Dosimetric improvements in a cohort of patients with simulated tracheal tumors receiving intensitymodulated radiotherapy (IMRT) accounting for optimization convergence errors (OCEs)

Todd J. Scarbrough, M.D.*; Samuel R. Faught, M.D.*; Charles R. Thomas, Jr., M.D.†* OMHS Mitchell Memorial Cancer Center, Dept. of Radiation Oncology, Owensboro, Kentucky, USA† Oregon Health & Science University, Dept. of Radiation Medicine, Portland, Oregon, USA

OMHS Mitchell Memorial Cancer Center1020 Breckenridge Street, Owensboro, Kentucky 42303 USA

email: scarbrtj@gmail.comphone: 1 270-231-6269

BACKGROUNDIn many commercially available treatment planning systems (TPSs), when planning IMRT, the fluence optimization process uses a different algorithm than the actual dose calculation work-space. In the Eclipse™ treatment planning system (v8.9.09.18617, Varian Medical Systems, Palo Alto, CA, USA), for static field IMRT, the fluence optimization algorithm used is dose volume opti-mizer (DVO) v8.9.08. Of the calculation algorithms available in Eclipse, there is evidence that the analytical anisotropic algo-rithm (AAA v8.9.08) is significantly more accurate in predicting delivered dose in most clinical situations, especially air/tissue in-terface scenarios as encountered in lung or tracheal carcinoma cases. However, the discrepancy in the DVO and AAA calcula-tion algorithms induces optimization convergence errors (OCEs) which are especially noticeable in the TPS in these particular clini-cal situations: doses become significantly inhomogenous within targets such that plans must be normalized to very low isodose lines in turn inducing unwanted high target inhomogeneities and/or hot spots in the plan. Zacarias and Mills (J Appl Clin Med Phys, 10:3061, 2009) have devised a fluence summing method to correct for OCEs in the Eclipse™ TPS by forcing the DVO to look at calculated AAA results. We selected a non-random cohort of 10 patients from our institution for this treatment planning study and created, within the TPS, simulated tissue masses (using the assign CT number function to voxels in the Eclipse™ TPS) within the mid-trachea 3 cm in longitudinal dimension and having AP and lateral dimensions equal to half the diameter of the trachea. We then performed IMRT plans on these simulated masses with or without OCE corrections, and recorded the results.

METHODSThe masses were created as above and constituted the CTV with-in the simulated plans on 10 patients who had previously been scanned and treated in the department for thoracic malignancies (and were deceased); these patients previous scans served as the planning phantoms for this study. A 5 mm isotropic margin was

added to the CTVs to create PTVs within the plans. The median CTV volume was 4.5 ccs; PTV volume, 17.8 ccs. A nine-field equal-ly spaced coplanar static field beam arrangement was employed using dMLCs, 600MU/min dose rate. The PTV was optimized to receive 100% of the Rx dose (60 Gy/30 fx). The maximum plan in-homogeneity at optimization was set to be 62 Gy. A smoothing factor of 20/20 X/Y was used for optimization. No other optimi-zation parameters were utilized. Eclipse™ DVO v8.9.08 was used for optimization. After initial optimization, plans were calculated using Eclipse™ AAA v8.9.08 (Plan 1). All plans were normalized such that 95% of the PTV received 100% of the Rx dose or more. PTV minimum, median and maximum doses were recorded, and PTV conformity indices (CIs) calculated. Next, each Plan 1 was re-optimized and re-calculated (again using AAA) to correct for OCEs using the method and tools of Zacarias and Mills (Plan 2). A smoothing factor of 0/0 X/Y was used for OCE-corrected opti-mization. PTV minimum, median and maximum doses, and PTV CIs, were recorded for each Plan 2. Plan metrics were compared using the Mann-Whitney test.

