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Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality

S Robertson

SP Robertson1*, JA Moore1 , X Hui1 , TL DeWeese2 , P Tran2 , H Quon2 , Z Cheng1 , K Bzdusek3 , P Kumar4 , TR McNutt1 , (1) Johns Hopkins University, Baltimore, MD, (2) John Hopkins Hospital, Baltimore, MD, (3) Philips, Fitchburg, WI, (4) Philips India Limited, Bangalore, Karnataka


SU-D-BRB-2 (Sunday, July 31, 2016) 2:05 PM - 3:00 PM Room: Ballroom B

Purpose: Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality.

Methods: Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan_A: traditional IMRT optimization using clinically relevant default objectives; Plan_B: traditional IMRT optimization using database dose predictions; Plan_C: autoplanning using default objectives; and Plan_D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volume (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%).

Results: All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman’s χ² ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A-D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A-D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B-D were significantly different from Plan_A for 3, 17, and 19 objectives, respectively. Plans C-D were also significantly different from Plan_B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan_D provided significantly lower dose than plan C (p = 0.003).

Conclusion: Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted in a larger cohort across HN, prostate, and other treatment sites.

Funding Support, Disclosures, and Conflict of Interest: This work is supported by Philips Radiation Oncology Systems.

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