T. Pawlicki

University of California, San Diego, San Diego, California, United States

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Publications (106)293.31 Total impact

  • International journal of radiation oncology, biology, physics 11/2015; 93(3):E105. DOI:10.1016/j.ijrobp.2015.07.815 · 4.26 Impact Factor
  • G Kim · R Manger · T Pawlicki ·
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    ABSTRACT: Failure Modes and Effects Analysis (FMEA) techniques have been used to analyze surface image guided radiosurgery (SIG-RS). Hazard model, a modified FMEA approach developed by the Dutch, is applied to SIG-RS risk assessment and evaluated against the AAPM's FMEA approach. The SAFER approach uses a risk inventory matrix to categorize hazards (rather than probabilities). A multidisciplinary team was assembled to create the process map of SIG-RS and 91 steps and 167 failure modes were determined. Each failure mode was categorized for frequency (weekly, monthly, quarterly, yearly and less than once a year) and severity (negligible, minor, moderate, major and catastrophic) according to the SAFER procedures. All failure modes are placed in the matrix of arbitrary risk score matrix: very high, high, low, and very low. The top 14 high risk failure modes from the Result of FMEA and SAFER analysis were compared. 167 failure modes categorized in the risk inventory matrix with 1 very high, 13 high, 66 low and 87 very low. Comparison of top 14 high risk failure modes between two techniques shows 9 common failure modes and 5 isolated failure modes. Two failure modes (FM: 58, 145) with the highest risk priority number (both RPN=288) in FMEA are also ranked as high risk in SAFER analysis. However one failure mode (FM: 154) with very high risk score in SAFER is not recognized by FMEA analysis due to its low "lack of detectability" score. FMEA is a well-established technique for prospective risk analysis. SAFER is a practical alternative that is easy to implement with a reliable category structure. Also the risk inventory matrix is conceptually straightforward to obtain agreement among multidisciplinary team members but still demonstrates a full scale of criticality.
    Medical Physics 06/2015; 42(6):3572. DOI:10.1118/1.4925442 · 2.64 Impact Factor
  • R Manger · T Pawlicki · G Kim ·
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    ABSTRACT: Dosimetry protocols devote so much time to the discussion of ionization chamber choice, use and performance that is easy to forget about the importance of the associated dosimetry equipment (ADE) in radiation dosimetry - barometer, thermometer, electrometer, phantoms, triaxial cables, etc. Improper use and inaccuracy of these devices may significantly affect the accuracy of radiation dosimetry. The purpose of this study is to evaluate the risk factors in the monthly output dosimetry procedure and recommend corrective actions using a TG-100 approach. A failure mode and effects analysis (FMEA) of the monthly linac output check procedure was performed to determine which steps and failure modes carried the greatest risk. In addition, a fault tree analysis (FTA) was performed to expand the initial list of failure modes making sure that none were overlooked. After determining the failure modes with the highest risk priority numbers (RPNs), 11 physicists were asked to score corrective actions based on their ease of implementation and potential impact. The results were aggregated into an impact map to determine the implementable corrective actions. Three of the top five failure modes were related to the thermometer and barometer. The two highest RPN-ranked failure modes were related to barometric pressure inaccuracy due to their high lack-of-detectability scores. Six corrective actions were proposed to address barometric pressure inaccuracy, and the survey results found the following two corrective actions to be implementable: 1) send the barometer for recalibration at a calibration laboratory and 2) check the barometer accuracy against the local airport and correct for elevation. An FMEA on monthly output measurements displayed the importance of ADE for accurate radiation dosimetry. When brainstorming for corrective actions, an impact map is helpful for visualizing the overall impact versus the ease of implementation.
    Medical Physics 06/2015; 42(6):3351. DOI:10.1118/1.4924448 · 2.64 Impact Factor
  • T Harry · S Yaddanapudi · B Cai · S Goddu · C Noel · S Mutic · T Pawlicki ·
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    ABSTRACT: New techniques and materials have recently been developed to expedite the conventional Linac Acceptance Testing Procedure (ATP). The new ATP method uses the Electronic Portal Imaging Device (EPID) for data collection and is presented separately. This new procedure is meant to be more efficient then conventional methods. While not clinically implemented yet, a prospective risk assessment is warranted for any new techniques. The purpose of this work is to investigate the risks and establish the pros and cons between the conventional approach and the new ATP method. ATP tests that were modified and performed with the EPID were analyzed. Five domain experts (Medical Physicists) comprised the core analysis team. Ranking scales were adopted from previous publications related to TG 100. The number of failure pathways for each ATP test procedure were compared as well as the number of risk priority numbers (RPN's) greater than 100 were compared. There were fewer failure pathways with the new ATP compared to the conventional, 262 and 556, respectively. There were fewer RPN's > 100 in the new ATP compared to the conventional, 41 and 115. Failure pathways and RPN's > 100 for individual ATP tests on average were 2 and 3.5 times higher in the conventional ATP compared to the new, respectively. The pixel sensitivity map of the EPID was identified as a key hazard to the new ATP procedure with an RPN of 288 for verifying beam parameters. The significant decrease in failure pathways and RPN's >100 for the new ATP mitigates the possibilities of a catastrophic error occurring. The Pixel Sensitivity Map determining the response and inherent characteristics of the EPID is crucial as all data and hence results are dependent on that process. Grant from Varian Medical Systems Inc.
