Purpose: To assess whether an education program on CT and MRI prostate anatomy would reduce inter- and intraobserver prostate contouring variation among experienced radiation oncologists.
Methods And Materials: Three patient CT and MRI datasets were selected. Five radiation oncologists contoured the prostate for each patient on CT first, then MRI, and again between 2 and 4 weeks later. Three education sessions were then conducted. The same contouring process was then repeated with the same datasets and oncologists. The observer variation was assessed according to changes in the ratio of the encompassing volume to intersecting volume (volume ratio [VR]), across sets of target volumes.
Results: For interobserver variation, there was a 15% reduction in mean VR with CT, from 2.74 to 2.33, and a 40% reduction in mean VR with MRI, from 2.38 to 1.41 after education. A similar trend was found for intraobserver variation, with a mean VR reduction for CT and MRI of 9% (from 1.51 to 1.38) and 16% (from 1.37 to 1.15), respectively.
Conclusion: A well-structured education program has reduced both inter- and intraobserver prostate contouring variations. The impact was greater on MRI than on CT. With the ongoing incorporation of new technologies into routine practice, education programs for target contouring should be incorporated as part of the continuing medical education of radiation oncologists.
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http://dx.doi.org/10.1016/j.ijrobp.2011.02.050 | DOI Listing |
Prostate
December 2024
Senior Department of Urology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
Background: Targeted and systematic transperineal biopsy of lesions guided by magnetic resonance imaging (MRI) and transrectal ultrasonography (TRUS) fusion technique may optimize the biopsy procedure and enhance the detection of prostate cancer. We described the transperineal biopsy guided by an automatic MRI-TRUS fusion technique, and evaluated the accuracy and feasibility of this method in a prospective single-center study.
Methods: The proposed method focuses on automating the delineation of prostate contours in both the MRI and TRUS images, the registration and fusion of MRI and TRUS images, the generation and visualiztion of the systematic biopsy cores in their corresponding locations within the 2D and the 3D views, as well as the computation and visualiztion of needle trajectories from preoperative planning to intraoperative navigation.
Br J Radiol
December 2024
Department of Oncology, Nottingham University Hospitals NHS Trust, City Hospital, Hucknall Road, Nottingham, NG5 1PB.
Objectives: To audit prospectively the accuracy, time saving and utility of a commercial artificial intelligence auto-contouring tool (AIAC). To assess the reallocation of time released by AIAC.
Methods: We audited the perceived usefulness (PU), clinical acceptability and reallocation of time during the introduction of a commercial AIAC.
ArXiv
November 2024
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston 02114.
Purpose: Deformable image registration (DIR) plays a critical role in adaptive radiation therapy (ART) to accommodate anatomical changes. However, conventional intensity-based DIR methods face challenges when registering images with unequal image intensities. In these cases, DIR accuracy can be improved using a hybrid image similarity metric which matches both image intensities and the location of known structures.
View Article and Find Full Text PDFRadiother Oncol
December 2024
Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands.
Phys Imaging Radiat Oncol
October 2024
Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China.
Adaptive carbon ion radiotherapy for localized prostate cancer requires accurate evaluation of biological dose and dose-averaged linear energy transfer (LET) changes. This study developed a deep learning model to rapidly predict the modified micro-dosimetric kinetic model (mMKM)-based dose and LET distributions. Using data from fifty patients for training and testing, the model achieved gamma passing rates exceeding 96% compared to true mMKM-based dose and LET recalculated from local effect model I (LEM I) plans.
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