1
|
Meyer I, Peters N, Tamborino G, Lee H, Bertolet A, Faddegon B, Mille MM, Lee C, Schuemann J, Paganetti H. A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system. Phys Med Biol 2024; 69:10.1088/1361-6560/ad64b6. [PMID: 39019051 PMCID: PMC11345907 DOI: 10.1088/1361-6560/ad64b6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 07/17/2024] [Indexed: 07/19/2024]
Abstract
Objective. To allow the estimation of secondary cancer risks from radiation therapy treatment plans in a comprehensive and user-friendly Monte Carlo (MC) framework.Method. Patient planning computed tomography scans were extended superior-inferior using the International Commission on Radiological Protection's Publication 145 computational mesh phantoms and skeletal matching. Dose distributions were calculated with the TOPAS MC system using novel mesh capabilities and the digital imaging and communications in medicine radiotherapy extension interface. Finally, in-field and out-of-field cancer risk was calculated using both sarcoma and carcinoma risk models with two alternative parameter sets.Result. The TOPAS MC framework was extended to facilitate epidemiological studies on radiation-induced cancer risk. The framework is efficient and allows automated analysis of large datasets. Out-of-field organ dose was small compared to in-field dose, but the risk estimates indicate a non-negligible contribution to the total radiation induced cancer risk.Significance. This work equips the TOPAS MC system with anatomical extension, mesh geometry, and cancer risk model capabilities that make state-of-the-art out-of-field dose calculation and risk estimation accessible to a large pool of users. Furthermore, these capabilities will facilitate further refinement of risk models and sensitivity analysis of patient specific treatment options.
Collapse
Affiliation(s)
- Isaac Meyer
- Massachusetts General Hospital
- Harvard Medical School
| | - Nils Peters
- Massachusetts General Hospital
- Harvard Medical School
| | | | - Hoyeon Lee
- Massachusetts General Hospital
- Harvard Medical School
| | | | - Bruce Faddegon
- Department of Radiation Oncology, University of California San Francisco
| | - Matthew M. Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH)
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH)
| | - Jan Schuemann
- Massachusetts General Hospital
- Harvard Medical School
| | | |
Collapse
|
2
|
Berrington de González A, Gibson TM, Lee C, Albert PS, Griffin KT, Kitahara CM, Liu D, Mille MM, Shin J, Bajaj BV, Flood TE, Gallotto SL, Paganetti H, Ahmed SK, Eaton BR, Indelicato DJ, Milgrom SA, Palmer JD, Baliga S, Poppe MM, Tsang DS, Wong K, Yock TI. The Pediatric Proton and Photon Therapy Comparison Cohort: Study Design for a Multicenter Retrospective Cohort to Investigate Subsequent Cancers After Pediatric Radiation Therapy. Adv Radiat Oncol 2023; 8:101273. [PMID: 38047226 PMCID: PMC10692298 DOI: 10.1016/j.adro.2023.101273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/08/2023] [Indexed: 12/05/2023] Open
Abstract
Purpose The physical properties of protons lower doses to surrounding normal tissues compared with photons, potentially reducing acute and long-term adverse effects, including subsequent cancers. The magnitude of benefit is uncertain, however, and currently based largely on modeling studies. Despite the paucity of directly comparative data, the number of proton centers and patients are expanding exponentially. Direct studies of the potential risks and benefits are needed in children, who have the highest risk of radiation-related subsequent cancers. The Pediatric Proton and Photon Therapy Comparison Cohort aims to meet this need. Methods and Materials We are developing a record-linkage cohort of 10,000 proton and 10,000 photon therapy patients treated from 2007 to 2022 in the United States and Canada for pediatric central nervous system tumors, sarcomas, Hodgkin lymphoma, or neuroblastoma, the pediatric tumors most frequently treated with protons. Exposure assessment will be based on state-of-the-art dosimetry facilitated by collection of electronic radiation records for all eligible patients. Subsequent cancers and mortality will be ascertained by linkage to state and provincial cancer registries in the United States and Canada, respectively. The primary analysis will examine subsequent cancer risk after proton therapy compared with photon therapy, adjusting for potential confounders and accounting for competing risks. Results For the primary aim comparing overall subsequent cancer rates between proton and photon therapy, we estimated that with 10,000 patients in each treatment group there would be 80% power to detect a relative risk of 0.8 assuming a cumulative incidence of subsequent cancers of 2.5% by 15 years after diagnosis. To date, 9 institutions have joined the cohort and initiated data collection; additional centers will be added in the coming year(s). Conclusions Our findings will affect clinical practice for pediatric patients with cancer by providing the first large-scale systematic comparison of the risk of subsequent cancers from proton compared with photon therapy.
