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Wong LM, Byrne M, van Dieren E, Zwart L, Ray X, Harms J, Aland T, Stanley D, Pawlicki T. Safety and Efficiency Analysis of Operational Decision-Making During Cone Beam Computed Tomography-Based Online Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys 2024; 119:1307-1316. [PMID: 38364949 DOI: 10.1016/j.ijrobp.2024.01.223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/06/2024] [Accepted: 01/28/2024] [Indexed: 02/18/2024]
Abstract
PURPOSE Cone beam computed tomography (CBCT)-based online adaptive radiation therapy (ART) is especially beneficial for patients with large interfractional anatomic changes. However, treatment planning and review decisions need to be made at the treatment console in real-time and may be delegated to clinical staff whose conventional scope of practice does not include making such decisions. Therefore, implementation can create new safety risks and inefficiencies. The objective of this work is to systematically analyze the safety and efficiency implications of human decision-making during the treatment session for CBCT-based online ART. METHODS AND MATERIALS The analysis was performed by applying the Systems-Theoretical Process Analysis technique and its extension for human decision-making. Four centers of different CBCT-based online ART practice models comprised the analysis team. RESULTS The general radiation therapy control structure was refined to model the interactions between routine treatment delivery staff and in-person or remote support staff. The treatment delivery staff perform 6 key control actions. Eighteen undesirable states of those control actions were identified as affecting safety and/or efficiency. In turn, 97 hazardous clinical scenarios were identified, with the control action "prepare and position patient" having the least number of scenarios and "delineate/edit influencer and target structures" having the most. Five of these are specific to either in-person or remote support during the treatment session, and 12 arise from staff support in general. CONCLUSIONS An optimally safe and efficient online ART program should require little to no support staff at the treatment console to reduce staff coordination. Uptraining of the staff already at the treatment console is needed to achieve this goal. Beyond the essential knowledge and skills such as contour editing and the selection of an optimal plan, uptraining should also target the specific cognitive biases identified in this work and the cognitive strategies to overcome these biases. Additionally, technological and organizational changes are necessary.
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Affiliation(s)
- Lawrence M Wong
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego, La Jolla, California.
| | - Mikel Byrne
- Icon Cancer Centre, South Brisbane, Queensland, Australia
| | - Erik van Dieren
- Department of Radiotherapy, Medical Spectrum Twente, Enschede, Netherlands
| | - Lisanne Zwart
- Department of Radiotherapy, Medical Spectrum Twente, Enschede, Netherlands
| | - Xenia Ray
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego, La Jolla, California
| | - Joseph Harms
- Department of Radiation Oncology/ Medical Physics, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Trent Aland
- Icon Cancer Centre, South Brisbane, Queensland, Australia
| | - Dennis Stanley
- Department of Radiation Oncology/ Medical Physics, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Todd Pawlicki
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego, La Jolla, California
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Pogue JA, Harms J, Cardenas CE, Ray X, Viscariello N, Popple RA, Stanley DN, Boggs DH. Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates. Phys Med Biol 2024; 69:115050. [PMID: 38729212 DOI: 10.1088/1361-6560/ad4a1c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/10/2024] [Indexed: 05/12/2024]
Abstract
Objective.Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources.Approach.Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates.Main results.Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively.Significance.This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.
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Affiliation(s)
- Joel A Pogue
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Joseph Harms
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Carlos E Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Xenia Ray
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, CA, United States of America
| | - Natalie Viscariello
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Richard A Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Dennis N Stanley
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - D Hunter Boggs
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America
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Zhuang T, Parsons D, Desai N, Gibbard G, Keilty D, Lin MH, Cai B, Nguyen D, Chiu T, Godley A, Pompos A, Jiang S. Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer. Biomed Phys Eng Express 2024; 10:025019. [PMID: 38241733 DOI: 10.1088/2057-1976/ad20aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.
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Affiliation(s)
- Tingliang Zhuang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - David Parsons
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Grant Gibbard
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dana Keilty
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Tsuicheng Chiu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Arnold Pompos
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
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