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Automated Patient Safety Management and Quality Control in Radiation Therapy
Automated Patient Safety Management and Quality Control in Radiation Therapy
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151319
- ISBN
- 9798382331805
- DDC
- 574.191
- 서명/저자
- Automated Patient Safety Management and Quality Control in Radiation Therapy
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 128 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Lamb, James Michael.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약The research aims presented in this dissertation are centered on broad themes of improving automation and error detection in radiation oncology. The first aim was to create a stereoscopic radiographic generator for the ExacTrac image-guidance system. Our methodology enables medical physicists to compute geometric parameters for ray tracing based on values contained in ExacTrac configuration logs. Rigid registrations to ground-truth radiographs were performed using medical imaging software, and the results demonstrated sub-millimeter accuracy.The second aim was to create deep-learning models to automatically detect off-by-one vertebral body misalignments with on-board planar imaging. Thoracic and abdominal radiotherapy plans were retrieved from our clinical servers using the DICOM networking protocol. Pairs of digital and treatment radiographs were organized according to beam energy and orientation. Realistic off-by-one misalignments were systematically produced. Convolutional neural networks were trained to classify whether such radiographic pairs are aligned or misaligned. Given a desired 95% model specificity, the orthogonal kilovoltage model achieved a sensitivity of 99%. The models established an independent review process for setup error incidents over large retrospective datasets, which are nearly impossible to review by hand. Of particular emphasis, an instance of a previously unnoticed off-by-one setup error at our institution was found.The third aim was to create automated algorithms to evaluate the quality of prostate radiotherapy treatment plans. Quality metrics included number of days to plan approval, target margins, presence of fiducial markers, and prescribed radiation dose. The automated measurements were compared with values determined manually in clinical software, and high accuracy was obtained. Furthermore, deep-learning models for auto-contouring prostate bed target volumes were created. Refined models were developed using a novel data-driven approach to separate contours based on anterior and posterior convexity. Quality outliers were flagged for retrospective human review. Among the cases reviewed, a previously unnoticed mistake was identified, where a prostate patient treated at our institution was overexposed by 2 Gy. The automated algorithms were applied on treatment plans from our institution and from hospitals in the greater community, which allowed us to assess the existing range of standards of care in clinical practice.
- 일반주제명
- Biophysics
- 일반주제명
- Medical imaging
- 일반주제명
- Oncology
- 기타저자
- University of California, Los Angeles Physics and Biology in Medicine 009Y
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382331805
■035 ▼a(MiAaPQ)AAI31239128
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574.191
■1001 ▼aCharters, John Austin.
■24510▼aAutomated Patient Safety Management and Quality Control in Radiation Therapy
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a128 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Lamb, James Michael.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aThe research aims presented in this dissertation are centered on broad themes of improving automation and error detection in radiation oncology. The first aim was to create a stereoscopic radiographic generator for the ExacTrac image-guidance system. Our methodology enables medical physicists to compute geometric parameters for ray tracing based on values contained in ExacTrac configuration logs. Rigid registrations to ground-truth radiographs were performed using medical imaging software, and the results demonstrated sub-millimeter accuracy.The second aim was to create deep-learning models to automatically detect off-by-one vertebral body misalignments with on-board planar imaging. Thoracic and abdominal radiotherapy plans were retrieved from our clinical servers using the DICOM networking protocol. Pairs of digital and treatment radiographs were organized according to beam energy and orientation. Realistic off-by-one misalignments were systematically produced. Convolutional neural networks were trained to classify whether such radiographic pairs are aligned or misaligned. Given a desired 95% model specificity, the orthogonal kilovoltage model achieved a sensitivity of 99%. The models established an independent review process for setup error incidents over large retrospective datasets, which are nearly impossible to review by hand. Of particular emphasis, an instance of a previously unnoticed off-by-one setup error at our institution was found.The third aim was to create automated algorithms to evaluate the quality of prostate radiotherapy treatment plans. Quality metrics included number of days to plan approval, target margins, presence of fiducial markers, and prescribed radiation dose. The automated measurements were compared with values determined manually in clinical software, and high accuracy was obtained. Furthermore, deep-learning models for auto-contouring prostate bed target volumes were created. Refined models were developed using a novel data-driven approach to separate contours based on anterior and posterior convexity. Quality outliers were flagged for retrospective human review. Among the cases reviewed, a previously unnoticed mistake was identified, where a prostate patient treated at our institution was overexposed by 2 Gy. The automated algorithms were applied on treatment plans from our institution and from hospitals in the greater community, which allowed us to assess the existing range of standards of care in clinical practice.
■590 ▼aSchool code: 0031.
■650 4▼aBiophysics
■650 4▼aMedical imaging
■650 4▼aOncology
■653 ▼aAutomated error detection
■653 ▼aAutomated quality control
■653 ▼aImage-guided radiation therapy
■653 ▼aOff-by-one vertebral body misalignments
■653 ▼aProstate cancer treatment
■653 ▼aRadiation oncology
■690 ▼a0786
■690 ▼a0574
■690 ▼a0800
■690 ▼a0992
■71020▼aUniversity of California, Los Angeles▼bPhysics and Biology in Medicine 009Y.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161171▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
