %), while adapted plans can recover the initial treatment plan quality for target (average V95%=95.5 %) and organs-at-risk. CPPT with increased proton beam flexibility increases plan quality and reduces normal tissue complication probability of Xerostomia and Dysphagia. On average, Xerostomia NTCP reductions compared to IMRT are -2.7 %/-3.4 %/-5.0 % for CPPT FHB/CPPT Gantry/CPPT Arc. The differences for IMPT FHB/IMPT Gantry/IMPT Arc are + 0.8 %/-0.9 %/-4.3 %. CONCLUSION CPPT for HNC needs adaptive treatments. Increasing proton beam flexibility in CPPT, either by using a gantry or an upright-positioned patient, improves treatment plan quality. However, the photon component is substantially reduced, therefore, the balance between improved plan quality and costs must be further determined.
Collapse
Affiliation(s)
- Florian Amstutz
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Reinhardt Krcek
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | | | - Damien C Weber
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Radiation Oncology, University Hospital Zurich, Switzerland; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland.
| |
Collapse
|
10
|
Prime S, Schiff JP, Hosni A, Stanescu T, Dawson LA, Henke LE. The Use of MR-Guided Radiation Therapy for Liver Cancer. Semin Radiat Oncol 2024; 34:36-44. [PMID: 38105091 DOI: 10.1016/j.semradonc.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The role of radiotherapy in the management of primary and metastatic liver malignancies has expanded in recent years due to advances such as IGRT and SBRT. MRI-guided radiotherapy (MRgRT) has arisen as an excellent option for the management of hepatocellular carcinoma, cholangiocarcinoma, and liver metastases due to the ability to combine improved hepatic imaging with conformal treatment planning paradigms like adaptive radiotherapy and advanced motion management techniques. Herein we review the data for MRgRT for liver malignancies, as well as describe workflow and technical considerations for the 2 commercially available MRgRT delivery platforms.
Collapse
Affiliation(s)
- Sabrina Prime
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO
| | - Joshua P Schiff
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO
| | - Ali Hosni
- Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Teodor Stanescu
- Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Laura A Dawson
- Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Lauren E Henke
- University Hospitals/Case Western Reserve University, Department of Radiation Oncology, Cleveland, OH.
| |
Collapse
|
11
|
McDonald BA, Dal Bello R, Fuller CD, Balermpas P. The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance. Semin Radiat Oncol 2024; 34:69-83. [PMID: 38105096 DOI: 10.1016/j.semradonc.2023.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.
Collapse
Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| |
Collapse
|
12
|
Lindegaard AM, Håkansson K, Bernsdorf M, Gothelf AB, Kristensen CA, Specht L, Vogelius IR, Friborg J. A systematic review on clinical adaptive radiotherapy for head and neck cancer. Acta Oncol 2023; 62:1360-1368. [PMID: 37560990 DOI: 10.1080/0284186x.2023.2245555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
INTRODUCTION Head and neck cancer (HNC) patients' anatomy may undergo significant changes during radiotherapy (RT). This potentially affects dose distribution and compromises conformity between planned and delivered dose. Adaptive radiotherapy (ART) is a promising technique to overcome this problem but requires a significant workload. This systematic review aims to estimate the clinical and dosimetric benefits of ART using prospective data. MATERIAL AND METHODS A search on PubMed and Web of Science according to the PRISMA guidelines was made on Feb 6, 2023. Search string used was: 'adaptive radiotherapy head neck cancer'. English language filter was applied. All studies were screened for inclusion on title and abstract, and the full text was read and discussed in the research group in case of uncertainty. Inclusion criteria were a prospective ART strategy for HNC investigating clinical or dosimetric outcomes. RESULTS A total of 1251 articles were identified of which 15 met inclusion criteria. All included studies were published between 2010 and 2023 with a substantial diversity in design, endpoints, and nomenclature. The number of patients treated with ART was small with a median of 20 patients per study (range 4 to 86), undergoing 1-2 replannings. Mean dose to the parotid glands was reduced by 0.4-7.1 Gy. Maximum dose to the spinal cord was reduced by 0.5-4.6 Gy. Only five studies reported clinical outcome and disease control was excellent. Data on toxicity were ambiguous with some studies indicating reduced acute toxicity and xerostomia, while others found reduced quality of life in patients treated with ART. CONCLUSION The literature on clinical ART in HNC is limited. ART is associated with small reductions in doses to organs at risk, but the influence on toxicity and disease control is uncertain. There is a clear need for larger, prospective trials with a well-defined control group.
Collapse
Affiliation(s)
- Anne Marie Lindegaard
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Katrin Håkansson
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Mogens Bernsdorf
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Anita B Gothelf
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Claus A Kristensen
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Lena Specht
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Ivan R Vogelius
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Jeppe Friborg
- Department of Oncology, Centre for Cancer and Organ diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| |
Collapse
|
13
|
Kang J, Chowdhry AK, Pugh SL, Park JH. Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials. Semin Radiat Oncol 2023; 33:386-394. [PMID: 37684068 PMCID: PMC10880815 DOI: 10.1016/j.semradonc.2023.06.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh decision-making based on their best understanding of information at hand. These complex, multifactorial decisions have a tremendous opportunity to benefit from data-driven machine learning (ML) methods to drive opportunities in artificial intelligence (AI). Within the past 5 years, we have seen AI move from simply a promising opportunity to being used in prospective trials. Here, we review recent efforts of AI in clinical trials that have moved the needle towards improved prediction of actionable outcomes, such as predicting acute care visits, short term mortality, and pathologic extranodal extension. We then pause and reflect on how these AI models ask a different question than traditional statistics models that readers may be more familiar with; how then should readers conceptualize and interpret AI models that they are not as familiar with. We end with what we believe are promising future opportunities for AI in oncology, with an eye towards allowing the data to inform us through unsupervised learning and generative models, rather than asking AI to perform specific functions.
Collapse
Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Washington, Seattle, WA..
| | - Amit K Chowdhry
- Department of Radiation Oncology, University of Rochester, Rochester, NY
| | - Stephanie L Pugh
- American College of Radiology, NRG Oncology Statistics and Data Management Center, Philadelphia PA
| | - John H Park
- Department of Radiation Oncology, Kansas City VA Medical Center, Kansas City, MO.; Department of Radiology, University of Missouri Kansas City School of Medicine, Kansas City, MO
| |
Collapse
|
14
|
Yap LM, Jamalludin Z, Ng AH, Ung NM. A multi-center survey on adaptive radiation therapy for head and neck cancer in Malaysia. Phys Eng Sci Med 2023; 46:1331-1340. [PMID: 37470929 DOI: 10.1007/s13246-023-01303-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.
Collapse
Affiliation(s)
- Lai Mun Yap
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
- Department of Radiotherapy, Aurelius Hospital Nilai, 71800, Nilai, Malaysia
| | - Zulaikha Jamalludin
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Aik Hao Ng
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Ngie Min Ung
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| |
Collapse
|
15
|
Gan Y, Langendijk JA, van der Schaaf A, van den Bosch L, Oldehinkel E, Lin Z, Both S, Brouwer CL. An efficient strategy to select head and neck cancer patients for adaptive radiotherapy. Radiother Oncol 2023; 186:109763. [PMID: 37353058 DOI: 10.1016/j.radonc.2023.109763] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND AND PURPOSE Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy. MATERIALS AND METHODS This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of Dmean (ΔDmean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔDmean of OARs in week 1 and 2 (maxΔDmean_1 and maxΔDmean_2). Four strategies to select candidates for ART, using cut-off maxΔDmean were compared. RESULTS The Spearman's rank correlation test showed significant positive correlation between maxΔDmean and BIOΔNTCP (p-value <0.001). For major BIOΔNTCP (>5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔDmean_1 (3.01 and 5.14 Gy) and cut-off maxΔDmean_2 (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). CONCLUSIONS We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with >5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.
Collapse
Affiliation(s)
- Yong Gan
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China.
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Arjen van der Schaaf
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Lisa van den Bosch
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Edwin Oldehinkel
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Zhixiong Lin
- Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China
| | - Stefan Both
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Charlotte L Brouwer
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| |
Collapse
|
16
|
Price AT, Schiff JP, Laugeman E, Maraghechi B, Schmidt M, Zhu T, Reynoso F, Hao Y, Kim T, Morris E, Zhao X, Hugo GD, Vlacich G, DeSelm CJ, Samson PP, Baumann BC, Badiyan SN, Robinson CG, Kim H, Henke LE. Initial clinical experience building a dual CT- and MR-guided adaptive radiotherapy program. Clin Transl Radiat Oncol 2023; 42:100661. [PMID: 37529627 PMCID: PMC10388162 DOI: 10.1016/j.ctro.2023.100661] [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: 02/13/2023] [Revised: 06/12/2023] [Accepted: 07/20/2023] [Indexed: 08/03/2023] Open
Abstract
Introduction Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period. Materials and Methods The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform. Results From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART. Conclusions We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.
