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Loebner HA, Mueller S, Volken W, Wallimann P, Aebersold DM, Stampanoni MFM, Fix MK, Manser P. Impact of the gradient in gantry-table rotation on dynamic trajectory radiotherapy plan quality. Med Phys 2023; 50:7104-7117. [PMID: 37748175 DOI: 10.1002/mp.16749] [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: 01/18/2023] [Revised: 09/07/2023] [Accepted: 09/10/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND To improve organ at risk (OAR) sparing, dynamic trajectory radiotherapy (DTRT) extends VMAT by dynamic table and collimator rotation during beam-on. However, comprehensive investigations regarding the impact of the gantry-table (GT) rotation gradient on the DTRT plan quality have not been conducted. PURPOSE To investigate the impact of a user-defined GT rotation gradient on plan quality of DTRT plans in terms of dosimetric plan quality, dosimetric robustness, deliverability, and delivery time. METHODS The dynamic trajectories of DTRT are described by GT and gantry-collimator paths. The GT path is determined by minimizing the overlap of OARs with planning target volume (PTV). This approach is extended to consider a GT rotation gradient by means of a maximum gradient of the path (G m a x ${G}_{max}$ ) between two adjacent control points (G = | Δ table angle / Δ gantry angle | $G = | \Delta {{\mathrm{table\ angle}}/\Delta {\mathrm{gantry\ angle}}} |$ ) and maximum absolute change of G (Δ G m a x ${{\Delta}}{G}_{max}$ ). Four DTRT plans are created with different maximum G&∆G:G m a x ${G}_{max}$ &Δ G m a x ${{\Delta}}{G}_{max}$ = 0.5&0.125 (DTRT-1), 1&0.125 (DTRT-2), 3&0.125 (DTRT-3) and 3&1(DTRT-4), including 3-4 dynamic trajectories, for three clinically motivated cases in the head and neck and brain region (A, B, and C). A reference VMAT plan for each case is created. For all plans, plan quality is assessed and compared. Dosimetric plan quality is evaluated by target coverage, conformity, and OAR sparing. Dosimetric robustness is evaluated against systematic and random patient-setup uncertainties between± 3 mm $ \pm 3\ {\mathrm{mm}}$ in the lateral, longitudinal, and vertical directions, and machine uncertainties between± 4 ∘ $ \pm 4^\circ \ $ in the dynamically rotating machine components (gantry, table, collimator rotation). Delivery time is recorded. Deliverability and delivery accuracy on a TrueBeam are assessed by logfile analysis for all plans and additionally verified by film measurements for one case. All dose calculations are Monte Carlo based. RESULTS The extension of the DTRT planning process with user-definedG m a x & Δ G m a x ${G}_{max}\& {{\Delta}}{G}_{max}$ to investigate the impact of the GT rotation gradient on plan quality is successfully demonstrated. With increasingG m a x & Δ G m a x ${G}_{max}\& {{\Delta}}{G}_{max}$ , slight (case C,D m e a n , p a r o t i d l . ${D}_{mean,\ parotid\ l.}$ : up to-1Gy) and substantial (case A,D 0.03 c m 3 , o p t i c n e r v e r . ${D}_{0.03c{m}^3,\ optic\ nerve\ r.}$ : up to -9.3 Gy, caseB,D m e a n , b r a i n $\ {D}_{mean,\ brain}$ : up to -4.7Gy) improvements in OAR sparing are observed compared to VMAT, while maintaining similar target coverage. All plans are delivered on the TrueBeam. Expected and actual machine position values recorded in the logfiles deviated by <0.2° for gantry, table and collimator rotation. The film measurements agreed by >96% (2%global/2 mm Gamma passing rate) with the dose calculation. With increasingG m a x & Δ G m a x ${G}_{max}\& {{\Delta}}{G}_{max}$ , delivery time is prolonged by <2 min/trajectory (DTRT-4) compared to VMAT and DTRT-1. The DTRT plans for case A and B and the VMAT plan for case C plan reveal the best dosimetric robustness for the considered uncertainties. CONCLUSION The impact of the GT rotation gradient on DTRT plan quality is comprehensively investigated for three cases in the head and neck and brain region. Increasing freedom in this gradient improves dosimetric plan quality at the cost of increased delivery time for the investigated cases. No clear dependency of GT rotation gradient on dosimetric robustness is observed.