RESULTS Plan 1 non-OCE and Plan 2 OCE-corrected plans differed signifi-cantly. PTV minimum dose differed for Plan 1 vs 2 (median 56.5 Gy vs 58.3 Gy, p=0.0004). PTV median dose differed for Plan 1 vs 2 (median 62.6 Gy vs 60.7 Gy, p=0.0002). PTV maximum dose dif-fered for Plan 1 vs 2 (67.4 Gy vs 62.2 Gy, p=0.0002). The CI differed for Plan 1 vs 2 (median 1.77 vs 1.67, p=0.0002). Plan 1 calculations revealed significantly more target inhomogeneity (median 112%) than Plan 2 calculations (median 104%), normalizing 95% of the PTV volumes to receive 60 Gy Rx dose or more.

CONCLUSIONS Correcting for OCEs in this simulated group of patients with in-traluminal tracheal tumors resulted in significantly more confor-mal and homogenous plans.

contour as usual:normal structures,

CTVs, PTVs, etc.

initial optimization:

PLAN_base

Re-optimize usingPLAN_base as a base dose

plan:PLAN_dosecorr

sum plans:

1) convert �uences to .txt �les dcm2Ascii.exe (on C: drive)

2) sum �uences in Microsoft Excel http://tinyurl.com/imrt-oce

3) import new summed �uences into each new �eld

PLAN_�nalNormalize as usual; if �nal

plan is unacceptable,repeat re-optimization

FIGURE 3. Correcting for OCEs. An initial plan is optimized & calculated in Eclipse. Next, a second plan using the initial plan as a base dose plan is optimized and calculated. The two plans’ fluences are summed beam-by-beam using the simple software pro-grams outlined above. The summed fluences are imported back into Eclipse, replacing the initial fluences from the initial plans. This plan is calculated, and then normalized per the planner’s specifications. The resultant plan usually results in significantly bet-ter homogeneity and overall PTV coverage.

FIGURE 2. Nine-field, coplanar, equispaced beam arrangement as used for all plans.

FIGURE 1. Simulated tracheal tumor (the clinical target volume, CTV) created to occupy ½ the tracheal lumen, and an inferior-superior extent of 3 cm. The CTV was assigned zero Hounsfield units. A margin of 5 mm was added to create the planning target volume (PTV), which was the optimization target.

FIGURE 4. Optimization parameters for “PLAN_base” as outlined in the METHODS section.

FIGURE 5. The fluence patterns for each plan, as created by the optimization algorithm, are converted to numerical text format (and individual “.txt” files) by the dcm2ascii.exe program. The .txt files are then loaded into a custom-designed Microsoft Excel macro (http://tinyurl.com/imrt-oce); this yields new summed fluences which are then loaded back into the Eclipse treatment planning software. For most situations, only a single dose-correction plan is necessary to yield desired calculated plan results.

Plan inhomogeneity max: 62 Gy

PTV prescription: 60 Gy

For PLAN_dosecorr, PLAN_base is selected as a base dose plan, and 0/0 smoothing is set, as well.

PLAN_base PLAN_final

The majority of the PTV is cov-ered by ≥105% of the pre-

scribed dose due to opti-mization convergence

errors generated by the DVO algorithm. The PTV has a maxi-

mum dose inhomo-geneity of 11.7%. This could be clini-cally significant depending on PTV overlap into critical structures.

For PLAN_final, only a small pro-portion of the PTV volume is re-ceiving ≥103.5% of the dose and the maximum inhomogeneity in the PTV is only 4.7%. Furthermore, less monitor units will be used in PLAN_final vs. PLAN_base, which re-sults in slightly faster treatment times and slightly less scatter dose to the patient.

FIGURE 6. Correcting for OCEs generates plans which result in significantly less target inhomogeneity and significantly better overall dose conformity.

Air/tissue interfaces (or high density/low

density interfaces) are regions in the

body where radia-tion dose absorption

changes rapidly. The anisotropic analytic al-gorithm (AAA) models this reasonably well, and is a dose calculation al-gorithm available in the Eclipse treatment planning system. However, the optimi-zation algorithm for static field IMRT (dose volume optimizer, DVO) does not model this dose phenomenon as well as AAA. Yet AAA uses the fluences created by the DVO.

We can “correct” the relative DVO error by running second plans using AAA cal-culation of the first plan as a base dose plan. Then, we sum the fluences of

each beam from each plan togeth-

er, and re-calculate the new plan using AAA.

“problem”areas