    Medical Physics 06/2015; 42(6):3394. DOI:10.1118/1.4924631 · 2.64 Impact Factor
  • S Yaddanapudi · B Cai · T Harry · B Sun · H Li · C Noel · S Goddu · T Pawlicki · S Mutic ·
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    ABSTRACT: The purpose of this project was to develop a process that utilizes the onboard kV and MV electronic portal imaging devices (EPIDs) to perform rapid acceptance testing (AT) of linacs in order to improve efficiency and standardize AT equipment and processes. In this study a Varian TrueBeam linac equipped with an amorphous silicon based EPID (aSi1000) was used. The conventional set of AT tests and tolerances was used as a baseline guide, and a novel methodology was developed to perform as many tests as possible using EPID exclusively. The developer mode on Varian TrueBeam linac was used to automate the process. In the current AT process there are about 45 tests that call for customer demos. Many of the geometric tests such as jaw alignment and MLC positioning are performed with highly manual methods, such as using graph paper. The goal of the new methodology was to achieve quantitative testing while reducing variability in data acquisition, analysis and interpretation of the results. The developed process was validated on two machines at two different institutions. At least 25 of the 45 (56%) tests which required customer demo can be streamlined and performed using EPIDs. More than half of the AT tests can be fully automated using the developer mode, while others still require some user interaction. Overall, the preliminary data shows that EPID-based linac AT can be performed in less than a day, compared to 2-3 days using conventional methods. Our preliminary results show that performance of onboard imagers is quite suitable for both geometric and dosimetric testing of TrueBeam systems. A standardized AT process can tremendously improve efficiency, and minimize the variability related to third party quality assurance (QA) equipment and the available onsite expertise. Research funding provided by Varian Medical Systems. Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.
    Medical Physics 06/2015; 42(6):3190. DOI:10.1118/1.4923790 · 2.64 Impact Factor
  • D Zaks · R Fletcher · S Salamon · G Kim · T Ning · M Cornell · T Pawlicki · L Cervino ·
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    ABSTRACT: To develop an online framework that tracks a patient's plan from initial simulation to treatment and that helps automate elements of the physics plan checks usually performed in the record and verify (RV) system and treatment planning system. We have developed PlanTracker, an online plan tracking system that automatically imports new patients tasks and follows it through treatment planning, physics checks, therapy check, and chart rounds. A survey was designed to collect information about the amount of time spent by medical physicists in non-physics related tasks. We then assessed these non-physics tasks for automation. Using these surveys, we directed our PlanTracker software development towards the automation of intra-plan physics review. We then conducted a systematic evaluation of PlanTracker's accuracy by generating test plans in the RV system software designed to mimic real plans, in order to test its efficacy in catching errors both real and theoretical. PlanTracker has proven to be an effective improvement to the clinical workflow in a radiotherapy clinic. We present data indicating that roughly 1/3 of the physics plan check can be automated, and the workflow optimized, and show the functionality of PlanTracker. When the full system is in clinical use we will present data on improvement of time use in comparison to survey data prior to PlanTracker implementation. We have developed a framework for plan tracking and automatic checks in radiation therapy. We anticipate using PlanTracker as a basis for further development in clinical/research software. We hope that by eliminating the most simple and time consuming checks, medical physicists may be able to spend their time on plan quality and other physics tasks rather than in arithmetic and logic checks. We see this development as part of a broader initiative to advance the clinical/research informatics infrastructure surrounding the radiotherapy clinic. This research project has been financially supported by Varian Medical Systems, Palo Alto, CA, through a Varian MRA.
    Medical Physics 06/2015; 42(6):3210. DOI:10.1118/1.4923871 · 2.64 Impact Factor
  • T Pawlicki · D Brown · P Dunscombe · S Mutic ·
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    ABSTRACT: Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties.
    Medical Physics 06/2014; 41(6):426-426. DOI:10.1118/1.4889164 · 2.64 Impact Factor
  • R Manger · A Paxton · T Pawlicki · G Kim ·
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    ABSTRACT: Purpose: To develop a process map detailing the steps in the clinical workflow for surface imaging guided stereotactic radiosurgery (SRS), conduct a failure mode and effects analysis (FMEA) based on the process map, and perform fault tree analysis (FTA) on the steps with the highest risk priority number (RPN).
    Medical Physics 06/2014; 41(6):433-433. DOI:10.1118/1.4889190 · 2.64 Impact Factor
  • A Paxton · R Manger · T Pawlicki · G Kim ·
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    ABSTRACT: Purpose: The present calibration method used for the AlignRT surface imaging system relies on the placement of a calibration plate at the linac isocenter using isocenter surrogates (crosshairs, room lasers, etc.). This work investigated the potential advantages of a new calibration method that shifts the AlignRT isocenter to be coincident with the linac MV beam isocenter.
    Medical Physics 06/2014; 41(6):208-208. DOI:10.1118/1.4888274 · 2.64 Impact Factor
  • D.A. Rahn · G.G. Kim · T. Pawlicki · A.J. Mundt ·