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Affiliation(s)
- Hannes A Loebner
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Silvan Mueller
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Werner Volken
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Philipp Wallimann
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Daniel M Aebersold
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | | | - Michael K Fix
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Peter Manser
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
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Hopper A, Salans M, Karunamuni R, Hattangadi-Gluth JA. Neurocognitive considerations in the treatment of meningioma with radiation therapy: applications for quantitative neuroimaging and precision radiation medicine. J Neurooncol 2023; 161:277-286. [PMID: 36572802 DOI: 10.1007/s11060-022-04175-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 10/18/2022] [Indexed: 12/27/2022]
Abstract
This article focuses on the role of radiotherapy in the management of meningioma, in the definitive and adjuvant setting and across the spectrum of meningioma grade. Treatment paradigms, informed by clinical evidence, are discussed. Notably, we focus on the impact of radiotherapy on normal brain tissues and neurocognitive function, particularly the dose-dependent changes in white matter and cerebral cortex thickness. Novel imaging techniques have allowed the identification of microstructural changes to eloquent white matter, cortex, and subcortical regions as biomarkers for understanding RT-induced changes in cognitive functioning. Deficits in multiple domains including attention, memory, language and executive function can become more pronounced following radiation. Longitudinal assessment with imaging and neurocognitive testing pre- and post-radiation have allowed correlation between dose to specific regions of the brain and decline in associated domains of neurocognitive function. These findings suggest incorporation of areas at higher risk for neurocognitive sequelae into precision radiation planning. Volumetric arc therapy, advanced planning with cortical sparing, proton therapy and stereotactic radiosurgery are reviewed as options for delivering therapeutic dose to target volumes while minimizing risk to adjacent sensitive regions. The treatment of meningioma is an evolving area, with improving outcomes for higher grade disease in modern trials, where care must be taken to maximize both disease control as well as quality of life for patients.
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Affiliation(s)
- Austin Hopper
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 9500 Gilman Dr., La Jolla, Mail Code 0861, San Diego, CA, 92093-0861, USA
| | - Mia Salans
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 9500 Gilman Dr., La Jolla, Mail Code 0861, San Diego, CA, 92093-0861, USA
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 9500 Gilman Dr., La Jolla, Mail Code 0861, San Diego, CA, 92093-0861, USA
| | - Jona A Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 9500 Gilman Dr., La Jolla, Mail Code 0861, San Diego, CA, 92093-0861, USA.
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Cognitive deficits in adult patients with high-grade glioma: A systematic review. Clin Neurol Neurosurg 2022; 219:107296. [DOI: 10.1016/j.clineuro.2022.107296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 04/04/2022] [Accepted: 05/13/2022] [Indexed: 11/15/2022]
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Woods KE, Ma TM, Cook KA, Morris ED, Gao Y, Sheng K, Kishan AU, Hegde JV, Felix C, Basehart V, Narahara K, Shen Z, Tenn S, Steinberg ML, Chin RK, Cao M. A Prospective Phase II Study of Automated Non-Coplanar VMAT for Recurrent Head and Neck Cancer: Initial Report of Feasibility, Safety, and Patient-Reported Outcomes. Cancers (Basel) 2022; 14:cancers14040939. [PMID: 35205686 PMCID: PMC8870161 DOI: 10.3390/cancers14040939] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The delivery of higher radiation doses has been shown to increase local control, and ultimately survival, for head and neck cancer patients, but highly conformal dose distributions are necessary to minimize normal tissue toxicity. Varian’s HyperArc non-coplanar automated treatment planning and delivery technique has been shown to improve dose conformity for intracranial treatment, but its safety and efficacy for head and neck cancer treatment has yet to be verified. This study evaluates the initial results of a prospective clinical trial using HyperArc for recurrent head and neck cancer patients. We demonstrated that HyperArc can enable significant tumor dose escalation compared to conventional volumetric modulated arc therapy (VMAT) planning while minimizing the dose to organs at risk. Treatment delivery was feasible and safe, with minimal treatment-related toxicities and positive patient-reported quality of life measures. Abstract This study reports the initial results for the first 15 patients on a prospective phase II clinical trial exploring the safety, feasibility, and efficacy of the HyperArc technique for recurrent head and neck cancer treatment. Eligible patients were simulated and planned with both conventional VMAT and HyperArc techniques and the plan with superior dosimetry was selected for treatment. Dosimetry, delivery feasibility and safety, treatment-related toxicity, and patient-reported quality of life (QOL) were all evaluated. HyperArc was chosen over conventional VMAT for all 15 patients and enabled statistically significant increases in dose conformity (R50% reduced by 1.2 ± 2.1, p < 0.05) and mean PTV and GTV doses (by 15.7 ± 4.9 Gy, p < 0.01 and 17.1 ± 6.0 Gy, p < 0.01, respectively). The average HyperArc delivery was 2.8 min longer than conventional VMAT (p < 0.01), and the mean intrafraction motion was ≤ 0.5 ± 0.4 mm and ≤0.3 ± 0.1°. With a median follow-up of 12 months, treatment-related toxicity was minimal (only one grade 3 acute toxicity above baseline) and patient-reported QOL metrics were favorable. HyperArc enabled superior dosimetry and significant target dose escalation compared to conventional VMAT planning, and treatment delivery was feasible, safe, and well-tolerated by patients.
