Diagnostic Computed Tomography Enabled Planning for Palliative Radiation Therapy: Removing the Need for a Planning Computed Tomography Scan

Wong, Shelley; Roderick, Stephanie; Kejda, Alannah; Atyeo, John; Grimberg, Kylie; Porter, Brian F.; Booth, Jeremy; Hruby, George; Eade, Thomas

doi:10.1016/j.prro.2020.10.010

Cited by 34 publications

(37 citation statements)

References 9 publications

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“…In terms of clinical application, integration of automatic diagnostic CT-based planning will require more investigation into how to duplicate the patient positioning from the diagnostic CT to the linear accelerator treatment table. Wong et al and Schuler et al studied the challenges related to differences between HU, field of view, and patient positioning for planning using the diagnostic CT vs simulation CT and implemented a working procedure into their clinic 8,48 . In addition, they found that the PET-CT was best suited for diagnostic CT-based planning for thoracic and lumbar regions of the spine.…”

Section: Discussionmentioning

confidence: 99%

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

Netherton

Nguyen

Cárdenas

et al. 2022

International Journal of Radiation Oncology*Biology*Physics

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Section: Discussionmentioning

confidence: 99%

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

Netherton

Nguyen

Cárdenas

et al. 2022

International Journal of Radiation Oncology*Biology*Physics

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“…However, recent research on the use of diagnostic imaging for planning and simulation-free techniques could be appropriately used to improve the efficiency of the system. [30][31][32] For treatment monitoring, telehealth and remote symptom monitoring applications could be used to aid in managing the patients' health during treatment. 33,34 Although, the effectiveness may be limited by patient access to internet service or willingness to participate in telemedicine.…”

Section: Discussionmentioning

confidence: 99%

Techno-Economic Feasibility Analysis of a Fully Mobile Radiation Oncology System Using Monte Carlo Simulation

Price

Canfield

Hugo

et al. 2022

JCO Global Oncology

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PURPOSE Disparities in radiation oncology (RO) can be attributed to geographic location, socioeconomic status, race, sex, and other societal factors. One potential solution is to implement a fully mobile (FM) RO system to bring radiotherapy to rural areas and reduce barriers to access. We use Monte Carlo simulation to quantify techno-economic feasibility with uncertainty, using two rural Missouri scenarios. METHODS Recently, a semimobile RO system has been developed by building an o-ring linear accelerator (linac) into a mobile coach that is used for temporary care, months at a time. Transitioning to a more FM-RO system, which changes location within a given day, presents technical challenges including logistics and quality assurance. This simulation includes cancer census in both northern and southeastern Missouri, multiple treatment locations within a given day, and associated expenditures and revenues. A subset of patients with lung, breast, and rectal diseases, treated with five fractions, was simulated in the FM-RO system. RESULTS The FM-RO can perform all necessary quality assurance tests as suggested in national medical physics guidelines within 1.5 hours, thus demonstrating technological feasibility. In northern and southeastern Missouri, five-fraction simulations' net incomes were, in US dollars (USD), $1.55 ± 0.17 million (approximately 74 patients/year) and $3.65 USD ± 0.25 million (approximately 98 patients/year), respectively. The number of patients seen had the highest correlation with net income as well as the ability to break-even within the simulation. The model does not account for disruptions in care or other commonly used treatment paradigms, which may lead to differences in estimated economic return. Overall, the mobile system achieved a net benefit, even for the most negative simulation scenarios. CONCLUSION Our simulations suggest technologic success and economic viability for a FM-RO system within rural Missouri and present an interesting solution to address other geographic disparities in access to radiotherapy.

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“…They created a full-body vac-bag to replicate the curvature of a diagnostic CT and treated 30 patients prospectively. They found that Hounsfield unit variations between the two scans were greatest in the thorax, and that the 95% dose coverage (V95) varied between -2% and 2.5% [ 5 ]. Finally, a third, separate trial by Schuler et al treated 150 patients with pain from osseous or soft-tissue metastases, without CT-simulation and noted that the rates of pain response, including pain resolution, were not significantly different from randomized historical controls [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

“…These potential shifts can be monitored and addressed through daily image guidance, particularly a cone-beam CT scan, for at least the first fraction. Alternatively, one of the previously mentioned studies overcame this difference by using a vacuum-locked body bag molded in a curved shape similar to a diagnostic CT bed [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

Dosimetry Comparison of Palliative Radiation Plans Generated From Available Diagnostic CT Images Versus Dedicated CT Simulation for Inpatients

Ho¹,

Smith-Raymond²,

Locke³

et al. 2021

Cureus

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Diagnostic Computed Tomography Enabled Planning for Palliative Radiation Therapy: Removing the Need for a Planning Computed Tomography Scan

Cited by 34 publications

References 9 publications

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

Techno-Economic Feasibility Analysis of a Fully Mobile Radiation Oncology System Using Monte Carlo Simulation

Dosimetry Comparison of Palliative Radiation Plans Generated From Available Diagnostic CT Images Versus Dedicated CT Simulation for Inpatients

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