    International Journal of Radiation OncologyBiologyPhysics 10/2013; 87(2):S119-S120. DOI:10.1016/j.ijrobp.2013.06.307 · 4.26 Impact Factor
  • R Rice · G Kim · M Whitaker · T Pawlicki ·

    Medical Physics 06/2013; 40(6):237. DOI:10.1118/1.4814581 · 2.64 Impact Factor
  • J. Hoisak · T. Pawlicki · G. Kim · R. Fletcher · K. Moore ·
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    ABSTRACT: Purpose: A new model of service and support has been developed between our institution and a linear accelerator vendor. Previously, a user would report machine breakdown events verbally by telephone to a dispatch center, which then coordinated repair actions with the technical helpdesk and local service engineer. In the new model, events are reported electronically directly to the vendor technical helpdesk, who can then contact the user immediately to coordinate a response. The purpose of this work is to report on a new model for vendor/institution collaboration to improve clinical operations. Methods: We developed a new on‐line event recording system that connected our clinic events directly to the linear accelerator vendor. Between February 2012 and 2013, our institutional electronic quality reporting database was reviewed. A machine down event was defined as a technical problem with the linear accelerator that interrupted, prevented, or required rescheduling or cancellation of patient treatments. Machine down time, vendor support response time, and whether the event was resolved by a service engineer visit or by physicists liaising with the technical helpdesk, were recorded. Results: Over 259 clinical days, there were 76 machine down events, with 45 before introduction of the new service model and 31 after. Under the new model, the average time for service response decreased by 92%, from 128 to 10 minutes and the number of times a service engineer had to be dispatched decreased by 70%. The down time per event decreased by 47% from 135 minutes to 71 minutes. Treatment cancellations or rescheduling decreased by 54%. Conclusion: A new model of linear accelerator support and service delivery was implemented and was found to decrease vendor response time and reduce the number of on‐site visits required by service engineers. These performance gains resulted in decreased machine downtime and decreased patient treatment cancellations. The work was undertaken in cooperation with Varian Medical Systems.
    Medical Physics 06/2013; 40(6). DOI:10.1118/1.4814079 · 2.64 Impact Factor
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    ABSTRACT: Purpose: Incident learning plays a key role in improving quality and safety in a wide range of industries and medical disciplines. However, implementing an effective incident learning system is complex, especially in radiation oncology. One current barrier is the lack of technical standards to guide users or developers. This report, the product of an initiative by the Work Group on Prevention of Errors in Radiation Oncology of the American Association of Physicists in Medicine, provides technical recommendations for the content and structure of incident learning databases in radiation oncology. Methods: A panel of experts was assembled and tasked with developing consensus recommendations in five key areas: definitions, process maps, severity scales, causality taxonomy, and data elements. Experts included representatives from all major North American radiation oncology organizations as well as users and developers of public and in-house reporting systems with over two decades of collective experience. Recommendations were developed that take into account existing incident learning systems as well as the requirements of outside agencies. Results: Consensus recommendations are provided for the five major topic areas. In the process mapping task, 91 common steps were identified for external beam radiation therapy and 88 in brachytherapy. A novel feature of the process maps is the identification of "safety barriers," also known as critical control points, which are any process steps whose primary function is to prevent errors or mistakes from occurring or propagating through the radiotherapy workflow. Other recommendations include a ten-level medical severity scale designed to reflect the observed or estimated harm to a patient, a radiation oncology-specific root causes table to facilitate and regularize root-cause analyses, and recommendations for data elements and structures to aid in development of electronic databases. Also presented is a list of key functional requirements of any reporting system. Conclusions: Incident learning is recognized as an invaluable tool for improving the quality and safety of treatments. The consensus recommendations in this report are intended to facilitate the implementation of such systems within individual clinics as well as on broader national and international scales.
    Medical Physics 12/2012; 39(12):7272-90. DOI:10.1118/1.4764914 · 2.64 Impact Factor
  • G. Kim · J. Uhl · A. Sandhu · T. Pawlicki ·