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Affiliation(s)
- Kaley E. Woods
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
- Department of Radiation Oncology, University of Southern California, Los Angeles, CA 90033, USA
| | - Ting Martin Ma
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Kiri A. Cook
- Department of Radiation Oncology, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Eric D. Morris
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Yu Gao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Amar U. Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - John V. Hegde
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Carol Felix
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Vincent Basehart
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Kelsey Narahara
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Zhouhuizi Shen
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Stephen Tenn
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Michael L. Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
| | - Robert K. Chin
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
- Correspondence: (R.K.C.); (M.C.)
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA; (K.E.W.); (T.M.M.); (E.D.M.); (Y.G.); (K.S.); (A.U.K.); (J.V.H.); (C.F.); (V.B.); (K.N.); (Z.S.); (S.T.); (M.L.S.)
- Correspondence: (R.K.C.); (M.C.)
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Northway C, Lincoln JD, Little B, Syme A, Thomas CG. Patient-Specific Collision Zones for 4π Trajectory Optimized Radiation Therapy. Med Phys 2022; 49:1407-1416. [PMID: 35023581 DOI: 10.1002/mp.15452] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/19/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The 4π methodology determines optimized non-coplanar sub arcs for stereotactic radiation therapy which minimize dose to organs-at-risk. Every combination of treatment angle is examined, but some angles are not appropriate as a collision would occur between the gantry and the couch or the gantry and the patient. Those combinations of couch and gantry angles are referred to as collision zones. A major barrier to applying 4π to stereotactic body radiation therapy (SBRT) is the unknown shape of the collision zones, which are significant as patients take up a large volume within the 4π sphere. This study presents a system which determines patient-specific collision zones, without additional clinical steps, to enable safe and deliverable non-coplanar treatment trajectories for SBRT patients. METHODS To augment patient's computed tomography (CT) scan, full body scans of patients in treatment position were acquired using an optical scanner. A library of a priori scans (N = 25) was created. Based on the patients treatment position and their body dimensions, a library scan is selected and registered to the CT scan of the patient. Next, a model of the couch and immobilization equipment is added to the patient model. This results in a patient model that is then aligned with a model of the treatment linac in a "virtual treatment room", where both components can be rotated to test for collisions. To test the collision detection algorithm, an end-to-end test was performed using a cranial phantom. The registration algorithm was tested by comparing the registered patient collision zones to those generated by using the patient's matching scan. RESULTS The collision detection algorithm was found to have a 97.80% accuracy, a 99.99% sensitivity and a 99.99% negative predictive value (NPV). Analysis of the registration algorithm determined that a 6 cm buffer was required to achieve a 99.65% mean sensitivity, where a sensitivity of unity is considered to be a requirement for safe treatment delivery. With a 6 cm buffer the mean accuracy was 86.70% and the mean NPV was 99.33%. CONCLUSIONS Our method of determining patient-specific collision zones can be accomplished with minimal user intervention based on an a priori library of body surface scans, thus enabling the safe application of 4π SBRT.
