Medical Physics Programs
Penn’s Master of Science in Medical Physics degree prepares students to bridge physics and clinical medicine, overseeing clinical applications of radiation and creating the cutting-edge medical technologies of tomorrow. Our two-year program combines the resources of one of the world’s top research universities and most prestigious medical schools, offering an outstanding education and unmatched opportunities.
PublicationExperience Based Quality Control in IMRT Treatment Planning of High Risk Post-Prostatectomy Prostate Cancer with RapidPlan(2017-01-01) O'Grady, Fionnbarr T; Geng, Huaizhi; Huang, Mi; Giaddui, Tawfik; Zong, Haoyu; Xiao, YingPurpose: To develop a knowledge based planning (KBP) model with RapidPlan (Varian Medical Systems, Palo Alto, USA) for the treatment of high risk post-prostatectomy prostate cancer. The model was trained on a knowledge database of high quality treatment plans from the national clinical trial RTOG 0621, then tested as a QA tool. Methods: An initial dosimetric analysis was carried out to identify high quality plans from clinical trial RTOG 0621. Treatment plans for patients enrolled in the trial were scored according to the system used by the Imaging and Radiation Oncology Core (IROC) of the National Clinical Trials Network (NCTN) of the NCI to assess adherence to the trial protocol. Of the 80 plans enrolled in the trial 39 were chosen for the training sample. Another subset of 8 plans, orthogonal to the training sample, was chosen for the validation sample to ensure that the model accurately predicts dose volume histograms (DVHs) for all critical structures. The validation plans were then re-optimized with the model in order to test its effectiveness as a tool for planning QA. DVHs of the re-optimized plans were compared with those of the original clinical plans. Normal tissue complication probabilities and tumor control probabilities were calculated with the Lyman-Kutcher-Burman (LKB) model before and after re-optimization to determine the effect on patient outcome. Results: The RapidPlan prostate model was shown to accurately predict estimated DVH bands for all plans in the validation sample that matched the geometry of the training sample. Three treatment plans in the validation sample were geometric outliers with respect to the training sample leading to inaccuracies in the model predictions for the cone down phase of these treatment plans. All of the re-optimized plans showed increased dose sparring to the bladder and rectum respectively without lose of target coverage. The average reduction in NTCP was 0.34 ± 0.21 % for the bladder and 0.11 ± 0.25 % for the rectum with corresponding p-values of 0.116 and 0.668. The average TCP for the prostate bed decreased slightly from 97.05 % to 96.54 % with a p-value of 0.149. Due to limited statistics the changes reported in these numbers are not statistically significant as indicated by the p-values. Although the average values are inconclusive the model was effectively used to identify sub-optimal treatment plans which were improved through re-optimization with the model. For treatment plan 0621c0027 the NTCP decreased from 0.35 % to 0.06 % for the bladder and from 0.10 % to 0.06 % for the rectum while the TCP increased from 96.78 % to 96.87 %. Conclusions: The RapidPlan prostate model developed in this study is an effective tool for monitoring the quality of IMRT treatment plans for high-risk post prostatectomy prostate cancer. PublicationKnowledge Based Planning for Retrospective Quality Study of RTOG 0822 Radiotherapy Plans(2018-04-25) Meekins, Evan GPurpose: To create a clinically viable dose-volume histogram (DVH) estimation model using the Varian RapidPlan (Varian Medical Systems, Palo Alto USA) knowledge based planning (KBP) platform. This model aims to evaluate locally advanced rectal cancer with 6X IMRT, and was developed on a plan database taken from the RTOG 0822 national clinical trial. This is the first multi-institutional 6X IMRT dose estimation model designed using RapidPlan. The effectiveness of the model as a dosimetry quality assurance (QA) tool was evaluated. Methods: Treatment plans submitted to the RTOG 0822 clinical trial were dosimetrically evaluated for plan quality. Plans whose DVH statistics met RTOG 0822 target criteria were identified as high-quality, and were used in the initial training sample for the model. Of the 97 IMRT plans enrolled in the trial, 58 were treated with only 6X photons, and 26 of those were identified as high-quality plans. All 6X enrolled plans were iteratively re-optimized with the model to test clinical effectiveness, evaluate the model as a tool for treatment planning QA, and continuously expand the model’s training sample. Re-optimized plans which met target criteria were added to the training sample, resulting in a total of 40 geometries in the training sample. Results: The rectal IMRT RapidPlan model created in this paper was shown to accurately predict estimated DVH bands for all viable high-quality plans enrolled in the clinical trial. The model was also able improve the DVH statistics for a significant majority of the low-quality plans enrolled in the clinical trial. Conclusion: The RapidPlan rectal 6X IMRT model created in this study can be used as an effective tool for dosimetry QA and initial plan creation.