Collapse
Affiliation(s)
- Alex T. Price
- University Hospitals/Case Western Reserve University, Department of Radiation Oncology, Cleveland, OH, USA
| | - Joshua P. Schiff
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Eric Laugeman
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Borna Maraghechi
- City of Hope Orange County, Department of Radiation Oncology, Irvine, CA, USA
| | - Matthew Schmidt
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Tong Zhu
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Francisco Reynoso
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Yao Hao
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Taeho Kim
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Eric Morris
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Xiaodong Zhao
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Geoffrey D. Hugo
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Gregory Vlacich
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Carl J. DeSelm
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Pamela P. Samson
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Brian C. Baumann
- Springfield Clinic, Department of Radiation Oncology, Springfield, IL, USA
| | - Shahed N. Badiyan
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, USA
| | - Clifford G. Robinson
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Hyun Kim
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Lauren E. Henke
- University Hospitals/Case Western Reserve University, Department of Radiation Oncology, Cleveland, OH, USA
| |
Collapse
|
17
|
Gupta T, Maheshwari G, Joshi K, Sawant P, Mishra A, Khairnar S, Patel P, Sinha S, Swain M, Budrukkar A, Ghosh-Laskar S, Agarwal JP. Image-guidance triggered adaptive radiation therapy in head and neck squamous cell carcinoma: single-institution experience and implications for clinical practice. J Med Imaging Radiat Sci 2023; 54:88-96. [PMID: 36517346 DOI: 10.1016/j.jmir.2022.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE To report frequency and timing of adaptive radiotherapy (ART) and assess patient, disease, and treatment-related characteristics potentially triggering the need for such adaptive replanning in head and neck squamous cell carcinoma (HNSCC). METHODS Medical records of HNSCC patients treated with definitive intensity modulated radiation therapy (IMRT) with or without concurrent systemic chemotherapy were reviewed retrospectively to identify patients undergoing image-guidance triggered adaptive replanning. Clinico-demographic characteristics of patients undergoing ART were compared with patients treated without adaptation using the chi-square test. RESULTS Two hundred patients with squamous cell cancers of the oropharynx, larynx, or hypopharynx treated with definitive IMRT between 2014 to 2019 comprised the study cohort. Twenty-seven (13.5%) patients underwent adaptive replanning during treatment at a median of 17 fractions (inter-quartile range 14-24 fractions). There were no significant differences in the baseline patient (age, gender), disease (site of primary, staging/grouping), and treatment-related characteristics (dose-fractionation, chemotherapy usage) in patients undergoing ART compared to those treated without adaptation. Weight loss during IMRT emerged as a significant factor predicting the need for ART; patients having ≥10% weight loss from baseline were more likely to undergo treatment adaptation compared to patients with <10% weight loss (p = 0.0002). There was variable impact of ART on dose-volume statistics of organs-at-risk such parotid glands and spinal cord. CONCLUSION Image-guidance triggered ART for HNSCC is not associated with significant improvement in OAR dosimetry. However, weight loss during definitive IMRT can be a potentially useful trigger for identifying patients who are most likely to benefit from such adaptive replanning.
Collapse
Affiliation(s)
- Tejpal Gupta
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India.
| | - Guncha Maheshwari
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Kishore Joshi
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Priya Sawant
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Ajay Mishra
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Sunil Khairnar
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Prapti Patel
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Shwetabh Sinha
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Monali Swain
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Ashwini Budrukkar
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Sarbani Ghosh-Laskar
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Jai-Prakash Agarwal
- Department of 1Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| |
Collapse
|
18
|
Yen A, Choi B, Inam E, Yeh A, Lin MH, Park C, Hrycushko B, Nwachukwu C, Albuquerque K. Spare the Bowel, Don't Spoil the Target: Optimal Margin Assessment for Online Cone Beam Adaptive Radiation Therapy (OnC-ART) of the Cervix. Pract Radiat Oncol 2023; 13:e176-e183. [PMID: 36356834 DOI: 10.1016/j.prro.2022.10.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/13/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE The standard treatment for locally advanced cervical cancer involves pelvic chemoradiation. Intensity modulated radiation therapy planning requires expansion of the cervix and uterus clinical target volume (CTV) by 1.5 to 2 cm to account for motion. With online cone beam adaptive radiation therapy (OnC-ART), interfractional movement is accounted for, which can potentially lead to smaller CTV to planned target volume (PTV) margins. In this study, we attempted to determine the optimal CTV-to-PTV margin for adequate coverage with OnC-ART and factors that can affect CTV coverage. METHODS AND MATERIALS A retrospective cohort of 21 patients with cervical cancer treated with definitive chemoradiation was included. Nine patients treated with nonadaptive radiation had the uterocervix contoured on pretreatment cone beam computed tomography (CBCT) and end-treatment CBCTs. Anterior-posterior, lateral, and superior-inferior shifts and the average shift in all directions were calculated. A CTV-to-PTV expansion was determined and verified on a validation cohort of 12 patients treated with OnC-ART. RESULTS The average anterior-posterior, lateral, and superior-inferior shifts with standard deviation were 0.32 ± 1.55 cm, 0.12 ± 2.31 cm, and 1.67 ± 3.41 cm, respectively. A uniform 5-mm expansion around the pretreatment CTV covered 98.85% ± 1.23% of the end-treatment CTV. This 5-mm expansion was applied to our validation cohort treated with OnC-ART, and 98.39% ± 3.0% of the end-treatment CTV was covered. Time between CBCTs >30 minutes and change in bladder volume were significantly correlated to CTV coverage. CONCLUSIONS Based on our analysis, a CTV-to-PTV margin of 5 mm is adequate to encompass 98% of the CTV. A significantly reduced margin could potentially decrease the toxicities associated with radiation for patients with cervical cancer and lead to improved patient reported toxicity outcomes. We recommend physicians begin with a 5-mm margin and assess adequate coverage with image guidance during daily adaptation.
Collapse
Affiliation(s)
- Allen Yen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Byongsu Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Enobang Inam
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Austin Yeh
- College of Natural Science, University of Texas at Austin, Austin, Texas
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Chunjoo Park
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida
| | - Brian Hrycushko
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Chika Nwachukwu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Kevin Albuquerque
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
| |
Collapse
|
19
|
Otsuka M, Yasuda K, Uchinami Y, Tsushima N, Suzuki T, Kano S, Suzuki R, Miyamoto N, Minatogawa H, Dekura Y, Mori T, Nishioka K, Taguchi J, Shimizu Y, Katoh N, Homma A, Aoyama H. Detailed analysis of failure patterns using deformable image registration in hypopharyngeal cancer patients treated with sequential boost intensity-modulated radiotherapy. J Med Imaging Radiat Oncol 2023; 67:98-110. [PMID: 36373823 DOI: 10.1111/1754-9485.13491] [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: 03/29/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Sequential boost intensity-modulated radiotherapy (SQB-IMRT) uses two different planning CTs (pCTs) and treatment plans. SQB-IMRT is a form of adaptive radiotherapy that allows for responses to changes in the shape of the tumour and organs at risk (OAR). On the other hand, dose accumulation with the two plans can be difficult to evaluate. The purpose of this study was to analyse patterns of loco-regional failure using deformable image registration (DIR) in hypopharyngeal cancer patients treated with SQB-IMRT. METHODS Between 2013 and 2019, 102 patients with hypopharyngeal cancer were treated with definitive SQB-IMRT at our institution. Dose accumulation with the 1st and 2nd plans was performed, and the dose to the loco-regional recurrent tumour volume was calculated using the DIR workflow. Failure was classified as follows: (i) in-field (≥95% of the recurrent tumour volume received 95% of the prescribed dose); (ii) marginal (20-95%); or (iii) out-of-field (<20%). RESULTS After a median follow-up period of 25 months, loco-regional failure occurred in 34 patients. Dose-volume histogram analysis showed that all loco-regional failures occurred in the field within 95% of the prescribed dose, with no marginal or out-of-field recurrences observed. CONCLUSION The dosimetric analysis using DIR showed that all loco-regional failures were within the high-dose region. More aggressive treatment may be required for gross tumours.