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Affiliation(s)
- Cassidy Northway
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Author's present intuition is Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - John David Lincoln
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
| | - Brian Little
- Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada
| | - Alasdair Syme
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada.,Beatrice Hunter Cancer Research Institute, Halifax, NS, Canada
| | - Christopher G Thomas
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada.,Beatrice Hunter Cancer Research Institute, Halifax, NS, Canada.,Department of Radiology, Dalhousie University, Halifax, NS, Canada
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Connor M, Kim MM, Cao Y, Hattangadi-Gluth J. Precision Radiotherapy for Gliomas: Implementing Novel Imaging Biomarkers to Improve Outcomes With Patient-Specific Therapy. Cancer J 2021; 27:353-363. [PMID: 34570449 PMCID: PMC8480523 DOI: 10.1097/ppo.0000000000000546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
ABSTRACT Gliomas are the most common primary brain cancer, yet are extraordinarily challenging to treat because they can be aggressive and infiltrative, locally recurrent, and resistant to standard treatments. Furthermore, the treatments themselves, including radiation therapy, can affect patients' neurocognitive function and quality of life. Noninvasive imaging is the standard of care for primary brain tumors, including diagnosis, treatment planning, and monitoring for treatment response. This article explores the ways in which advanced imaging has and will continue to transform radiation treatment for patients with gliomas, with a focus on cognitive preservation and novel biomarkers, as well as precision radiotherapy and treatment adaptation. Advances in novel imaging techniques continue to push the field forward, to more precisely guided treatment planning, radiation dose escalation, measurement of therapeutic response, and understanding of radiation-associated injury.
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Affiliation(s)
- Michael Connor
- From the Department of Radiation Medicine and Applied Sciences, UC San Diego, Moores Cancer Center, La Jolla, CA
| | - Michelle M Kim
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Jona Hattangadi-Gluth
- From the Department of Radiation Medicine and Applied Sciences, UC San Diego, Moores Cancer Center, La Jolla, CA
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Schipaanboord BWK, Giżyńska MK, Rossi L, de Vries KC, Heijmen BJM, Breedveld S. Fully automated treatment planning for MLC-based robotic radiotherapy. Med Phys 2021; 48:4139-4147. [PMID: 34037258 PMCID: PMC8457110 DOI: 10.1002/mp.14993] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/06/2021] [Accepted: 05/14/2021] [Indexed: 01/20/2023] Open
Abstract
PURPOSE To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multileaf collimator. METHODS The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in-house. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. RESULTS AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (-49%)), segments (59.5/98.9 (-40%)), and delivery times (17.1/22.3 min. (-23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (-21%)) and had on average shorter delivery times (18.6/22.3 min. (-17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. CONCLUSIONS A new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (-23%) due to large reductions in beam and segment numbers.
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Affiliation(s)
- Bastiaan W K Schipaanboord
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
| | - Marta K Giżyńska
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
| | - Linda Rossi
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
| | - Kim C de Vries
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
| | - Ben J M Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
| | - Sebastiaan Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Zuid Holland, 3015GD, The Netherlands
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Woods K, Chin RK, Cook KA, Sheng K, Kishan AU, Hegde JV, Tenn S, Steinberg ML, Cao M. Automated Non-Coplanar VMAT for Dose Escalation in Recurrent Head and Neck Cancer Patients. Cancers (Basel) 2021; 13:cancers13081910. [PMID: 33921062 PMCID: PMC8071369 DOI: 10.3390/cancers13081910] [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: 03/08/2021] [Revised: 04/05/2021] [Accepted: 04/12/2021] [Indexed: 11/29/2022] Open
Abstract
Simple Summary The ability to escalate the radiation dose to head and neck tumors has been shown to offer improved local control, and consequently, survival for recurrent head and neck cancer (rHNC) patients. This study evaluates the HyperArc automated non-coplanar planning technique (originally developed for intracranial treatment) for 20 rHNC patients, and compares this technique to conventional planning methods. HyperArc enables significant tumor dose escalation, with average increases in mean target dose of over 11.5 Gy (26%), while maintaining clinically-equivalent doses to nearby organs. Our results show that the average probability of tumor control is 23% higher for HyperArc than conventional techniques. Abstract This study evaluates the potential for tumor dose escalation in recurrent head and neck cancer (rHNC) patients with automated non-coplanar volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) planning (HyperArc). Twenty rHNC patients are planned with conventional VMAT SBRT to 40 Gy while minimizing organ-at-risk (OAR) doses. They are then re-planned with the HyperArc technique to match these minimal OAR doses while escalating the target dose as high as possible. Then, we compare the dosimetry, tumor control probability (TCP), and normal tissue complication probability (NTCP) for the two plan types. Our results show that the HyperArc technique significantly increases the mean planning target volume (PTV) and gross tumor volume (GTV) doses by 10.8 ± 4.4 Gy (25%) and 11.5 ± 5.1 Gy (26%) on average, respectively. There are no clinically significant differences in OAR doses, with maximum dose differences of <2 Gy on average. The average TCP is 23% (± 21%) higher for HyperArc than conventional plans, with no significant differences in NTCP for the brainstem, cord, mandible, or larynx. HyperArc can achieve significant tumor dose escalation while maintaining minimal OAR doses in the head and neck—potentially enabling improved local control for rHNC SBRT patients without increased risk of treatment-related toxicities.
