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A Knowledge-Based Approach to Improving and Homogenizing Intensity Modulated Radiation Therapy Planning Quality Among Treatment Centers: An Example Application to Prostate Cancer Planning

Good, David, MS ; Lo, Joseph, PhD ; Lee, W. Robert, MD ; Wu, Q. Jackie, PhD ; Yin, Fang-Fang, PhD ; Das, Shiva K., PhD

International journal of radiation oncology, biology, physics, 2013-09, Vol.87 (1), p.176-181 [Periódico revisado por pares]

United States: Elsevier Inc

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Título:
A Knowledge-Based Approach to Improving and Homogenizing Intensity Modulated Radiation Therapy Planning Quality Among Treatment Centers: An Example Application to Prostate Cancer Planning
Autor: Good, David, MS ; Lo, Joseph, PhD ; Lee, W. Robert, MD ; Wu, Q. Jackie, PhD ; Yin, Fang-Fang, PhD ; Das, Shiva K., PhD
Assuntos: BLADDER ; Cancer Care Facilities - standards ; COMPARATIVE EVALUATIONS ; Feasibility Studies ; Femur Head - radiation effects ; Hematology, Oncology and Palliative Medicine ; Hospitals, Community ; Humans ; Knowledge Bases ; Male ; NEOPLASMS ; Organs at Risk - radiation effects ; PLANNING ; PROSTATE ; Prostatic Neoplasms - radiotherapy ; QUALITY ASSURANCE ; Quality Improvement - standards ; Radiology ; RADIOLOGY AND NUCLEAR MEDICINE ; RADIOTHERAPY ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted - methods ; Radiotherapy Planning, Computer-Assisted - standards ; Radiotherapy, Conformal ; Radiotherapy, Intensity-Modulated - methods ; Radiotherapy, Intensity-Modulated - standards ; RECTUM ; Rectum - radiation effects ; Technology Transfer ; Urinary Bladder - radiation effects
É parte de: International journal of radiation oncology, biology, physics, 2013-09, Vol.87 (1), p.176-181
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: Purpose Intensity modulated radiation therapy (IMRT) treatment planning can have wide variation among different treatment centers. We propose a system to leverage the IMRT planning experience of larger institutions to automatically create high-quality plans for outside clinics. We explore feasibility by generating plans for patient datasets from an outside institution by adapting plans from our institution. Methods and Materials A knowledge database was created from 132 IMRT treatment plans for prostate cancer at our institution. The outside institution, a community hospital, provided the datasets for 55 prostate cancer cases, including their original treatment plans. For each “query” case from the outside institution, a similar “match” case was identified in the knowledge database, and the match case’s plan parameters were then adapted and optimized to the query case by use of a semiautomated approach that required no expert planning knowledge. The plans generated with this knowledge-based approach were compared with the original treatment plans at several dose cutpoints. Results Compared with the original plan, the knowledge-based plan had a significantly more homogeneous dose to the planning target volume and a significantly lower maximum dose. The volumes of the rectum, bladder, and femoral heads above all cutpoints were nominally lower for the knowledge-based plan; the reductions were significantly lower for the rectum. In 40% of cases, the knowledge-based plan had overall superior (lower) dose–volume histograms for rectum and bladder; in 54% of cases, the comparison was equivocal; in 6% of cases, the knowledge-based plan was inferior for both bladder and rectum. Conclusions Knowledge-based planning was superior or equivalent to the original plan in 95% of cases. The knowledge-based approach shows promise for homogenizing plan quality by transferring planning expertise from more experienced to less experienced institutions.
Editor: United States: Elsevier Inc
Idioma: Inglês

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A Knowledge-Based Approach to Improving and Homogenizing Intensity Modulated Radiation Therapy Planning Quality Among Treatment Centers: An Example Application to Prostate Cancer Planning

Good, David, MS ; Lo, Joseph, PhD ; Lee, W. Robert, MD ; Wu, Q. Jackie, PhD ; Yin, Fang-Fang, PhD ; Das, Shiva K., PhD

United States: Elsevier Inc

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