AAPM Task Group 329: Reference dose specification for dose calculations: Dose-to-water or dose-to-muscle?

Linac calibration is done in water, but patients are comprised primarily of soft tissue. Conceptually, and specified in NRG/RTOG trials, dose should be reported as dose-to-muscle to describe the dose to the patient. Historically, the dose-to-water of the linac calibration was often converted to dose-to-muscle for patient calculations through manual application of a 0.99 dose-to-water to dose-to-muscle correction factor, applied during the linac clinical reference calibration. However, many current treatment planning system (TPS) dose calculation algorithms approximately provide dose-to-muscle (tissue), making application of a manual scaling unnecessary. There is little guidance on when application of a scaling factor is appropriate, resulting in highly inconsistent application of this scaling by the community. In this report we provide guidance on the steps necessary to go from the linac absorbed dose-to-water calibration to dose-to-muscle in patient, for various commercial TPS algorithms. If the TPS does not account for the difference between dose-to-water and dose-to-muscle, then TPS reference dose scaling is warranted. We have tabulated the major vendors' TPS in terms of whether they approximate dose-to-muscle or calculate dose-to-water and recommend the correction factor required to report dose-to-muscle directly from the TPS algorithm. Physicists should use this report to determine the applicable correction required for specifying the reference dose in their TPS to achieve this goal and should remain attentive to possible changes to their dose calculation algorithm in the future.

Quantitatively Excessive Normal Tissue Toxicity and Poor Target Coverage in Postoperative Lung Cancer Radiotherapy Meta-analysis.

BACKGROUND: A previous meta-analysis (MA) found postoperative radiotherapy (PORT) in lung cancer patients to be detrimental in N0/N1 patients and equivocal in the N2 setting. We hypothesized that treatment plans generated using MA protocols had worse dosimetric outcomes compared to modern plans. PATIENTS AND METHODS: We retrieved plans for 13 patients who received PORT with modern planning. A plan was recreated for each patient using the 8 protocols included in MA. Dosimetric values were then compared between the modern and simulated MA plans. RESULTS: A total of 104 MA plans were generated. Median prescribed dose was 50.4 (range, 50-60) Gy in the modern plans and 53.2 (30-60) Gy in the MA protocols. Median planning volume coverage was 96% (93%-100%) in the modern plans, versus 58% (0%-100%) in the MA plans (P < .001). Internal target volume coverage was 100% (99%-100%) versus 65% (0%-100%), respectively (P < .001). Organs at risk received the following doses: spinal cord maximum dose, 36.8 (4.6-50.4) Gy versus 46.8 (2.9-74.0) Gy (P < .001); esophageal mean dose, 22.9 (5.5-35) Gy versus 30.5 (11.1-52.5) Gy (P = .003); heart V30 (percentage of volume of an organ receiving at least a dose of 30 Gy), 16% (0%-45%) versus 35% (0%-79%) (P = .047); mean lung dose, 12.4 (3.4-24.3) Gy versus 14.8 (4.1-27.4) Gy (P = .008); and lung V20, 18% (4%-34%) versus 25% (8%-67%) (P = .023). CONCLUSION: We quantitatively confirm the inferiority of the techniques used in the PORT MA. Our analysis showed a lower therapeutic ratio in the MA plans, which may explain the poor outcomes in the MA. The findings of the MA are not relevant in the era of modern treatment planning.

Technical Note: Motion-perturbation method applied to dosimetry of dynamic MLC target tracking--A proof-of-concept.

PURPOSE: Previous studies show that dose to a moving target can be estimated using 4D measurement-guided dose reconstruction based on a process called virtual motion simulation, or VMS. A potential extension of VMS is to estimate dose during dynamic multileaf collimator (MLC)-tracking treatments. The authors introduce a modified VMS method and quantify its performance as proof-of-concept for tracking applications. METHODS: Direct measurements with a moving biplanar diode array were used to verify accuracy of the VMS dose estimates. A tracking environment for variably sized circular MLC apertures was simulated by sending preprogrammed control points to the MLC while simultaneously moving the accelerator treatment table. Sensitivity of the method to simulated tracking latency (0-700 ms) was also studied. Potential applicability of VMS to fast changing beam apertures was evaluated by modeling, based on the demonstrated dependence of the cumulative dose on the temporal dose gradient. RESULTS: When physical and virtual latencies were matched, the agreement rates (2% global/2 mm gamma) between the VMS and the biplanar dosimeter were above 96%. When compared to their own reference dose (0 induced latency), the agreement rates for VMS and biplanar array track closely up to 200 ms of induced latency with 10% low-dose cutoff threshold and 300 ms with 50% cutoff. Time-resolved measurements suggest that even in the modulated beams, the error in the cumulative dose introduced by the 200 ms VMS time resolution is not likely to exceed 0.5%. CONCLUSIONS: Based on current results and prior benchmarks of VMS accuracy, the authors postulate that this approach should be applicable to any MLC-tracking treatments where leaf speeds do not exceed those of the current Varian accelerators.