Collapse
Affiliation(s)
| | - Todd M. Gibson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Paul S. Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Keith T. Griffin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Cari Meinhold Kitahara
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Danping Liu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Matthew M. Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Jungwook Shin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Benjamin V.M. Bajaj
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Tristin E. Flood
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Sara L. Gallotto
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Safia K. Ahmed
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Bree R. Eaton
- Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Daniel J. Indelicato
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, Florida
| | - Sarah A. Milgrom
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Joshua D. Palmer
- Department of Radiation Oncology, James Cancer Hospital at the Ohio State University Wexner Medical Center and Nationwide Children's Hospital, Columbus, Ohio
| | - Sujith Baliga
- Department of Radiation Oncology, James Cancer Hospital at the Ohio State University Wexner Medical Center and Nationwide Children's Hospital, Columbus, Ohio
| | - Matthew M. Poppe
- Department of Radiation Oncology, University of Utah–Huntsman Cancer Institute, Salt Lake City, Utah
| | - Derek S. Tsang
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kenneth Wong
- Radiation Oncology Program, Children's Hospital Los Angeles, Los Angeles, California
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Torunn I. Yock
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
3
|
Griffin KT, Yeom YS, Mille MM, Lee C, Jung JW, Hertel NE, Lee C. Comparison of out-of-field normal tissue dose estimates for pencil beam scanning proton therapy: MCNP6, PHITS, and TOPAS. Biomed Phys Eng Express 2022; 9:10.1088/2057-1976/acaab1. [PMID: 36562506 PMCID: PMC10772933 DOI: 10.1088/2057-1976/acaab1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/12/2022] [Indexed: 12/14/2022]
Abstract
Monte Carlo (MC) methods are considered the gold-standard approach to dose estimation for normal tissues outside the treatment field (out-of-field) in proton therapy. However, the physics of secondary particle production from high-energy protons are uncertain, particularly for secondary neutrons, due to challenges in performing accurate measurements. Instead, various physics models have been developed over the years to reenact these high-energy interactions based on theory. It should thus be acknowledged that MC users must currently accept some unknown uncertainties in out-of-field dose estimates. In the present study, we compared three MC codes (MCNP6, PHITS, and TOPAS) and their available physics models to investigate the variation in out-of-field normal tissue dosimetry for pencil beam scanning proton therapy patients. Total yield and double-differential (energy and angle) production of two major secondary particles, neutrons and gammas, were determined through irradiation of a water phantom at six proton energies (80, 90, 100, 110, 150, and 200 MeV). Out-of-field normal tissue doses were estimated for intracranial irradiations of 1-, 5-, and 15-year-old patients using whole-body computational phantoms. Notably, the total dose estimates for each out-of-field organ varied by approximately 25% across the three codes, independent of its distance from the treatment volume. Dose discrepancies amongst the codes were linked to the utilized physics model, which impacts the characteristics of the secondary radiation field. Using developer-recommended physics, TOPAS produced both the highest neutron and gamma doses to all out-of-field organs from all examined conditions; this was linked to its highest yields of secondary particles and second hardest energy spectra. Subsequent results when using other physics models found reduced yields and energies, resulting in lower dose estimates. Neutron dose estimates were the most impacted by physics model choice, and thus the variation in out-of-field dose estimates may be even larger than 25% when considering biological effectiveness.