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Affiliation(s)
- Kaley Woods
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Robert K. Chin
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Kiri A. Cook
- Department of Radiation Oncology, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Amar U. Kishan
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - John V. Hegde
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Stephen Tenn
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Michael L. Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA; (K.W.); (R.K.C.); (K.S.); (A.U.K.); (J.V.H.); (S.T.); (M.L.S.)
- Correspondence:
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9
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Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med 2020; 14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022]
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
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Affiliation(s)
- Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
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10
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Nagtegaal SHJ, David S, Snijders TJ, Philippens MEP, Leemans A, Verhoeff JJC. Effect of radiation therapy on cerebral cortical thickness in glioma patients: Treatment-induced thinning of the healthy cortex. Neurooncol Adv 2020; 2:vdaa060. [PMID: 32642712 PMCID: PMC7284116 DOI: 10.1093/noajnl/vdaa060] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Background With overall survival of brain tumors improving, radiation induced brain injury is becoming an increasing issue. One of the effects of radiation therapy (RT) is thinning of the cerebral cortex, which could be one of the factors contributing to cognitive impairments after treatment. In healthy brain, cortex thickness varies between 1 and 4.5 mm. In this study, we assess the effect of RT on the thickness of the cerebral cortex and relate the changes to the local dose. Methods We identified 28 glioma patients with optimal scan quality. Clinical CTs and MRIs at baseline and 1 year post-RT were collected and coregistered. The scans were processed via an automated image processing pipeline, which enabled measuring changes of the cortical thickness, which were related to local dose. Results Three areas were identified where significant dose-dependent thinning occurred, with thinning rates of 5, 6, and 26 μm/Gy after 1 year, which corresponds to losses of 5.4%, 7.2%, and 21.6% per 30 Gy per year. The first area was largely located in the right inferior parietal, supramarginal, and superior parietal regions, the second in the right posterior cingulate and paracentral regions, and the third almost completely in the right lateral orbital frontal region. Conclusions We have identified three areas susceptible to dose-dependent cortical thinning after radiation therapy. Should future prospective studies conclude that irradiation of these areas lead to cognitive decline, they need to be spared in order to prevent this debilitating consequence of treatment.
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Affiliation(s)
- Steven H J Nagtegaal
- Department of Radiation Oncology, University Medical Center, Utrecht, The Netherlands
| | - Szabolcs David
- Image Sciences Institute, University Medical Center, Utrecht, The Netherlands
| | - Tom J Snijders
- Department of Neurology, University Medical Center, Utrecht, The Netherlands
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center, Utrecht, The Netherlands
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11
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Barkousaraie AS, Ogunmolu O, Jiang S, Nguyen D. A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy. Med Phys 2020; 47:880-897. [PMID: 31868927 PMCID: PMC7849631 DOI: 10.1002/mp.13986] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 12/10/2019] [Accepted: 12/10/2019] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Beam orientation selection, whether manual or protocol-based, is the current clinical standard in radiation therapy treatment planning, but it is tedious and can yield suboptimal results. Many algorithms have been designed to optimize beam orientation selection because of its impact on treatment plan quality, but these algorithms suffer from slow calculation of the dose influence matrices of all candidate beams. We propose a fast beam orientation selection method, based on deep learning neural networks (DNN), capable of developing a plan comparable to those developed by the state-of-the-art column generation (CG) method. Our model's novelty lies in its supervised learning structure (using CG to teach the network), DNN architecture, and ability to learn from anatomical features to predict dosimetrically suitable beam orientations without using dosimetric information from the