Engineering topics

Galvin, J

Publications and source records attributed to Galvin, J.

3 records

DOE OSTI2016

SU-F-P-13: NRG Oncology Medical Physics Manpower Survey Quantifying Support Demands for Multi Institutional Clinical Trials

Purpose: A survey was taken by NRG Oncology to assess Full Time Equivalent (FTE) contributions to multi institutional clinical trials by medical physicists.No current quantification of physicists’ efforts in FTE units associated with clinical trials is available. The complexity of multi-institutional trials increases with new technologies and techniques. Proper staffing may directly impact the quality of trial data and outcomes. The demands on physics time supporting clinical trials needs to be assessed. Methods: The NRG Oncology Medical Physicist Subcommittee created a sixteen question survey to obtain this FTE data. IROC Houston distributed the survey to their list of 1802 contact physicists. Results: After three weeks, 363 responded (20.1% response). 187 (51.5%) institutions reporting external beam participation were processed. There was a wide range in number of protocols active and supported at each institution. Of the 187 clinics, 134 (71.7%) participate in 0 to 10 trials, 28 (15%) in 11 to 20 trials, 10 (5.3%) in 21 to 30 trials, 9 (4.8%) had 40 to 75 trials. On average, physicist spent 2.7 hours (SD: 6.0) per week supervising or interacting with clinical trial staff. 1.25 hours (SD: 3.37), 1.83 hours (SD: 4.13), and 0.64 hours(SD: 1.13) per week were spent on patient simulation, reviewing treatment plans, and maintaining a DICOM server, respectively. For all protocol credentialing activities, physicist spent an average of 37.05 hours (SD: 96.94) yearly. To support dosimetrists, clinicians, and therapists, physicist spend on average 2.07 hours (SD: 3.52) per week just reading protocols. Physicist attended clinical trial meetings for on average 1.13 hours (SD: 1.85) per month. Conclusion: Responding physicists spend a nontrivial amount of time: 8.8 hours per week (0.22 FTE) supporting, on average, 9 active multi-institutional clinical trials.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-237: The Imaging and Radiation Oncology Core (IROC) Cooperatives Activities Supporting the NCI’s National Clinical Trial Network

Purpose: The Imaging and Radiation Oncology Core (IROC) Cooperative has been active for the past two years supporting the National Clinical Trial Network and the details of that support are reported. Methods: There are six QA centers (Houston, Ohio, Philadelphia-RT, Philadelphia-DI, Rhode Island, St. Louis) providing an integrated RT and DI quality control program in support of the NCI’s clinical trials. The QA Center’s efforts are focused on assuring high quality data for clinical trials designed to improve the clinical outcomes for cancer patients worldwide. This program is administered through five core services: site qualification, trial design support, credentialing, data management, and case review. Results: IROC currently provides core support for 172 NCTN trials with RT, DI and RT/DI components. Many of these trials were legacy trial from the previous cooperative group program. IROC monitors nearly 1800 RT photon and 20 proton institutions. Over 28,000 beams outputs were monitored with 8% of the sites requiring repeat audits due to beam out of criteria. As part of credentialing, 950 QA phantoms have been irradiated, 515 imaging modalities evaluated and almost 4000 credentialing letters have been issued. In just year 2, 5290 RT and 4934 DI patient datasets were received (many using TRIAD) by IROC QA Centers to be prepared for review. During the past 2 years, a total of 6300 RT cases and 19,000 DI image sets were reviewed by IROC technical staff. To date, IROC has published 36 manuscripts. Conclusion: The QA services provided by IROC are numerous and are continually being evaluated for effectiveness, harmonized across all NCTN Groups and administered in an efficient and timely manner to enhance accurate and per protocol trial data submission. These efforts increase each NCTN Group’s ability to derive meaningful outcomes from their clinical trials. This work was supported by DHHS NIH grant 5U24CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly.

60 APPLIED LIFE SCIENCES↗