Collapse
Affiliation(s)
- Keith T. Griffin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yeon Soo Yeom
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, South Korea
| | - Matthew M. Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jae Won Jung
- Department of Physics, East Carolina University, Greenville, NC, USA
| | - Nolan E. Hertel
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| |
Collapse
|
4
|
Shrestha S, Newhauser WD, Donahue WP, Pérez-Andújar A. Stray neutron radiation exposures from proton therapy: physics-based analytical models of neutron spectral fluence, kerma and absorbed dose. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/25/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Patients who receive proton beam therapy are exposed to unwanted stray neutrons. Stray radiations increase the risk of late effects in normal tissues, such as second cancers and cataracts, and may cause implanted devices such as pacemakers to malfunction. Compared to therapeutic beams, little attention has been paid to modeling stray neutron exposures. In the past decade, substantial progress was made to develop semiempirical models of stray neutron dose equivalent, but models to routinely calculate neutron absorbed dose and kerma are still lacking. The objective of this work was to develop a new physics based analytical model to calculate neutron spectral fluence, kerma, and absorbed dose in a water phantom. Approach. We developed the model using dosimetric data from Monte Carlo simulations and neutron kerma coefficients from the literature. The model explicitly considers the production, divergence, scattering, and attenuation of neutrons. Neutron production was modeled for 120–250 MeV proton beams impinging on a variety of materials. Fluence, kerma and dose calculations were performed in a 30 × 180 × 44 cm3 phantom at points up to 43 cm in depth and 80 cm laterally. Main Results. Predictions of the analytical model agreed reasonably with corresponding values from Monte Carlo simulations, with a mean difference in average energy deposited of 20%, average kerma coefficient of 21%, and absorbed dose to water of 49%. Significance. The analytical model is simple to implement and use, requires less configuration data that previously reported models, and is computationally fast. This model appears potentially suitable for integration in treatment planning system, which would enable risk calculations in prospective and retrospective cases, providing a powerful tool for epidemiological studies and clinical trials.
Collapse
|
5
|
Romero-Expósito M, Toma-Dasu I, Dasu A. Determining Out-of-Field Doses and Second Cancer Risk From Proton Therapy in Young Patients—An Overview. Front Oncol 2022; 12:892078. [PMID: 35712488 PMCID: PMC9197425 DOI: 10.3389/fonc.2022.892078] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Proton therapy has the potential to provide survival and tumor control outcomes comparable and frequently superior to photon therapy. This has led to a significant concern in the medical physics community on the risk for the induction of second cancers in all patients and especially in younger patients, as they are considered more radiosensitive than adults and have an even longer expected lifetime after treatment. Thus, our purpose is to present an overview of the research carried out on the evaluation of out-of-field doses linked to second cancer induction and the prediction of this risk. Most investigations consisted of Monte Carlo simulations in passive beam facilities for clinical scenarios. These works established that equivalent doses in organs could be up to 200 mSv or 900 mSv for a brain or a craniospinal treatment, respectively. The major contribution to this dose comes from the secondary neutrons produced in the beam line elements. Few works focused on scanned-beam facilities, but available data show that, for these facilities, equivalent doses could be between 2 and 50 times lower. Patient age is a relevant factor in the dose level, especially for younger patients (by means of the size of the body) and, in addition, in the predicted risk by models (due to the age dependence of the radiosensitivity). For risks, the sex of the patient also plays an important role, as female patients show higher sensitivity to radiation. Thus, predicted risks of craniospinal irradiation can range from 8% for a 15-year-old male patient to 58% for a 2-year-old female patient, using a risk model from a radiological protection field. These values must be taken with caution due to uncertainties in risk models, and then dosimetric evaluation of stray radiation becomes mandatory in order to complement epidemiological studies and be able to model appropriate dose–response functions for this dose range. In this sense, analytical models represent a useful tool and some models have been implemented to be used for young patients. Research carried out so far confirmed that proton beam therapy reduces the out-of-field doses and second cancer risk. However, further investigations may be required in scanned-beam delivery systems.
Collapse
Affiliation(s)
- Maite Romero-Expósito
- The Skandion Clinic, Uppsala, Sweden
- Oncology Pathology Department, Karolinska Institutet, Stockholm, Sweden
- *Correspondence: Maite Romero-Expósito,
| | - Iuliana Toma-Dasu
- Oncology Pathology Department, Karolinska Institutet, Stockholm, Sweden
- Medical Radiation Physics, Stockholm University, Stockholm, Sweden
| | - Alexandru Dasu
- The Skandion Clinic, Uppsala, Sweden
- Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| |
Collapse
|