candidate beams. This may save hours of computation. METHODS A supervised DNN is trained to mimic the CG algorithm, which iteratively chooses beam orientations one-by-one by calculating beam fitness values based on Karush-Kush-Tucker optimality conditions at each iteration. The DNN learns to predict these values. The dataset contains 70 prostate cancer patients - 50 training, 7 validation, and 13 test patients - to develop and test the model. Each patient's data contains 6 contours: PTV, body, bladder, rectum, and left and right femoral heads. Column generation was implemented with a GPU-based Chambolle-Pock algorithm, a first-order primal-dual proximal-class algorithm, to create 6270 plans. The DNN trained over 400 epochs, each with 2500 steps and a batch size of 1, using the Adam optimizer at a learning rate of 1 × 10-5 and a sixfold cross-validation technique. RESULTS The average and standard deviation of training, validation, and testing loss functions among the six folds were 0.62 ± 0.09%, 1.04 ± 0.06%, and 1.44 ± 0.11%, respectively. Using CG and supervised DNN, we generated two sets of plans for each scenario in the test set. The proposed method took at most 1.5 s to select a set of five beam orientations and 300 s to calculate the dose influence matrices for 5 beams and finally 20 s to solve the fluence map optimization (FMO). However, CG needed around 15 h to calculate the dose influence matrices of all beams and at least 400 s to solve both the beam orientation selection and FMO problems. The differences in the dose coverage of PTV between plans generated by CG and by DNN were 0.2%. The average dose differences received by organs at risk were between 1 and 6 percent: Bladder had the smallest average difference in dose received (0.956 ± 1.184%), then Rectum (2.44 ± 2.11%), Left Femoral Head (6.03 ± 5.86%), and Right Femoral Head (5.885 ± 5.515%). The dose received by Body had an average difference of 0.10 ± 0.1% between the generated treatment plans. CONCLUSIONS We developed a fast beam orientation selection method based on a DNN that selects beam orientations in seconds and is therefore suitable for clinical routines. In the training phase of the proposed method, the model learns the suitable beam orientations based on patients' anatomical features and omits time intensive calculations of dose influence matrices for all possible candidate beams. Solving the FMO to get the final treatment plan requires calculating dose influence matrices only for the selected beams.
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Affiliation(s)
- Azar Sadeghnejad Barkousaraie
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Olalekan Ogunmolu
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
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12
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Lyu Q, Neph R, Yu VY, Ruan D, Boucher S, Sheng K. Many-isocenter optimization for robotic radiotherapy. Phys Med Biol 2020; 65:045003. [PMID: 31851958 PMCID: PMC7100370 DOI: 10.1088/1361-6560/ab63b8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be managed for versatile applications. This study investigates the dosimetry and delivery efficiency of purposefully creating many isocenters to achieve simultaneously high MLC modulation resolution and large tumor coverage. An integrated optimization framework was proposed for simultaneous beam orientation optimization (BOO), isocenter selection, and fluence map optimization (FMO). The framework includes a least-square dose fidelity objective, a total variation term for regularizing the fluence smoothness, and a group sparsity term for beam selection. A minimal number of isocenters were identified for efficient target coverage. Colliding beams excluded, high-resolution small-field 4π intensity-modulated radiotherapy (IMRT) treatment plans with 50 cm source-to-isocenter distance (SID-50) on 10 Head and Neck (H&N) cancer patients were compared with low-resolution large-field plans with 100 cm SID (SID-100). With the same or better target coverage, the average reduction of [Dmean, Dmax] of 20-beam SID-50 plans from 20-beam SID-100 plans were [2.09 Gy, 1.19 Gy] for organs at risk (OARs) overall, [3.05 Gy, 0.04 Gy] for parotid gland, [3.62 Gy, 5.19 Gy] for larynx, and [3.27 Gy, 1.10 Gy] for mandible. R50 and integral dose were reduced by 5.3% and 9.6%, respectively. Wilcoxon signed-rank test showed significant difference (p < 0.05) in planning target volume (PTV) homogeneity, PTV Dmax, R50, Integral dose, and OAR Dmean and Dmax. The estimated delivery time of 20-beam [SID-50, SID-100] plans were [19, 18] min and [14, 9] min, assuming 5 fractions and 30 fractions, respectively. With clinically acceptable delivery efficiency, many-isocenter optimization is dosimetrically desirable for treating large targets with high modulation resolution on the robotic platform.