    International Journal of Radiation OncologyBiologyPhysics 11/2012; 84(3):S871-S872. DOI:10.1016/j.ijrobp.2012.07.2331 · 4.26 Impact Factor
  • G. Kim · R. Rice · J. Lawson · K. Murphy · T. Pawlicki ·

    International Journal of Radiation OncologyBiologyPhysics 11/2012; 84(3):S823. DOI:10.1016/j.ijrobp.2012.07.2205 · 4.26 Impact Factor
  • R. Rice · I. Dragojevic · J. Hoisak · T. Pawlicki · A.J. Mundt ·

    International Journal of Radiation OncologyBiologyPhysics 11/2012; 84(3):S773. DOI:10.1016/j.ijrobp.2012.07.2069 · 4.26 Impact Factor
  • T. Pawlicki · T. Harry · M. Taylor · R. Fletcher · A. Mundt ·

    International Journal of Radiation OncologyBiologyPhysics 11/2012; 84(3):S543. DOI:10.1016/j.ijrobp.2012.07.1448 · 4.26 Impact Factor
  • T Harry · M Whitaker · T Pawlicki ·
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    ABSTRACT: Purpose: Mathematical models are used in Industrial & Systems Engineering to analyze complex integrated operational systems. Adapting this approach to radiation therapy can help quantify the precision and accuracy necessary to achieve optimal outcome of radiation treatment. The purpose of this work is to develop such a model using clinical data and assess the effect uncertainties have on treatment outcomes. Methods: The Taguchi Loss Function (TLF) is adapted to radiation therapy using conventional radiobiological models for tumor control probabilities (TCP) and normal tissues complication probabilities (NTCP) based on the equivalent uniform dose. The TCP and NTCP curves are combined to create a failure probability function for a given treatment plan. The composite effects of all uncertainties involved in treating a patient are modeled by a normal distribution. The standard deviation and mean of the normal distribution represent the precision and accuracy of a treatment. The failure probability function is convolved with the normal distribution to arrive at an expected failure probability. Precision was varied from 0.5% to 25% while accuracy ranged from ±5% to investigate uncertainties effects on complication-free local tumor control. 3D 4-field box plans where compared to IMRT plans for 18 prostate patients using this method. Results: The average expected failure probability at the prescription dose for the 3D 4-field box plans was 30.02% and 18.13% for the IMRT plans at zero uncertainty. At 25% uncertainty the expected failure probabilities were 76.85% and 64.36%, respectively. On average the IMRT plans failure probability was 14.84% less than the 3D 4-field box plans for all uncertainty levels. Conclusion: This study demonstrates that uncertainty in radiotherapy procedures has a quantifiable effect on treatment outcome. To further improve complication-free local tumor control we must both improve treatment technologies and improve quality to minimize the uncertainties in radiation therapy.
    Medical Physics 06/2012; 39(6):3760-3761. DOI:10.1118/1.4735315 · 2.64 Impact Factor
  • G Kim · D Cao · T Pawlicki ·
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    ABSTRACT: Intensity modulated radiotherapy (IMRT) has been used clinically for many years. Reports from the RPC indicate that up to 30% of the institutions fail to pass RPC IMRT credentialing process on the first attempt. While volumetric modulated arc therapy (VMAT) has been introduced more recently, it has quickly gained wide clinical use. In spite of the long history with IMRT and rapid adoption of VMAT, commissioning and developing a quality assurance (QA) program continues to be a challenge especially in busy departments. These points indicate that a review of commissioning and quality assurance for IMRT is still very much needed. In this session, the development of an overall IMRT/VMAT QA program, the role of team members and on-going program functions will be described