Collapse
Affiliation(s)
- Manami Otsuka
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan.,Department of Radiation Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Koichi Yasuda
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Uchinami
- Department of Radiation Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Nayuta Tsushima
- Department of Otolaryngology-Head and Neck Surgery, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Takayoshi Suzuki
- Department of Otolaryngology-Head and Neck Surgery, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Ryusuke Suzuki
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| | - Naoki Miyamoto
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| | - Hideki Minatogawa
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan
| | - Yasuhiro Dekura
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan
| | - Takashi Mori
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan
| | - Kentaro Nishioka
- Department of Radiation Medical Science and Engineering, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Jun Taguchi
- Department of Medical Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yasushi Shimizu
- Department of Medical Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Norio Katoh
- Department of Radiation Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hidefumi Aoyama
- Department of Radiation Oncology, Hokkaido University Hospital, Sapporo, Japan.,Department of Radiation Oncology, Faculty and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| |
Collapse
|
20
|
McDonald BA, Zachiu C, Christodouleas J, Naser MA, Ruschin M, Sonke JJ, Thorwarth D, Létourneau D, Tyagi N, Tadic T, Yang J, Li XA, Bernchou U, Hyer DE, Snyder JE, Bubula-Rehm E, Fuller CD, Brock KK. Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation. Front Oncol 2023; 12:1086258. [PMID: 36776378 PMCID: PMC9909539 DOI: 10.3389/fonc.2022.1086258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023] Open
Abstract
MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.
Collapse
Affiliation(s)
- Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Tuebingen, Germany
| | - Daniel Létourneau
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Daniel E. Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Jeffrey E. Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|
21
|
Bäumer C, Frakulli R, Kohl J, Nagaraja S, Steinmeier T, Worawongsakul R, Timmermann B. Adaptive Proton Therapy of Pediatric Head and Neck Cases Using MRI-Based Synthetic CTs: Initial Experience of the Prospective KiAPT Study. Cancers (Basel) 2022; 14:cancers14112616. [PMID: 35681594 PMCID: PMC9179385 DOI: 10.3390/cancers14112616] [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: 03/11/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND AND PURPOSE Interfractional anatomical changes might affect the outcome of proton therapy (PT). We aimed to prospectively evaluate the role of Magnetic Resonance Imaging (MRI) based adaptive PT for children with tumors of the head and neck and base of skull. METHODS MRI verification images were acquired at half of the treatment course. A synthetic computed tomography (CT) image was created using this MRI and a deformable image registration (DIR) to the reference MRI. The methodology was verified with in-silico phantoms and validated using a clinical case with a shrinking cystic hygroma on the basis of dosimetric quantities of contoured structures. The dose distributions on the verification X-ray CT and on the synthetic CT were compared with a gamma-index test using global 2 mm/2% criteria. RESULTS Regarding the clinical validation case, the gamma-index pass rate was 98.3%. Eleven patients were included in the clinical study. The most common diagnosis was rhabdomyosarcoma (73%). Craniofacial tumor site was predominant in 64% of patients, followed by base of skull (18%). For one individual case the synthetic CT showed an increase in the median D2 and Dmax dose on the spinal cord from 20.5 GyRBE to 24.8 GyRBE and 14.7 GyRBE to 25.1 GyRBE, respectively. Otherwise, doses received by OARs remained relatively stable. Similarly, the target volume coverage seen by D95% and V95% remained unchanged. CONCLUSIONS The method of transferring anatomical changes from MRIs to a synthetic CTs was successfully implemented and validated with simple, commonly available tools. In the frame of our early results on a small cohort, no clinical relevant deterioration for neither PTV coverage nor an increased dose burden to OARs occurred. However, the study will be continued to identify a pediatric patient cohort, which benefits from adaptive treatment planning.
Collapse
Affiliation(s)
- Christian Bäumer
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Department of Physics, Technische Universität Dortmund, 44227 Dortmund, Germany
- Correspondence:
| | - Rezarta Frakulli
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Jessica Kohl
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
| | - Sindhu Nagaraja
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Theresa Steinmeier
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Rasin Worawongsakul
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
- Radiation Oncology Unit, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Nakhon 73170, Thailand
| | - Beate Timmermann
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| |
Collapse
|
22
|
MR-Guided Adaptive Radiotherapy for OAR Sparing in Head and Neck Cancers. Cancers (Basel) 2022; 14:cancers14081909. [PMID: 35454816 PMCID: PMC9028510 DOI: 10.3390/cancers14081909] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Normal tissue toxicities in head and neck cancer persist as a cause of decreased quality of life and are associated with poorer treatment outcomes. The aim of this article is to review organ at risk (OAR) sparing approaches available in MR-guided adaptive radiotherapy and present future developments which hope to improve treatment outcomes. Increasing the spatial conformity of dose distributions in radiotherapy is an important first step in reducing normal tissue toxicities, and MR-guided treatment devices presents a new opportunity to use biological information to drive treatment decisions on a personalized basis. Abstract MR-linac devices offer the potential for advancements in radiotherapy (RT) treatment of head and neck cancer (HNC) by using daily MR imaging performed at the time and setup of treatment delivery. This article aims to present a review of current adaptive RT (ART) methods on MR-Linac devices directed towards the sparing of organs at risk (OAR) and a view of future adaptive techniques seeking to improve the therapeutic ratio. This ratio expresses the relationship between the probability of tumor control and the probability of normal tissue damage and is thus an important conceptual metric of success in the sparing of OARs. Increasing spatial conformity of dose distributions to target volume and OARs is an initial step in achieving therapeutic improvements, followed by the use of imaging and clinical biomarkers to inform the clinical decision-making process in an ART paradigm. Pre-clinical and clinical findings support the incorporation of biomarkers into ART protocols and investment into further research to explore imaging biomarkers by taking advantage of the daily MR imaging workflow. A coherent understanding of this road map for RT in HNC is critical for directing future research efforts related to sparing OARs using image-guided radiotherapy (IGRT).
Collapse
|
23
|
Hirotaki K, Moriya S, Tachibana H, Sakae T. Detection of anatomical changes using two-dimensional X-ray images for head and neck adaptive radiotherapy. Med Phys 2022; 49:3288-3297. [PMID: 35235222 DOI: 10.1002/mp.15587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/22/2022] [Accepted: 02/23/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To develop a system for detecting anatomical changes using two-dimensional (2D) X-ray images. METHODS Ten patients with head and neck cancer were retrospectively analyzed using 2D x-ray and cone-beam computed tomography (CBCT) images. The 2D x-ray images were acquired daily, whereas the CBCT images were acquired weekly during the treatment period. The developed system imported the 2D x-ray images obtained on the initial treatment day and on another day, and thereafter converted them into the water equivalent thickness (WET) using the conversion table. The difference between the WET images for the 1st and other treatment days (ΔWET) was calculated as the quantitative value for anatomical changes and visualized to recognize the anatomical change location. We compared ΔWET and the difference in the lateral neck distance (ΔLND) on the corresponding CBCT images. ΔLND was used as the ground truth for anatomical changes. ΔWET and ΔLND were measured at the first cervical vertebra (C1) and the tumor center (TC). C1 and TC were selected to observe the volume changes in the parotid gland and tumor, respectively. Sensitivity and specificity were calculated to evaluate the performance of the 2D-WET system. The cutoff values of WET and LND were set to 2-10 mm. Furthermore, intensity-modulated proton therapy (IMPT) plans for six patients with rescan CT images were generated. The IMPT plans on the rescan CT images were compared to the original plans on simulation CT using the dosimetric parameters for the target and the organs at risk (OARs). RESULTS The mean differences between ΔWET and ΔLND for C1 and TC were -0.62 ± 1.66 mm and -0.93 ± 1.28 mm (mean ± 1SD), respectively. ΔWET in the proposed system was in good agreement with ΔLND using the CBCT images. In the sensitivity and specificity results for C1 and TC with cut-off values from 2 mm to 10 mm, the sensitivity was >85% for all cut-off values, while the specificity was > 90% at 5-10 mm and < 90% at less than 5 mm. The average ΔWET at the time of replanning was 12.8 mm which resulted in maximum dose increase in the spinal cord D1cc by 8.4 Gy, the parotid gland D50 by 26.6 Gy, and the oral cavity D50 by 23.2 Gy. CONCLUSIONS We developed a new system for detecting anatomical changes using 2D x-ray images. The developed system with ΔWET showed an agreement with ΔLND at C1 and TC with an average difference of less than 1 mm. ΔWET detected anatomical changes with high sensitivity and specificity with a cut-off value of 5-10 mm. This system can monitor daily anatomical changes without causing high exposure to patients and requiring any inefficient work, and it can be applied to daily online adaptive PBT and triggered adaptive radiotherapy. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Kouta Hirotaki