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Affiliation(s)
- Qihui Lyu
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, United States of America
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13
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Karunamuni R, Tringale KR, Burkeen J, Tibbs MD, Huynh-Le MP, Bahrami N, Marshall D, Seibert TM, McDonald CR, Hattangadi-Gluth JA. Multi-domain neurocognitive classification of primary brain tumor patients prior to radiotherapy on a prospective clinical trial. J Neurooncol 2020; 146:131-138. [PMID: 31760596 PMCID: PMC7025809 DOI: 10.1007/s11060-019-03353-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 11/20/2019] [Indexed: 01/10/2023]
Abstract
INTRODUCTION We investigated multi-domain baseline neurocognition of primary brain tumor patients prior to radiotherapy (RT), including clinical predictors of function and association between pre-RT and post-RT impairment on a prospective trial. METHODS A multi-domain neuropsychological battery (memory, executive functioning, language, attention, processing) was performed on 37 patients, pre-RT and 3-(n = 21), 6-(n = 22) and 12-(n = 14) months post-RT. Impairment rate was the proportion of patients with standardized T-scores ≤ 1.5 standard deviations below normative means. Per-patient impairment across all domains was calculated using a global deficit score (GDS; higher value indicates more impairment). Associations between baseline GDS and clinical variables were tested. Global GDS impairment rate at each time point was the fraction of patients with GDS scores > 0.5. RESULTS Statistically significant baseline neurocognitive impairments were identified on 4 memory (all p ≤ 0.03) and 2 out of 3 (p = 0.01, p = 0.027) executive functioning tests. Per-patient baseline GDS was significantly associated with tumor volume (p = 0.048), tumor type (p = 0.043), seizure history (p = 0.007), and use of anti-epileptics (p = 0.009). The percentage of patients with the same impairment status at 3-, 6-, and 12-months as at baseline were 88%, 85%, and 85% respectively. CONCLUSIONS Memory and executive functioning impairment were the most common cognitive deficits prior to RT. Patients with larger tumors, more aggressive histology, and use of anti-epileptics had higher baseline GDS values. GDS is a promising tool to encompass multi-domain neurocognitive function, and baseline GDS can identify those at risk of cognitive impairment.
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Affiliation(s)
- Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Kathryn R Tringale
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
| | - Jeffrey Burkeen
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
| | - Michelle D Tibbs
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
| | - Minh-Phuong Huynh-Le
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
| | - Naeim Bahrami
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Deborah Marshall
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
| | - Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jona A Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA.
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.
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14
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Woods K, Neph R, Nguyen D, Sheng K. A sparse orthogonal collimator for small animal intensity-modulated radiation therapy. Part II: hardware development and commissioning. Med Phys 2019; 46:5733-5747. [PMID: 31621091 DOI: 10.1002/mp.13870] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 10/07/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022] Open
Abstract
PURPOSE A dose-modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better-suited sparse orthogonal collimator (SOC) for delivering small animal intensity-modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system. METHODS The SOC system was modeled in computer-aided design software and fabricated with machined tungsten leaves and three-dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino-controlled stepper motors. A Winston-Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra- and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement. RESULTS The Winston-Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra- and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size. CONCLUSIONS A sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose-modulation resolution, is ideal for delivering high-quality small animal IMRT with our RAO-based treatment planning system.
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Affiliation(s)
- Kaley Woods
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ryan Neph
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Dan Nguyen
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ke Sheng
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
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15
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Woods K, Nguyen D, Neph R, Ruan D, O'Connor D, Sheng K. A sparse orthogonal collimator for small animal intensity-modulated radiation therapy part I: Planning system development and commissioning. Med Phys 2019; 46:5703-5713. [PMID: 31621920 DOI: 10.1002/mp.13872] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 12/18/2022] Open
Abstract
PURPOSE To achieve more translatable preclinical research results, small animal irradiation needs to more closely simulate human radiotherapy. Although the clinical gold standard is intensity-modulated radiation therapy (IMRT), the direct translation of this method for small animals is impractical. In this study we describe the treatment planning system for a novel dose modulation device to address this challenge. METHODS Using delineated target and avoidance structures, a rectangular aperture optimization (RAO) problem was formulated to penalize deviations from a desired dose distribution and limit the number of selected rectangular apertures. RAO was used to create IMRT plans with highly concave targets in the mouse brain, and the plan quality was compared to that using a hypothetical miniaturized multileaf collimator (MLC). RAO plans were also created for a realistic application of mouse whole liver irradiation and for a highly complex two-dimensional (2D) dose distribution as a proof-of-principle. Beam commissioning data, including output and off-axis factors and percent depth dose (PDD) curves, were acquired for our small animal irradiation system and incorporated into the treatment planning system. A plan post-processing step was implemented for aperture size-specific dose recalculation and aperture weighting reoptimization. RESULTS The first RAO test case achieved highly conformal doses to concave targets in the brain, with substantially better dose gradient, conformity, and target dose homogeneity than the hypothetical miniaturized MLC plans. In the second test case, a highly conformal dose to the liver was achieved with significant sparing of the kidneys. RAO also successfully replicated a complex 2D dose distribution with three prescription dose levels. Energy spectra for field sizes 1 to 20 mm were calculated to match the measured PDD curves, with maximum and mean dose deviations of 4.47 ± 0.30% and 1.71 ± 0.18%. The final reoptimization of aperture weightings for the complex RAO test plan was able to reduce the maximum and mean dose deviations between the optimized and recalculated dose distributions from 10.3% to 6.6% and 4.0% to 2.8%, respectively. CONCLUSIONS Using the advanced optimization techniques, complex IMRT plans were achieved using a simple dose modulation device. Beam commissioning data were incorporated into the treatment planning process to more accurately predict the resulting dose distribution. This platform substantially reduces the gap in treatment plan quality between clinical and preclinical radiotherapy, potentially increasing the value and flexibility of small animal studies.