including aspects of both quality and safety. General issues and specifics of IMRT/VMAT commissioning and quality assurance will be covered. While the general principles of commissioning and QA apply to any device capable of intensity-modulation, specific examples will be provided for Elekta and Varian linear accelerators. Strategies for commission and useful checklists will be discussed as well as some differences between Elekta and Varian technologies. There will also be a focus on practical advice towards the implementation and on-going QA of linac-based IMRT and VMAT. Patient- specific QA strategies along with the comparison of different QA equipment and techniques will be presented. Lastly, differences will be highlighted between IMRT and VMAT for patient-specific QA.Learning Objectives:1. Understand approaches to IMRT/VMAT commissioning and QA2. Describe most relevant issues in patient-specific QA for IMRT/VMAT3. Discuss issues with IMRT /VMAT QA equipment and techniques.
    Medical Physics 06/2012; 39(6):3863. DOI:10.1118/1.4735773 · 2.64 Impact Factor
  • T Harry · D Rahn · C Yashar · J Einck · T Pawlicki · S Jiang · L Cervino ·
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    ABSTRACT: Purpose: To evaluate the clinical implementation of a deep inspiration breath hold (DIBH) treatment for left breast radiotherapy using surface imaging and visual aid. Methods: A CT scan of the patient at DIBH is acquired and used for treatment planning. The plan and skin contour, containing isocenter and surface information are exported from the treatment planning system and imported into the surface imaging system (SIS). The skin contour constitutes the treatment reference surface or target DIBH position. A region of interest (ROI) consisting of the sternum and medial breasts is selected in the SIS. A set of video goggles allows the patient to view their breathing signal within the SIS, aiding in producing a reproducible and stable DIBH similar to simulation. Once the patient is set up at free breathing, she performs a DIBH while being monitored with the SIS. Shifts to minimize displacements from their reference DIBH surface are made. The surface image and patient setup are validated with weekly MV images. The beam is enabled when the two surfaces are within a predetermined tolerance. Results: Data for evaluation of the implementation was acquired for 4 patients throughout treatment. Average treatment time was 16.8 minutes and 14.2 minutes for setup. The average displacement from the reference surface was 0.4 mm during DIBHs. The average reduction of heart mean dose and volume receiving 50% of the prescribed dose between DIBH and FB was 38% and 89% respectively. A total of 15 patients have completed this new treatment. 2 were excluded for inability to achieve reproducible and stable DIBH.Conclusion: The workflow we have implemented has proven to be effective and efficient for clinical purposes. Surface imaging provides adequate real time information valuable to the treatment process. Visual aid has helped patients achieve DIBH with high reproducibility and stability.
    Medical Physics 06/2012; 39(6):3972. DOI:10.1118/1.4736209 · 2.64 Impact Factor

Publication Stats

902 Citations
293.31 Total Impact Points


  • 2007-2012
    • University of California, San Diego
      • Department of Radiation Oncology
      San Diego, California, United States
  • 2011
    • Spokane VA Medical Center
      Spokane, Washington, United States
  • 2000-2006
    • Stanford University
      • • Department of Radiation Oncology
      • • Department of Medicine
      Palo Alto, California, United States
  • 1999-2005
    • Stanford Medicine
      • Department of Radiation Oncology
      Stanford, California, United States
  • 1996
    • Medical University of Ohio at Toledo
      Toledo, Ohio, United States