- Doctoral Program in Medical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, 3058575, Japan.,Department of Radiological Technology, National Cancer Center Hospital East, Chiba, 2778577, Japan
| | - Shunsuke Moriya
- Faculty of Medicine, University of Tsukuba, Ibaraki, 3058575, Japan
| | - Hidenobu Tachibana
- Section of Radiation Safety and Quality Assurance, National Cancer Center Hospital East, Chiba, 2778577, Japan
| | - Takeji Sakae
- Faculty of Medicine, University of Tsukuba, Ibaraki, 3058575, Japan
| |
Collapse
|
24
|
Siciarz P, McCurdy B, Hanumanthappa N, Van Uytven E. Adaptive radiation therapy strategies in the treatment of prostate cancer patients using hypofractionated VMAT. J Appl Clin Med Phys 2021; 22:7-26. [PMID: 34787360 PMCID: PMC8664140 DOI: 10.1002/acm2.13415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 07/21/2021] [Accepted: 08/20/2021] [Indexed: 11/13/2022] Open
Abstract
PURPOSE To perform a comprehensive evaluation of eight adaptive radiation therapy strategies in the treatment of prostate cancer patients who underwent hypofractionated volumetric modulated arc therapy (VMAT) treatment. MATERIAL AND METHODS The retrospective study included 20 prostate cancer patients treated with 40 Gy total dose over five fractions (8 Gy/fraction) using VMAT. Daily cone beam computed tomography images were acquired before the delivery of every fraction and then, with the application of deformable image registration used for the estimation of daily dose, contouring and plan re-optimization. Dosimetric benefits of the various ART strategies were quantified by the comparison of dose and dose-volume metrics derived from treatment planning objectives for original treatment plan and adapted plans with the consideration of target volumes (PTV and CTV) as well as critical structures (bladder, rectum, left, and right femoral heads). RESULTS Percentage difference (ΔD) between planning objectives and delivered dose in the D99% > 4000cGy (CTV) metric was -3.9% for the non-ART plan and 2.1% to 4.1% for ART plans. For D99% > 3800cGy and Dmax < 4280cGy (PTV), ΔD was -11.2% and -6.5% for the non-ART plan as well as -3.9% to -1.6% and -0.2% to 1.8% for ART plans, respectively. For D15% < 3200 cGy and D20% < 2800 cGy (bladder), ΔD was -62.4% and -68.8% for the non-ART plan as well as -60.0% to -57.4% and -67.0% to -64.0% for ART plans. For D15% < 3200 cGy and D20% < 2800 cGy (rectum), ΔD was -11.4% and -8.15% for non-ART plan as well as -14.9% to -9.0% and -11.8% to -5.1% for ART plans. CONCLUSIONS Daily on-line adaptation approaches were the most advantageous, although strategies adapting every other fraction were also impactful while reducing relative workload as well. Offline treatment adaptations were shown to be less beneficial due to increased dose delivered to bladder and rectum compared toother ART strategies.
Collapse
Affiliation(s)
- Pawel Siciarz
- Department of Medical PhysicsCancerCare ManitobaWinnipegManitobaCanada
- Department of Physics and AstronomyUniversity of ManitobaWinnipegManitobaCanada
| | - Boyd McCurdy
- Department of Medical PhysicsCancerCare ManitobaWinnipegManitobaCanada
- Department of Physics and AstronomyUniversity of ManitobaWinnipegManitobaCanada
- Department of RadiologyUniversity of ManitobaWinnipegManitobaCanada
| | | | - Eric Van Uytven
- Department of Medical PhysicsCancerCare ManitobaWinnipegManitobaCanada
| |
Collapse
|
25
|
Paganetti H, Botas P, Sharp GC, Winey B. Adaptive proton therapy. Phys Med Biol 2021; 66:10.1088/1361-6560/ac344f. [PMID: 34710858 PMCID: PMC8628198 DOI: 10.1088/1361-6560/ac344f] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/28/2021] [Indexed: 12/25/2022]
Abstract
Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.
Collapse
Affiliation(s)
- Harald Paganetti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pablo Botas
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Foundation 29 of February, Pozuelo de Alarcón, Madrid, Spain
| | - Gregory C Sharp
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brian Winey
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| |
Collapse
|
26
|
Pakela JM, Matuszak MM, Ten Haken RK, McShan DL, El Naqa I. Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer. Phys Med Biol 2021; 66. [PMID: 34587597 DOI: 10.1088/1361-6560/ac2b80] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 09/29/2021] [Indexed: 01/01/2023]
Abstract
Objective.Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on pre-learned patterns from a well-defined patient population.Approach.Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new joint framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance characteristics of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores.Main results.The MRNN framework had slightly better performance than the QRNN framework, with MRNN (QRNN) validation AUC scores of 0.742±0.021 (0.675±0.036), 0.709±0.026 (0.656±0.021), 0.724±0.036 (0.652±0.044), and 0.698±0.016 (0.605±0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, only the results from the two higher order states had statistically significant differences(p<0.05).A similar trend was also observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN (QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively.Significance.These results suggest that both stochastic models have potential value in predicting patient changes during the course of adaptive radiotherapy.
Collapse
Affiliation(s)
- Julia M Pakela
- Applied Physics Program, University of Michigan, Ann Arbor, MI, United States of America.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Daniel L McShan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Issam El Naqa
- Applied Physics Program, University of Michigan, Ann Arbor, MI, United States of America.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| |
Collapse
|
27
|
Weppler S, Quon H, Schinkel C, Yarschenko A, Barbera L, Harjai N, Smith W. Patient-Reported Outcomes-Guided Adaptive Radiation Therapy for Head and Neck Cancer. Front Oncol 2021; 11:759724. [PMID: 34737963 PMCID: PMC8560706 DOI: 10.3389/fonc.2021.759724] [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: 08/16/2021] [Accepted: 09/20/2021] [Indexed: 01/12/2023] Open
Abstract
Purpose To identify which patient-reported outcomes (PROs) may be most improved through adaptive radiation therapy (ART) with the goal of reducing toxicity incidence among head and neck cancer patients. Methods One hundred fifty-five head and neck cancer patients receiving radical VMAT (chemo)radiotherapy (66-70 Gy in 30-35 fractions) completed the MD Anderson Symptom Inventory, MD Anderson Dysphagia Inventory (MDADI), and Xerostomia Questionnaire while attending routine follow-up clinics between June-October 2019. Hierarchical clustering characterized symptom endorsement. Conventional statistical approaches indicated associations between dose and commonly reported symptoms. These associations, and the potential benefit of interfractional dose corrections, were further explored via logistic regression. Results Radiotherapy-related symptoms were commonly reported (dry mouth, difficulty swallowing/chewing). Clustering identified three patient subgroups reporting: none/mild symptoms for most items (60.6% of patients); moderate/severe symptoms affecting some aspects of general well-being (32.9%); and moderate/severe symptom reporting for most items (6.5%). Clusters of PRO items broadly consisted of acute toxicities, general well-being, and head and neck-specific symptoms (xerostomia, dysphagia). Dose-PRO relationships were strongest between delivered pharyngeal constrictor Dmean and patient-reported dysphagia, with MDADI composite scores (mean ± SD) of 25.7 ± 18.9 for patients with Dmean <50 Gy vs. 32.4 ± 17.1 with Dmean ≥50 Gy. Based on logistic regression models, during-treatment dose corrections back to planned values may confer ≥5% decrease in the absolute risk of self-reported physical dysphagia symptoms ≥1 year post-treatment in 1.2% of patients, with a ≥5% decrease in relative risk in 23.3% of patients. Conclusions Patient-reported dysphagia symptoms are strongly associated with delivered dose to the pharyngeal constrictor. Dysphagia-focused ART may provide the greatest toxicity benefit to head and neck cancer patients, and represent a potential new direction for ART, given that the existing ART literature has focused almost exclusively on xerostomia reduction.
Collapse
Affiliation(s)
- Sarah Weppler
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Harvey Quon
- Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Colleen Schinkel
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Adam Yarschenko
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Mechanical Engineering, University of Calgary, Calgary, AB, Canada
| | - Lisa Barbera
- Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Nabhya Harjai
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Wendy Smith
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
28
|
Rong Y, Rosu-Bubulac M, Benedict SH, Cui Y, Ruo R, Connell T, Kashani R, Latifi K, Chen Q, Geng H, Sohn J, Xiao Y. Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation. Pract Radiat Oncol 2021; 11:282-298. [PMID: 33662576 PMCID: PMC8406084 DOI: 10.1016/j.prro.2021.02.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/05/2021] [Accepted: 02/16/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning. METHODS AND MATERIALS The American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology. RESULTS AND CONCLUSIONS The NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.