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Affiliation(s)
- Kaley Woods
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Nguyen
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ryan Neph
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel O'Connor
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ke Sheng
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, 90095, USA
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16
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Barragán‐Montero AM, Nguyen D, Lu W, Lin MH, Norouzi‐Kandalan R, Geets X, Sterpin E, Jiang S. Three‐dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations. Med Phys 2019; 46:3679-3691. [DOI: 10.1002/mp.13597] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/12/2019] [Accepted: 05/10/2019] [Indexed: 12/23/2022] Open
Affiliation(s)
- Ana María Barragán‐Montero
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) UCLouvain Brussels Belgium
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
| | - Weiguo Lu
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
| | - Roya Norouzi‐Kandalan
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
| | - Xavier Geets
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) UCLouvain Brussels Belgium
- Department of Radiation Oncology Cliniques universitaires Saint‐Luc Brussels Belgium
| | - Edmond Sterpin
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) UCLouvain Brussels Belgium
- Laboratory of Experimental Radiotherapy, Department of Oncology KU Leuven Leuven Belgium
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA
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17
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Smyth G, Evans PM, Bamber JC, Bedford JL. Recent developments in non-coplanar radiotherapy. Br J Radiol 2019; 92:20180908. [PMID: 30694086 PMCID: PMC6580906 DOI: 10.1259/bjr.20180908] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/15/2019] [Accepted: 01/17/2019] [Indexed: 11/05/2022] Open
Abstract
This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.
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Affiliation(s)
- Gregory Smyth
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Jeffrey C Bamber
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - James L Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
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18
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Nagtegaal SHJ, David S, van der Boog ATJ, Leemans A, Verhoeff JJC. Changes in cortical thickness and volume after cranial radiation treatment: A systematic review. Radiother Oncol 2019; 135:33-42. [PMID: 31015168 DOI: 10.1016/j.radonc.2019.02.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 12/10/2018] [Accepted: 02/15/2019] [Indexed: 12/23/2022]
Abstract
Cognitive decline has a clear impact on quality of life in patients who have received cranial radiation treatment. The pathophysiological process is most likely multifactorial, with a possible role for decreased cortical thickness and volume. As radiotherapy treatment systems are becoming more sophisticated, precise sparing of vulnerable regions and tissue is possible. This allows radiation oncologists to make treatment more patient-tailored. A systematic search was performed to collect and review all available evidence regarding the effect of cranial radiation treatment on cortical thickness and volume. We searched the Pubmed, Embase and Cochrane databases, with an additional reference check in the Scopus database. Studies that examined cortical changes on MRI within patients as well as between treated and non-treated patients were included. The quality of the studies was assessed with a checklist specially designed for this review. No meta-analysis was performed due to the lack of randomised trials. Out of 1915 publications twenty-one papers were selected, of which fifteen observed cortical changes after radiation therapy. Two papers reported radiation-dependent decrease in cortical thickness within patients one year after radiation treatment, suggesting a clear relation between the two. However, study quality was considered mostly suboptimal, and there was great inhomogeneity between the included studies. This means that, although there has been increasing interest in the effects of radiation treatment on cortex morphology, no reliable conclusion can be drawn based on the currently available evidence. This calls for more research, preferably with a sufficiently large patient population, and adequate methodology.
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Affiliation(s)
- Steven H J Nagtegaal
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands.
| | - Szabolcs David
- Image Sciences Institute, University Medical Center, Utrecht, the Netherlands.
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center, Utrecht, the Netherlands.
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands.
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