Methods, software and datasets to verify DVH calculations against analytical values: Twenty years late(r).

PURPOSE: The authors designed data, methods, and metrics that can serve as a standard, independent of any software package, to evaluate dose-volume histogram (DVH) calculation accuracy and detect limitations. The authors use simple geometrical objects at different orientations combined with dose grids of varying spatial resolution with linear 1D dose gradients; when combined, ground truth DVH curves can be calculated analytically in closed form to serve as the absolute standards. METHODS: dicom RT structure sets containing a small sphere, cylinder, and cone were created programmatically with axial plane spacing varying from 0.2 to 3 mm. Cylinders and cones were modeled in two different orientations with respect to the IEC 1217 Y axis. The contours were designed to stringently but methodically test voxelation methods required for DVH. Synthetic RT dose files were generated with 1D linear dose gradient and with grid resolution varying from 0.4 to 3 mm. Two commercial DVH algorithms-pinnacle (Philips Radiation Oncology Systems) and PlanIQ (Sun Nuclear Corp.)-were tested against analytical values using custom, noncommercial analysis software. In Test 1, axial contour spacing was constant at 0.2 mm while dose grid resolution varied. In Tests 2 and 3, the dose grid resolution was matched to varying subsampled axial contours with spacing of 1, 2, and 3 mm, and difference analysis and metrics were employed: (1) histograms of the accuracy of various DVH parameters (total volume, Dmax, Dmin, and doses to % volume: D99, D95, D5, D1, D0.03 cm(3)) and (2) volume errors extracted along the DVH curves were generated and summarized in tabular and graphical forms. RESULTS: In Test 1, pinnacle produced 52 deviations (15%) while PlanIQ produced 5 (1.5%). In Test 2, pinnacle and PlanIQ differed from analytical by >3% in 93 (36%) and 18 (7%) times, respectively. Excluding Dmin and Dmax as least clinically relevant would result in 32 (15%) vs 5 (2%) scored deviations for pinnacle vs PlanIQ in Test 1, while Test 2 would yield 53 (25%) vs 17 (8%). In Test 3, statistical analyses of volume errors extracted continuously along the curves show pinnacle to have more errors and higher variability (relative to PlanIQ), primarily due to pinnacle's lack of sufficient 3D grid supersampling. Another major driver for pinnacle errors is an inconsistency in implementation of the "end-capping"; the additional volume resulting from expanding superior and inferior contours halfway to the next slice is included in the total volume calculation, but dose voxels in this expanded volume are excluded from the DVH. PlanIQ had fewer deviations, and most were associated with a rotated cylinder modeled by rectangular axial contours; for coarser axial spacing, the limited number of cross-sectional rectangles hinders the ability to render the true structure volume. CONCLUSIONS: The method is applicable to any DVH-calculating software capable of importing dicom RT structure set and dose objects (the authors' examples are available for download). It includes a collection of tests that probe the design of the DVH algorithm, measure its accuracy, and identify failure modes. Merits and applicability of each test are discussed.

SU-E-J-66: Evaluation of Proton Induced X-Ray Fluorescence from Gold Fiducial Markers for In-Vivo Determination of Proton Range and Energy.

PURPOSE: To evaluate a method for in-vivo determination of proton range and post-Bragg peak straggling by detection of proton induced x-ray fluorescence of markers placed at known locations. METHODS: Therapeutic beams from the UF Proton Therapy Institute were used to excite proton-induced x-ray fluorescence emission (PIXE) from cylindrical pure gold fiducial markers. The markers were embedded in a homogeneous water phantom and PIXE was measured using NaI photodetectors with energy dispersive spectral analysis. The geometry of the phantom and marker placement was chosen to model parallel-opposed beam treatment of prostate cancer by proton therapy. The fluorescence yield from these markers was further modeled using the GEANT4 Monte-Carlo package with low-energy corrections. Gold K and L shell fluorescence yield as determined by the GEANT4 simulations was verified quantitatively by comparison to measured Au yield at energies from 1 MeV to 68 MeV, and to semiempirical model calculations covering the energy range from 1 MeV to 15 MeV. RESULTS: The Au K-shell fluorescence cross section is significantly smaller thanthat of the L-shell, but the higher yield of the L-shell fluorescence isoffset by the larger absorption as the x-ray exits the phantom. The overallrelative detection efficiency of K and L shell fluorescence depends on thedetails of the shape of the phantom and location of the marker. Acharacteristic shape of fluorescence yield as it depends on proton range isfound, which can be used to extract an in-vivo PDD profile of a spread-outBragg peak (SOBP). CONCLUSIONS: A combination of a specific protocol for delivering a SOBP, geometriclocation of fiducial markers from CT, and simultaneous detection of protoninduced x-ray fluorescence, can determine the depth range of a primary protonbeam in-vivo. The fluorescence yield as measured at the Proton Therapy Institute is easily distinguished from background radiation.