Collapse
Affiliation(s)
- Yi Rong
- Department of Radiation Oncology, University of California Davis Cancer Center, Sacramento, California; Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
| | - Mihaela Rosu-Bubulac
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Cancer Center, Sacramento, California
| | - Yunfeng Cui
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Russell Ruo
- Department of Medical Physics, McGill University Health Center, Montreal, QC, Canada
| | - Tanner Connell
- Department of Medical Physics, McGill University Health Center, Montreal, QC, Canada
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky
| | - Huaizhi Geng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jason Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| |
Collapse
|
29
|
Cagni E, Botti A, Chendi A, Iori M, Spezi E. Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy. Phys Med Biol 2021; 66. [PMID: 34098549 DOI: 10.1088/1361-6560/ac08b0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/07/2021] [Indexed: 11/12/2022]
Abstract
This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate the amount of sparing in organs at risk (OARs) in the re-planning strategy for adaptive radiotherapy (ART). Eighty head and neck (HN) VMAT Pareto plans from our institute's database were used to train a knowledge-based planning (KBP) model. An evaluation set of another 20 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT, corresponding to the middle and second half of the radiotherapy treatment course, were extracted. The original plan was re-calculated on a daily deformed CT (delivered dose-volume histogram (DVH)) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH uncertainties was compared with the gains obtained from re-planning. DVH differences and receiver operating characteristic (ROC) curve analysis were used for this purpose. On average, final KBP uncertainties were smaller than the gain in re-planning. Statistical tests confirmed significant differences between the two groups. ROC analysis showed KBP performance in terms of area under the curve values higher than 0.7, which confirmed a good accuracy in predicted values. Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 47% of cases. We have established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, ART process.
Collapse
Affiliation(s)
- Elisabetta Cagni
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.,School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Andrea Botti
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Agnese Chendi
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.,Department of Medical Physics, Alma Mater Studiorum Bologna University, Bologna, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, United Kingdom
| |
Collapse
|
30
|
Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review. JOURNAL OF ONCOLOGY 2021; 2021:5566508. [PMID: 34211551 PMCID: PMC8211491 DOI: 10.1155/2021/5566508] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/18/2021] [Accepted: 05/25/2021] [Indexed: 12/24/2022]
Abstract
Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches. MATERIALS AND METHODS A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported. RESULTS Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases. CONCLUSIONS Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.
Collapse
|
31
|
Weppler S, Quon H, Schinkel C, Ddamba J, Harjai N, Vigal C, Beers CA, Van Dyke L, Smith W. Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic. Front Oncol 2021; 11:650335. [PMID: 34164338 PMCID: PMC8216638 DOI: 10.3389/fonc.2021.650335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/04/2021] [Indexed: 12/26/2022] Open
Abstract
Purpose To determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines. Methods We considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal cord, parotid glands, and pharyngeal constrictor, as well as prediction of patient weight loss. Two-hundred head and neck cancer patients were used for model development and an additional 50 for model validation. Patient chart data, pre-treatment images, treatment plans, on-unit patient measurements, and combinations thereof were assessed as potential predictors of each objective. A stepwise approach identified combinations of predictors maximizing the Youden index of random forest (RF) models. A heuristic translated RF results into simple patient selection guidelines which were further refined to balance predictive capability and practical resource costs. Generalizability of the RF models and simplified guidelines to new data was tested using the validation set. Results Top performing RF models used various categories of predictors, however, final simplified patient selection guidelines only required pre-treatment information for ART predictions, indicating the potential for significant ART process streamlining. The simplified guidelines for each objective predicted which patients would experience increases in dose to: brainstem/spinal cord with sensitivity = 1.0, specificity = 0.66; parotid glands with sensitivity = 0.82, specificity = 0.70; and pharyngeal constrictor with sensitivity = 0.84, specificity = 0.68. Weight loss could be predicted with sensitivity = 0.60 and specificity = 0.55. Furthermore, depending on the ART objective, 28%-58% of patients required replan assessment, less than for previous studies, indicating a step towards more effective patient selection. Conclusions The above ART objectives appear to be practically achievable, with patients selected for ART according to simple clinical patient selection guidelines. Explicit ART guidelines are rare in the literature, and our guidelines may aid in balancing the potential clinical gains of ART with high associated resource costs, formalizing ART trials, and ensuring the reproducibility of clinical successes.
Collapse
Affiliation(s)
- Sarah Weppler
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Harvey Quon
- Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Colleen Schinkel
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - James Ddamba
- Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Nabhya Harjai
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Clarisse Vigal
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| | - Craig A Beers
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lukas Van Dyke
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Wendy Smith
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
32
|
Lafata KJ, Chang Y, Wang C, Mowery YM, Vergalasova I, Niedzwiecki D, Yoo DS, Liu JG, Brizel DM, Yin FF. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. Med Phys 2021; 48:3767-3777. [PMID: 33959972 DOI: 10.1002/mp.14926] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/26/2021] [Accepted: 04/25/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS). MATERIALS AND METHODS Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. RESULTS While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. CONCLUSIONS Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.
Collapse
Affiliation(s)
- Kyle J Lafata
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yushi Chang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Donna Niedzwiecki
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - David S Yoo
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Jian-Guo Liu
- Department of Mathematics, Duke University, Durham, NC, USA.,Department of Physics, Duke University, Durham, NC, USA
| | - David M Brizel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| |
Collapse
|
33
|
Chamberlain M, Krayenbuehl J, van Timmeren JE, Wilke L, Andratschke N, Garcia Schüler H, Tanadini-Lang S, Guckenberger M, Balermpas P. Head and neck radiotherapy on the MR linac: a multicenter planning challenge amongst MRIdian platform users. Strahlenther Onkol 2021; 197:1093-1103. [PMID: 33891126 PMCID: PMC8604891 DOI: 10.1007/s00066-021-01771-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/22/2021] [Indexed: 11/30/2022]
Abstract
Purpose Purpose of this study is to evaluate plan quality on the MRIdian (Viewray Inc., Oakwood Village, OH, USA) system for head and neck cancer (HNC) through comparison of planning approaches of several centers. Methods A total of 14 planners using the MRIdian planning system participated in this treatment challenge, centrally organized by ViewRay, for one contoured case of oropharyngeal carcinoma with standard constraints for organs at risk (OAR). Homogeneity, conformity, sparing of OARs, and other parameters were evaluated according to The International Commission on Radiation Units and Measurements (ICRU) recommendations anonymously, and then compared between centers. Differences amongst centers were assessed by means of Wilcoxon test. Each plan had to fulfil hard constraints based on dose–volume histogram (DVH) parameters and delivery time. A plan quality metric (PQM) was evaluated. The PQM was defined as the sum of 16 submetrics characterizing different DVH goals. Results For most dose parameters the median score of all centers was higher than the threshold that results in an ideal score. Six participants achieved the maximum number of points for the OAR dose parameters, and none had an unacceptable performance on any of the metrics. Each planner was able to achieve all the requirements except for one which exceeded delivery time. The number of segments correlated to improved PQM and inversely correlated to brainstem D0.1cc and to Planning Target Volume1 (PTV) D0.1cc. Total planning experience inversely correlated to spinal canal dose. Conclusion Magnetic Resonance Image (MRI) linac-based planning for HNC is already feasible with good quality. Generally, an increased number of segments and increasing planning experience are able to provide better results regarding planning quality without significantly prolonging overall treatment time. Supplementary Information The online version of this article (10.1007/s00066-021-01771-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Madalyne Chamberlain
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.
