07/06/2022
By Matthew C. Schmidt

The Biomedical Engineering and Biotechnology program invites you to attend a doctoral dissertation defense by Matthew C. Schmidt on “API Assisted Automation in Radiation Therapy: Applications in Standardizing and Streamlining Linear Accelerator Quality Assurance and Treatment Planning”.

Candidate Name: Matthew C. Schmidt
Defense Date: (Day of the week, Month, Day, Year) Monday, July 18, 2022
Time (from/to): 3- 5:30 p.m. EST
Location: This will be a virtual defense via Zoom. Those interested in attending should contact the student (matthew_schmidt1@student.uml.edu) and committee advisor (erno_sajo@uml.edu) at least 24 hours prior to the defense to request access to the meeting.

Committee:
Advisor: Erno Sajo, Ph.D., Professor - Biomedical Engineering & Biotechnology, Director – Medical Physics, University of Massachusetts Lowell

Committee Members
Piotr Zygmanski, Ph.D, Associate Professor of Radiation Oncology, Radiation Oncology, Brigham and Women’s Hospital
Marian Jandel, Ph.D, Assistant Professor, Physics, University of Massachusetts Lowell
Nels Knutson, Ph.D, Assistant Professor, Radiation Oncology, Washington University in St. Louis School of Medicine
Francisco Reynoso, Ph.D, Assistant Professor, Radiation Oncology, Washington University in St. Louis School of Medicine
Baozhou Sun, Ph.D, Associate Professor, Radiation Oncology, Washington University in St. Louis School of Medicine

Brief Abstract:
Introduction:
The generation of a treatment plan by the Treatment Planning System (TPS) is a critical and time-consuming process in the delivery of Radiation therapy. Methods for quality assurance of the linear accelerator and dosimetric quality of the treatment plan have been improved with modern technologies over the years. Commercial Treatment Planning Systems have adopted Application Programming Interfaces (APIs) to allow for the customization of software generated tools within the Treatment Planning System, empowering users to fill gaps in clinical workflow left by the TPS.
Methods:
Utilizing multiple APIs within the TPS and Oncology Information System (OIS) environment, a QA application development system was generated. The AutoQA Builder allows for clinicians to generate new patients and QA plans for periodic linear accelerator QA while the AutoQA Analysis programmatically analyzes the QA images. The validation of both tools was performed over a 6-month period on 3 linear accelerators for common QA tests. An automated treatment planning platform, QuickPlan, was also developed from the Eclipse Scripting API (ESAPI) utilizing the Clinical Template Reader class library to interpret planning templates into programmable objects for treatment plan creation. The Clinical Template Reader was validated through the generation of 39 retrospective pelvis and prostate patients. The QuickPlan application was validated on 22 retrospective Hippocampal-Avoidance Whole-Brain Radiotherapy patients with 10 prospective patients as the tool continues to develop plans in clinical use.
Results:
The AutoQA Builder has been used in the generation of 184 QA plans, most of which are performed daily. The AutoQA Analysis was compared against DoseLab, showing similar results for tests such as the Junction Test, Light vs. Radiation coincidence test, picket fence tests, and RapidArc QA test. The Clinical Template Reader generated 37 pelvis and prostate patients successfully. In this validation set, the plans generated with automation showed significant improvements in mean generalized equivalent uniform dose (gEUD) for the femurs and rectum with mean gEUD decreasing by 127 cGy (p ≤ 0.001) and 408 cGy (p ≤ 0.001), respectively. Other analyzed dose metrics for rectum and bladder decreased with plan automation, but not significantly so, while target coverage improved by a non-significant amount. Utilizing the Clinical Template Reader into a generalized automated planning platform, QuickPlan, 22 automated treatment plans were generated wherein the median planning time between manual and automated planning decreased by 9’ 50” ± 4’ 33”.
Conclusions:
API-assisted automation allows for the standardization of both quality assurance and treatment planning. QA development allows for a safe and clinically effective workspace for gaining comfort with API features and programmatic workflow. Combining API features with clinical templates describing the design and clinical goals of treatment plans have the potential to generate clinically similar plans in a shorter time.

All interested students and faculty members are invited to attend the online defense via remote access.