Comment on “Dosimetric evaluations of the interplay effect in respiratory‐gated intensity‐modulated radiation therapy” [Med. Phys. 36, 893–903 (2009)]

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134968/1/mp2483.pd

Long-Term Results of Conservative Surgery and Radiotherapy for Ductal Carcinoma In Situ Using Lung Density Correction: The University of Michigan Experience

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72520/1/j.1524-4741.2007.00447.x.pd

Inverse plan optimization accounting for random geometric uncertainties with a multiple instance geometry approximation (MIGA)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135142/1/mp1016.pd

Safety considerations for IMRT: Executive summary

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98742/1/MPH005067.pd

Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134937/1/mp0218.pd

IsoBED: a tool for automatic calculation of biologically equivalent fractionation schedules in radiotherapy using IMRT with a simultaneous integrated boost (SIB) technique

<p>Abstract</p> <p>Background</p> <p>An advantage of the Intensity Modulated Radiotherapy (IMRT) technique is the feasibility to deliver different therapeutic dose levels to PTVs in a single treatment session using the Simultaneous Integrated Boost (SIB) technique. The paper aims to describe an automated tool to calculate the dose to be delivered with the SIB-IMRT technique in different anatomical regions that have the same Biological Equivalent Dose (BED), i.e. IsoBED, compared to the standard fractionation.</p> <p>Methods</p> <p>Based on the Linear Quadratic Model (LQM), we developed software that allows treatment schedules, biologically equivalent to standard fractionations, to be calculated. The main radiobiological parameters from literature are included in a database inside the software, which can be updated according to the clinical experience of each Institute. In particular, the BED to each target volume will be computed based on the alpha/beta ratio, total dose and the dose per fraction (generally 2 Gy for a standard fractionation). Then, after selecting the reference target, i.e. the PTV that controls the fractionation, a new total dose and dose per fraction providing the same isoBED will be calculated for each target volume.</p> <p>Results</p> <p>The IsoBED Software developed allows: 1) the calculation of new IsoBED treatment schedules derived from standard prescriptions and based on LQM, 2) the conversion of the dose-volume histograms (DVHs) for each Target and OAR to a nominal standard dose at 2Gy per fraction in order to be shown together with the DV-constraints from literature, based on the LQM and radiobiological parameters, and 3) the calculation of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) curve versus the prescribed dose to the reference target.</p

Monte Carlo vs. Pencil Beam based optimization of stereotactic lung IMRT

<p>Abstract</p> <p>Background</p> <p>The purpose of the present study is to compare finite size pencil beam (fsPB) and Monte Carlo (MC) based optimization of lung intensity-modulated stereotactic radiotherapy (lung IMSRT).</p> <p>Materials and methods</p> <p>A fsPB and a MC algorithm as implemented in a biological IMRT planning system were validated by film measurements in a static lung phantom. Then, they were applied for static lung IMSRT planning based on three different geometrical patient models (one phase static CT, density overwrite one phase static CT, average CT) of the same patient. Both 6 and 15 MV beam energies were used. The resulting treatment plans were compared by how well they fulfilled the prescribed optimization constraints both for the dose distributions calculated on the static patient models and for the accumulated dose, recalculated with MC on each of 8 CTs of a 4DCT set.</p> <p>Results</p> <p>In the phantom measurements, the MC dose engine showed discrepancies < 2%, while the fsPB dose engine showed discrepancies of up to 8% in the presence of lateral electron disequilibrium in the target. In the patient plan optimization, this translates into violations of organ at risk constraints and unpredictable target doses for the fsPB optimized plans. For the 4D MC recalculated dose distribution, MC optimized plans always underestimate the target doses, but the organ at risk doses were comparable. The results depend on the static patient model, and the smallest discrepancy was found for the MC optimized plan on the density overwrite one phase static CT model.</p> <p>Conclusions</p> <p>It is feasible to employ the MC dose engine for optimization of lung IMSRT and the plans are superior to fsPB. Use of static patient models introduces a bias in the MC dose distribution compared to the 4D MC recalculated dose, but this bias is predictable and therefore MC based optimization on static patient models is considered safe.</p

Factors affecting the implementation of complex and evolving technologies: multiple case study of intensity-modulated radiation therapy (IMRT) in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Research regarding the decision to adopt and implement technological innovations in radiation oncology is lacking. This is particularly problematic since these technologies are often complex and rapidly evolving, requiring ongoing revisiting of decisions regarding which technologies are the most appropriate to support. Variations in adoption and implementation decisions for new radiation technologies across cancer centres can impact patients' access to appropriate and innovative forms of radiation therapy. This study examines the key steps in the process of adopting and implementing intensity modulated radiation therapy (IMRT) in publicly funded cancer centres and identifies facilitating or impeding factors.</p> <p>Methods</p> <p>A multiple case study design, utilizing document analysis and key informant interviews was employed. Four cancer centres in Ontario, Canada were selected and interviews were conducted with radiation oncologists, medical physicists, radiation therapists, and senior administrative leaders.</p> <p>Results</p> <p>Eighteen key informants were interviewed. Overall, three centres made fair to excellent progress in the implementation of IMRT, while one centre achieved only limited implementation as of 2009. Key factors that influenced the extent of IMRT implementation were categorized as: 1) leadership, 2) training, expertise and standardization, 3) collaboration, 4) resources, and 5) resistance to change.</p> <p>Conclusion</p> <p>A framework for the adoption and implementation of complex and evolving technologies is presented. It identifies the key factors that should be addressed by decision-makers at specific stages of the adoption/implementation process.</p