| | - Jerome Krayenbuehl
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | - Lotte Wilke
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | | | | | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| |
Collapse
|
34
|
McDonald BA, Vedam S, Yang J, Wang J, Castillo P, Lee B, Sobremonte A, Ahmed S, Ding Y, Mohamed ASR, Balter P, Hughes N, Thorwarth D, Nachbar M, Philippens MEP, Terhaard CHJ, Zips D, Böke S, Awan MJ, Christodouleas J, Fuller CD. Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation. Int J Radiat Oncol Biol Phys 2021; 109:1606-1618. [PMID: 33340604 PMCID: PMC7965360 DOI: 10.1016/j.ijrobp.2020.12.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/04/2020] [Accepted: 12/11/2020] [Indexed: 01/20/2023]
Abstract
PURPOSE This prospective study is, to our knowledge, the first report of daily adaptive radiation therapy (ART) for head and neck cancer (HNC) using a 1.5T magnetic resonance imaging-linear accelerator (MR-linac) with particular focus on safety and feasibility and dosimetric results of an online rigid registration-based adapt to position (ATP) workflow. METHODS AND MATERIALS Ten patients with HNC received daily ART on a 1.5T/7MV MR-linac, 6 using ATP only and 4 using ATP with 1 offline adapt-to-shape replan. Setup variability with custom immobilization masks was assessed by calculating the mean systematic error (M), standard deviation of the systematic error (Σ), and standard deviation of the random error (σ) of the isocenter shifts. Quality assurance was performed with a cylindrical diode array using 3%/3 mm γ criteria. Adaptive treatment plans were summed for each patient to compare the delivered dose with the planned dose from the reference plan. The impact of dosimetric variability between adaptive fractions on the summation plan doses was assessed by tracking the number of optimization constraint violations at each individual fraction. RESULTS The random errors (mm) for the x, y, and z isocenter shifts, respectively, were M = -0.3, 0.7, 0.1; Σ = 3.3, 2.6, 1.4; and σ = 1.7, 2.9, 1.0. The median (range) γ pass rate was 99.9% (90.9%-100%). The differences between the reference and summation plan doses were -0.61% to 1.78% for the clinical target volume and -11.74% to 8.11% for organs at risk (OARs), although an increase greater than 2% in OAR dose only occurred in 3 cases, each for a single OAR. All cases had at least 2 fractions with 1 or more constraint violations. However, in nearly all instances, constraints were still met in the summation plan despite multiple single-fraction violations. CONCLUSIONS Daily ART on a 1.5T MR-linac using an online ATP workflow is safe and clinically feasible for HNC and results in delivered doses consistent with planned doses.
Collapse
Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, Texas
| | - Sastry Vedam
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Pamela Castillo
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Belinda Lee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Angela Sobremonte
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sara Ahmed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yao Ding
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, Texas
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Neil Hughes
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | | | - Chris H J Terhaard
- Department of Radiotherapy, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Simon Böke
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Musaddiq J Awan
- Department of Radiation Oncology, Medical College of Wisconsin, Wauwatosa, Wisconsin
| | - John Christodouleas
- Elekta, Inc., Stockholm, Sweden; Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| |
Collapse
|
35
|
Boeke S, Mönnich D, van Timmeren JE, Balermpas P. MR-Guided Radiotherapy for Head and Neck Cancer: Current Developments, Perspectives, and Challenges. Front Oncol 2021; 11:616156. [PMID: 33816247 PMCID: PMC8017313 DOI: 10.3389/fonc.2021.616156] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 02/01/2021] [Indexed: 02/06/2023] Open
Abstract
Based on the development of new hybrid machines consisting of an MRI and a linear accelerator, magnetic resonance image guided radiotherapy (MRgRT) has revolutionized the field of adaptive treatment in recent years. Although an increasing number of studies have been published, investigating technical and clinical aspects of this technique for various indications, utilizations of MRgRT for adaptive treatment of head and neck cancer (HNC) remains in its infancy. Yet, the possible benefits of this novel technology for HNC patients, allowing for better soft-tissue delineation, intra- and interfractional treatment monitoring and more frequent plan adaptations appear more than obvious. At the same time, new technical, clinical, and logistic challenges emerge. The purpose of this article is to summarize and discuss the rationale, recent developments, and future perspectives of this promising radiotherapy modality for treating HNC.
Collapse
Affiliation(s)
- Simon Boeke
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - David Mönnich
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | | | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| |
Collapse
|
36
|
Kim N, Chun J, Chang JS, Lee CG, Keum KC, Kim JS. Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area. Cancers (Basel) 2021; 13:cancers13040702. [PMID: 33572310 PMCID: PMC7915955 DOI: 10.3390/cancers13040702] [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: 12/19/2020] [Revised: 02/02/2021] [Accepted: 02/06/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary We analyzed the contouring data of 23 organs-at-risk from 100 patients with head and neck cancer who underwent definitive radiation therapy (RT). Deep learning-based segmentation (DLS) with continual training was compared to DLS with conventional training and deformable image registration (DIR) in both quantitative and qualitative (Turing’s test) methods. Results indicate the effectiveness of DLS over DIR and that of DLS with continual training over DLS with conventional training in contouring for head and neck region, especially for glandular structures. DLS with continual training might be beneficial for optimizing personalized adaptive RT in head and neck region. Abstract This study investigated the feasibility of deep learning-based segmentation (DLS) and continual training for adaptive radiotherapy (RT) of head and neck (H&N) cancer. One-hundred patients treated with definitive RT were included. Based on 23 organs-at-risk (OARs) manually segmented in initial planning computed tomography (CT), modified FC-DenseNet was trained for DLS: (i) using data obtained from 60 patients, with 20 matched patients in the test set (DLSm); (ii) using data obtained from 60 identical patients with 20 unmatched patients in the test set (DLSu). Manually contoured OARs in adaptive planning CT for independent 20 patients were provided as test sets. Deformable image registration (DIR) was also performed. All 23 OARs were compared using quantitative measurements, and nine OARs were also evaluated via subjective assessment from 26 observers using the Turing test. DLSm achieved better performance than both DLSu and DIR (mean Dice similarity coefficient; 0.83 vs. 0.80 vs. 0.70), mainly for glandular structures, whose volume significantly reduced during RT. Based on subjective measurements, DLS is often perceived as a human (49.2%). Furthermore, DLSm is preferred over DLSu (67.2%) and DIR (96.7%), with a similar rate of required revision to that of manual segmentation (28.0% vs. 29.7%). In conclusion, DLS was effective and preferred over DIR. Additionally, continual DLS training is required for an effective optimization and robustness in personalized adaptive RT.
Collapse
|
37
|
LEE VSC, SchettIno G, Nisbet A. UK adaptive radiotherapy practices for head and neck cancer patients. BJR Open 2020; 2:20200051. [PMID: 33367201 PMCID: PMC7749087 DOI: 10.1259/bjro.20200051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To provide evidence on the extent and manner in which adaptive practices have been employed in the UK and identify the main barriers for the clinical implementation of adaptive radiotherapy (ART) in head and neck (HN) cancer cases. METHODS In December 2019, a Supplementary Material 1, of 23 questions, was sent to all UK radiotherapy centres (67). This covered general information to current ART practices and perceived barriers to implementation. RESULTS 31 centres responded (46%). 56% responding centres employed ART for between 10 and 20 patients/annum. 96% of respondents were using CBCT either alone or with other modalities for assessing "weight loss" and "shell gap," which were the main reasons for ART. Adaptation usually occurs at week three or four during the radiotherapy treatment. 25 responding centres used an online image-guided radiotherapy (IGRT) approach and 20 used an offline ad hoc ART approach, either with or without protocol level. Nearly 70% of respondents required 2 to 3 days to create an adaptive plan and 95% used 3-5 mm adaptive planning target volume margins. All centres performed pre-treatment QA. "Limited staff resources" and "lack of clinical relevance" were identified as the two main barriers for ART implementation. CONCLUSION There is no consensus in adaptive practice for HN cancer patients across the UK. For those centres not employing ART, similar clinical implementation barriers were identified. ADVANCES IN KNOWLEDGE An insight into contemporary UK practices of ART for HN cancer patients indicating national guidance for ART implementation for HN cancer patients may be required.
Collapse
Affiliation(s)
| | | | - Andrew Nisbet
- Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| |
Collapse
|
38
|
Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images. Phys Med Biol 2020; 65:245034. [PMID: 32097892 DOI: 10.1088/1361-6560/ab79c3] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is developed for H&N OAR segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, parotid glands, and submandibular glands to evaluate the proposed SCSA-Net. The proposed SCSA-Net consistently outperforms the state-of-the-art methods on the public dataset. Specifically, compared with Res-Net and SE-Net, which is constructed from squeeze-and-excitation block equipped residual blocks, the DSC of the optic nerves and submandibular glands is improved by 0.06, 0.03 and 0.05, 0.04 by the SCSA-Net. Moreover, the proposed method achieves statistically significant improvements in terms of DSC on all and eight of nine OARs over Res-Net and SE-Net, respectively. The trained network was able to achieve good segmentation results on the serial dataset, but the results were further improved after fine-tuning of the model using the simulation CT images. For the parotids and submandibular glands, the volume changes of individual patients are highly consistent between the automated and manual segmentation (Pearson's correlation 0.97-0.99). The proposed SCSA-Net is computationally efficient to perform segmentation (sim 2 s/CT).
Collapse
Affiliation(s)
- Shuiping Gou
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi 710071, People's Republic of China
| | | | | | | | | | | |
Collapse
|
39
|
Bertholet J, Anastasi G, Noble D, Bel A, van Leeuwen R, Roggen T, Duchateau M, Pilskog S, Garibaldi C, Tilly N, García-Mollá R, Bonaque J, Oelfke U, Aznar MC, Heijmen B. Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II: Offline and online plan adaption for interfractional changes. Radiother Oncol 2020; 153:88-96. [PMID: 32579998 PMCID: PMC7758781 DOI: 10.1016/j.radonc.2020.06.017] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 06/08/2020] [Accepted: 06/12/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE The POP-ART RT study aims to determine to what extent and how intrafractional real-time respiratory motion management (RRMM), and plan adaptation for interfractional anatomical changes (ART) are used in clinical practice and to understand barriers to implementation. Here we report on part II: ART using more than one plan per target per treatment course. MATERIALS AND METHODS A questionnaire on the current practice of ART, wishes for expansion or implementation, and barriers to implementation was distributed worldwide. Four types of ART were discriminated: daily online replanning, online plan library, protocolled offline replanning (all three based on a protocol), and ad-hoc offline replanning. RESULTS The questionnaire was completed by 177 centres from 40 countries. ART was used by 61% of respondents (31% with protocol) for a median (range) of 3 (1-8) tumour sites. CBCT/MVCT was the main imaging modality except for online daily replanning (11 users) where 10 users used MR. Two thirds of respondents wished to implement ART for a new tumour site; 40% of these had plans to do it in the next 2 years. Human/material resources and technical limitations were the main barriers to further use and implementation. CONCLUSIONS ART was used for a broad range of tumour sites, mainly with ad-hoc offline replanning and for a median of 3 tumour sites. There was a large interest in implementing ART for more tumour sites, mainly limited by human/material resources and technical limitations. Daily online replanning was primarily performed on MR-linacs.
Collapse
Affiliation(s)
- Jenny Bertholet
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, United Kingdom; Division of Medical Radiation Physics, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | - Gail Anastasi
- Department of Medical Physics, Royal Surrey County Hospital, St. Luke's Cancer Centre, Guildford, United Kingdom
| | - David Noble
- Cancer Research UK VoxTox Research Group, University of Cambridge Department of Oncology, Cambridge Biomedical Campus, Addenbrooke's Hospital, United Kingdom
| | - Arjan Bel
- Amsterdam UMC, Department of Radiation Oncology, The Netherlands
| | - Ruud van Leeuwen
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Toon Roggen
- Applied Research, Varian Medical Systems Imaging Laboratory GmbH, Dättwil, Switzerland
| | | | - Sara Pilskog
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway; Department of Physics and Technology, University of Bergen, Norway
| | - Cristina Garibaldi
- IEO, European Institute of Oncology IRCCS, Unit of Radiation Research, Milan, Italy
| | - Nina Tilly
- Elekta Instruments AB, Stockholm, Sweden; Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Sweden
| | - Rafael García-Mollá
- Servicio de Radiofísica y Protección Radiológica, Consorcio Hospital General Universitario de Valencia, Spain
| | - Jorge Bonaque
- Servicio de Radiofísica y Protección Radiológica, Consorcio Hospitalario Provincial de Castellón, Castelló de la Plana, Spain
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, United Kingdom
| | - Marianne C Aznar
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, United Kingdom; Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ben Heijmen
- Erasmus MC Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands
| |
Collapse
|
40
|
Oktay O, Nanavati J, Schwaighofer A, Carter D, Bristow M, Tanno R, Jena R, Barnett G, Noble D, Rimmer Y, Glocker B, O’Hara K, Bishop C, Alvarez-Valle J, Nori A. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Netw Open 2020; 3:e2027426. [PMID: 33252691 PMCID: PMC7705593 DOI: 10.1001/jamanetworkopen.2020.27426] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/01/2020] [Indexed: 12/17/2022] Open
Abstract
Importance Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. Objective To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. Design, Setting, and Participants This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. Main Outcomes and Measures The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. Results A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time. Conclusions and Relevance In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.
Collapse
Affiliation(s)
- Ozan Oktay
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Jay Nanavati
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | | | - David Carter
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Melissa Bristow
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Ryutaro Tanno
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Rajesh Jena
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Gill Barnett
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - David Noble
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, United Kingdom
- now with Edinburgh Cancer Centre, Western General Hospital, Edinburgh, United Kingdom
| | - Yvonne Rimmer
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, United Kingdom
| | - Ben Glocker
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Kenton O’Hara
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | | | | | - Aditya Nori
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| |
Collapse
|
41
|
Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020; 30:341-357. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
Collapse
Affiliation(s)
- Monica Shukla
- Department of Radiation Oncology, Froedtert and Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory, Department of Radiology, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada
| | - Mohit Agarwal
- Department of Radiology, Section of Neuroradiology, Froedtert and Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
| |
Collapse
|
42
|
Grégoire V, Guckenberger M, Haustermans K, Lagendijk JJW, Ménard C, Pötter R, Slotman BJ, Tanderup K, Thorwarth D, van Herk M, Zips D. Image guidance in radiation therapy for better cure of cancer. Mol Oncol 2020; 14:1470-1491. [PMID: 32536001 PMCID: PMC7332209 DOI: 10.1002/1878-0261.12751] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/08/2020] [Accepted: 06/08/2020] [Indexed: 12/11/2022] Open
Abstract
The key goal and main challenge of radiation therapy is the elimination of tumors without any concurring damages of the surrounding healthy tissues and organs. Radiation doses required to achieve sufficient cancer-cell kill exceed in most clinical situations the dose that can be tolerated by the healthy tissues, especially when large parts of the affected organ are irradiated. High-precision radiation oncology aims at optimizing tumor coverage, while sparing normal tissues. Medical imaging during the preparation phase, as well as in the treatment room for localization of the tumor and directing the beam, referred to as image-guided radiotherapy (IGRT), is the cornerstone of precision radiation oncology. Sophisticated high-resolution real-time IGRT using X-rays, computer tomography, magnetic resonance imaging, or ultrasound, enables delivery of high radiation doses to tumors without significant damage of healthy organs. IGRT is the most convincing success story of radiation oncology over the last decades, and it remains a major driving force of innovation, contributing to the development of personalized oncology, for example, through the use of real-time imaging biomarkers for individualized dose delivery.
Collapse
Affiliation(s)
- Vincent Grégoire
- Department of Radiation OncologyLéon Bérard Cancer CenterLyonFrance
| | - Matthias Guckenberger
- Department for Radiation OncologyUniversity Hospital ZurichUniversity of ZurichSwitzerland
| | - Karin Haustermans
- Department of Radiation OncologyLeuven Cancer InstituteUniversity Hospital GasthuisbergLeuvenBelgium
| | | | | | - Richard Pötter
- Department of Radiation OncologyMedical UniversityGeneral Hospital of ViennaAustria
| | - Ben J. Slotman
- Department of Radiation OncologyAmsterdam University Medical CentersThe Netherlands
| | - Kari Tanderup
- Department of OncologyAarhus University HospitalDenmark
| | - Daniela Thorwarth
- Section for Biomedical PhysicsDepartment of Radiation OncologyUniversity of TübingenGermany
| | - Marcel van Herk
- Department of Biomedical Engineering and PhysicsCancer Center AmsterdamAmsterdam UMCUniversity of AmsterdamThe Netherlands
- Institute of Cancer SciencesUniversity of ManchesterUK
- Department of Radiotherapy Related ResearchThe Christie NHS Foundation TrustManchesterUK
| | - Daniel Zips
- Department of Radiation OncologyUniversity of TübingenGermany
| |
Collapse
|
43
|
Kearney M, Coffey M, Leong A. A review of Image Guided Radiation Therapy in head and neck cancer from 2009-201 - Best Practice Recommendations for RTTs in the Clinic. Tech Innov Patient Support Radiat Oncol 2020; 14:43-50. [PMID: 32566769 PMCID: PMC7296359 DOI: 10.1016/j.tipsro.2020.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/17/2020] [Accepted: 02/11/2020] [Indexed: 02/06/2023] Open
Abstract
Radiation therapy (RT) is beneficial in Head and Neck Cancer (HNC) in both the definitive and adjuvant setting. Highly complex and conformal planning techniques are becoming standard practice in delivering increased doses in HNC. A sharp falloff in dose outside the high dose area is characteristic of highly complex techniques and geometric uncertainties must be minimised to prevent under dosage of the target volume and possible over dosage of surrounding critical structures. CTV-PTV margins are employed to account for geometric uncertainties such as set up errors and both interfraction and intrafraction motion. Robust immobilisation and Image Guided Radiation Therapy (IGRT) is also essential in this group of patients to minimise discrepancies in patient position during the treatment course. IGRT has evolved with increased 2-Dimensional (2D) and 3-Dimensional (3D) IGRT modalities available for geometric verification. 2D and 3D IGRT modalities are both beneficial in geometric verification while 3D imaging is a valuable tool in assessing volumetric changes that may have dosimetric consequences for this group of patients. IGRT if executed effectively and efficiently provides clinicians with confidence to reduce CTV-PTV margins thus limiting treatment related toxicities in patients. Accumulated exposure dose from IGRT vary considerably and may be incorporated into the treatment plan to avoid excess dose. However, there are considerable variations in the application of IGRT in RT practice. This paper aims to summarise the advances in IGRT in HNC treatment and provide clinics with recommendations for an IGRT strategy for HNC in the clinic.
Collapse
Affiliation(s)
- Maeve Kearney
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College, Dublin 2, Ireland
| | - Mary Coffey
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College, Dublin 2, Ireland
| | - Aidan Leong
- Department of Radiation Therapy, University of Otago, Wellington, New Zealand.,Bowen Icon Cancer Centre, Wellington, New Zealand
| |
Collapse
|
44
|
Bonomo P, Talamonti C, Caini S. Reply to Yamazaki et al (HED-19-525.R1). Head Neck 2020; 42:2219-2220. [PMID: 32149454 DOI: 10.1002/hed.26113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 02/12/2020] [Indexed: 11/07/2022] Open
Affiliation(s)
- Pierluigi Bonomo
- Azienda Ospedaliero-Universitaria Careggi, Radiation Oncology, Florence, Italy
| | - Cinzia Talamonti
- Azienda Ospedaliero-Universitaria Careggi, Medical Physics, Florence, Italy
| | - Saverio Caini
- Institute for Cancer Research, Prevention, and Clinical Network (ISPRO), Cancer Risk Factors and Lifestyle Epidemiology Unit, Florence, Italy
| |
Collapse
|
45
|
Delaby N, Martin S, Barateau A, Henry O, Perichon N, De Crevoisier R, Chajon E, Castelli J, Lafond C. Implementation of an optimization method for parotid gland sparing during inverse planning for head and neck cancer radiotherapy. Cancer Radiother 2020; 24:28-37. [PMID: 32007370 DOI: 10.1016/j.canrad.2019.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To guide parotid gland (PG) sparing at the dose planning step, a specific model based on overlap between PTV and organ at risk (Moore et al.) was developed and evaluated for VMAT in head-and-neck (H&N) cancer radiotherapy. MATERIALS AND METHODS One hundred and sixty patients treated for locally advanced H&N cancer were included. A model optimization was first performed (20 patients) before a model evaluation (110 patients). Thirty cases were planned with and without the model to quantify the PG dose sparing. The inter-operator variability was evaluated on one case, planned by 12 operators with and without the model. The endpoints were PG mean dose (Dmean), PTV homogeneity and number of monitor units (MU). RESULTS The PG Dmean predicted by the model was reached in 89% of cases. Using the model significantly reduced the PG Dmean: -6.1±4.3Gy. Plans with the model showed lower PTV dose homogeneity and more MUs (+10.5% on average). For the inter-operator variability, PG dose volume histograms without the optimized model were significantly different compared to those with the model; the Dmean standard deviation for the ipsilateral PG decreased from 2.2Gy to 1.2Gy. For the contralateral PG, this value decreased from 2.9Gy to 0.8Gy. CONCLUSION During the H&N inverse planning, the optimized model guides to the lowest PG achievable mean dose, allowing a significant PG mean dose reduction of -6.1Gy. Integrating this method at the treatment-planning step significantly reduced the inter-patient and inter-operator variabilities.
Collapse
Affiliation(s)
- N Delaby
- Centre Eugène Marquis, Unité de Physique Médicale, rue de La Bataille Flandres Dunkerque, CS 44229, 35042 Rennes Cedex, France.
| | - S Martin
- Centre Eugène Marquis, Département de Radiothérapie, rue de La Bataille Flandres Dunkerque, CS 44229, 35042 Rennes Cedex, France
| | - A Barateau
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - O Henry
- Centre Eugène Marquis, Unité de Physique Médicale, rue de La Bataille Flandres Dunkerque, CS 44229, 35042 Rennes Cedex, France
| | - N Perichon
- Centre Eugène Marquis, Unité de Physique Médicale, rue de La Bataille Flandres Dunkerque, CS 44229, 35042 Rennes Cedex, France
| | - R De Crevoisier
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - E Chajon
- Centre Eugène Marquis, Département de Radiothérapie, rue de La Bataille Flandres Dunkerque, CS 44229, 35042 Rennes Cedex, France
| | - J Castelli
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - C Lafond
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| |
Collapse
|
46
|
Loi G, Fusella M, Vecchi C, Menna S, Rosica F, Gino E, Maffei N, Menghi E, Savini A, Roggio A, Radici L, Cagni E, Lucio F, Strigari L, Strolin S, Garibaldi C, Romanò C, Piovesan M, Franco P, Fiandra C. Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study. Pract Radiat Oncol 2019; 10:125-132. [PMID: 31786233 DOI: 10.1016/j.prro.2019.11.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/22/2019] [Accepted: 11/12/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. METHODS AND MATERIALS Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms' accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. RESULTS Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. CONCLUSIONS The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.
Collapse
Affiliation(s)
- Gianfranco Loi
- Department of Medical Physics, University Hospital "Maggiore della Carità," Novara, Italy
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
| | | | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Fisica Sanitaria, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Rome, Italy
| | | | - Eva Gino
- SC Fisica Sanitaria, A.O. Ordine Mauriziano di Torino, Italy
| | - Nicola Maffei
- Department of Medical Physics, A.O. U. di Modena, Modena, Italy; University of Turin, Post Graduate School in Medical Physics, Turin, Italy
| | - Enrico Menghi
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Alessandro Savini
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Antonella Roggio
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Lorenzo Radici
- Ospedale regionale "Umberto Parini" Azienda USL VDA, Fisica Sanitaria, Italy
| | - Elisabetta Cagni
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; School of Engineering, Cardiff University, Cardiff, Wales, UK
| | | | - Lidia Strigari
- Department of Medical Physics, St. Orsola-Malpighi Hospital, Bologna, Italy
| | | | - Cristina Garibaldi
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | - Chiara Romanò
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | | | | | - Christian Fiandra
- University of Turin, Department of Oncology, Turin, Italy; School of Bioengineering and Medical-Surgical Sciences, Politecnico di Torino, Turin, Italy
| |
Collapse
|
47
|
Bonomo P, Talamonti C, Desideri I, Marrazzo L, Pezzulla D, Rampini A, Bertocci S, De Majo R, Gasperi C, Curion AS, Lastrucci L, Dominici L, Pallotta S, Livi L, Caini S. Analysis of skin dose distribution for the prediction of severe radiation dermatitis in head and neck squamous cell carcinoma patients treated with concurrent chemo-radiotherapy. Head Neck 2019; 42:244-253. [PMID: 31682308 DOI: 10.1002/hed.25997] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 09/16/2019] [Accepted: 10/09/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND We investigated whether the pattern of intensity-modulated radiotherapy (IMRT) dose distribution to the skin can be correlated with the development of G3/G4 radiation dermatitis (RD). METHODS A frequency-matched cohort analysis was perfomed on patients treated with IMRT and concurrent cisplatin or cetuximab. Risk ratios were obtained by fitting Poisson regression models. RESULTS The incidence of G3/G4 RD was 41.1% in 90 patients included (50% vs 36.6% in the cetuximab and cisplatin cohorts, respectively). In multivariate analysis, PS ≥ 1 and weight loss at RT completion >10 kg were the only factors that retained significance. The best dosimetric predictive accuracy was provided by 19.9 cc and 5.8 cc of skin ring 2 mm V50 and V60, respectively (AUC: 0.61 for both). CONCLUSION Along with clinical factors, the pattern of dose distribution to a ring structure localized 2 mm below the patient's surface may help predict the development of severe RD.
Collapse
Affiliation(s)
- Pierluigi Bonomo
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Cinzia Talamonti
- Medical Physics, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Isacco Desideri
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Livia Marrazzo
- Medical Physics, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Donato Pezzulla
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | | | | | | | | | | | - Luca Dominici
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Stefania Pallotta
- Medical Physics, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Lorenzo Livi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Saverio Caini
- Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention, and Clinical Network (ISPRO), Florence, Italy
| |
Collapse
|
48
|
Kiser KJ, Smith BD, Wang J, Fuller CD. "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era. Front Oncol 2019; 9:983. [PMID: 31632914 PMCID: PMC6779062 DOI: 10.3389/fonc.2019.00983] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/16/2019] [Indexed: 12/17/2022] Open
Abstract
Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.
Collapse
Affiliation(s)
- Kendall J Kiser
- John P. and Kathrine G. McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States.,School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.,Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin D Smith
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jihong Wang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|