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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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Li Z, Gan G, Guo J, Zhan W, Chen L. Accurate object localization facilitates automatic esophagus segmentation in deep learning. Radiat Oncol 2024; 19:55. [PMID: 38735947 PMCID: PMC11088757 DOI: 10.1186/s13014-024-02448-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 05/01/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.
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Affiliation(s)
- Zhibin Li
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guanghui Gan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jian Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei Zhan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Long Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
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Gonzalez Y, Visak J, Overman L, Liao CY, Yen A, Zhuang T, Cai B, Godley A, Zhang Y, Timmerman R, Iyengar P, Westover K, Parsons D, Lin MH. Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system. J Appl Clin Med Phys 2024:e14375. [PMID: 38712917 DOI: 10.1002/acm2.14375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/27/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
PURPOSE Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. METHODS We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. RESULTS Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). CONCLUSION We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.
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Affiliation(s)
- Yesenia Gonzalez
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Justin Visak
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Luke Overman
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chien-Yi Liao
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Allen Yen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tingliang Zhuang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yuanyuan Zhang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Robert Timmerman
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Puneeth Iyengar
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Kenneth Westover
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - David Parsons
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Wang YF, Price MJ, Elliston CD, Munbodh R, Spina CS, Horowitz DP, Kachnic LA. Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow. Adv Radiat Oncol 2024; 9:101399. [PMID: 38292890 PMCID: PMC10823112 DOI: 10.1016/j.adro.2023.101399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/11/2023] [Indexed: 02/01/2024] Open
Abstract
Purpose The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the "black box" adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians. Methods and Materials An interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across 5 treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment. Results The OART workflow has 4 components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4). Conclusions This study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.
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Affiliation(s)
- Yi-Fang Wang
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - Michael J. Price
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - Carl D. Elliston
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - Reshma Munbodh
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - Catherine S. Spina
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - David P. Horowitz
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
| | - Lisa A. Kachnic
- Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center
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Weykamp F, Meixner E, Arians N, Hoegen-Saßmannshausen P, Kim JY, Tawk B, Knoll M, Huber P, König L, Sander A, Mokry T, Meinzer C, Schlemmer HP, Jäkel O, Debus J, Hörner-Rieber J. Daily AI-Based Treatment Adaptation under Weekly Offline MR Guidance in Chemoradiotherapy for Cervical Cancer 1: The AIM-C1 Trial. J Clin Med 2024; 13:957. [PMID: 38398270 PMCID: PMC10889253 DOI: 10.3390/jcm13040957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/13/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: External beam radiotherapy (EBRT) and concurrent chemotherapy, followed by brachytherapy (BT), offer a standard of care for patients with locally advanced cervical carcinoma. Conventionally, large safety margins are required to compensate for organ movement, potentially increasing toxicity. Lately, daily high-quality cone beam CT (CBCT)-guided adaptive radiotherapy, aided by artificial intelligence (AI), became clinically available. Thus, online treatment plans can be adapted to the current position of the tumor and the adjacent organs at risk (OAR), while the patient is lying on the treatment couch. We sought to evaluate the potential of this new technology, including a weekly shuttle-based 3T-MRI scan in various treatment positions for tumor evaluation and for decreasing treatment-related side effects. (2) Methods: This is a prospective one-armed phase-II trial consisting of 40 patients with cervical carcinoma (FIGO IB-IIIC1) with an age ≥ 18 years and a Karnofsky performance score ≥ 70%. EBRT (45-50.4 Gy in 25-28 fractions with 55.0-58.8 Gy simultaneous integrated boosts to lymph node metastases) will be accompanied by weekly shuttle-based MRIs. Concurrent platinum-based chemotherapy will be given, followed by 28 Gy of BT (four fractions). The primary endpoint will be the occurrence of overall early bowel and bladder toxicity CTCAE grade 2 or higher (CTCAE v5.0). Secondary outcomes include clinical feasibility, quality of life, and imaging-based response assessment.
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Affiliation(s)
- Fabian Weykamp
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Eva Meixner
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Nathalie Arians
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Philipp Hoegen-Saßmannshausen
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Ji-Young Kim
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Bouchra Tawk
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Division of Molecular and Translational Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Division of Molecular and Translational Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Peter Huber
- Clinical Cooperation Unit Molecular Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Laila König
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Anja Sander
- Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany
| | - Theresa Mokry
- Department of Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Department of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Clara Meinzer
- Department of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Department of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Oliver Jäkel
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site, 69120 Heidelberg, Germany
| | - Juliane Hörner-Rieber
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany (J.H.-R.)
- Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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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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Wang E, Yen A, Hrycushko B, Wang S, Lin J, Zhong X, Dohopolski M, Nwachukwu C, Iqbal Z, Albuquerque K. The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100546. [PMID: 38369990 PMCID: PMC10869256 DOI: 10.1016/j.phro.2024.100546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
Background and Purpose Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.
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Affiliation(s)
- Ethan Wang
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Allen Yen
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Brian Hrycushko
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Siqiu Wang
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Jingyin Lin
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Xinran Zhong
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Michael Dohopolski
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Chika Nwachukwu
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Zohaib Iqbal
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Kevin Albuquerque
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
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Liu X, Yang R, Xiong T, Yang X, Li W, Song L, Zhu J, Wang M, Cai J, Geng L. CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset. Cancers (Basel) 2023; 15:5479. [PMID: 38001738 PMCID: PMC10670900 DOI: 10.3390/cancers15225479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. MATERIALS AND METHODS A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder-decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. RESULTS The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. CONCLUSIONS Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.
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Affiliation(s)
- Xi Liu
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Ruijie Yang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Tianyu Xiong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Xueying Yang
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Liming Song
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Jiarui Zhu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Mingqing Wang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Lisheng Geng
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing 102206, China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing 100191, China
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9
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Price AT, Kang KH, Reynoso FJ, Laugeman E, Abraham CD, Huang J, Hilliard J, Knutson NC, Henke LE. In silico trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy. Phys Imaging Radiat Oncol 2023; 28:100491. [PMID: 37772278 PMCID: PMC10523006 DOI: 10.1016/j.phro.2023.100491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/26/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023] Open
Abstract
Background and Purpose Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the in silico clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT. Materials and methods Ten patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study. The CBCT was the adaptive image-of-the-day to simulate first fraction on-board imaging. Initial contours defined on the MRI were rigidly matched to the CBCT. Online ART was used to create treatment plans at first fraction. Dose-volume metrics of these simulation-free plans were compared to standard-workflow HA-WBRT plans on each patient CT simulation dataset. Timing data for the adaptive planning sessions were recorded. Results For all ten patients, simulation-free HA-WBRT plans were successfully created utilizing the online ART workflow and met all constraints. The median hippocampi D100% was 7.8 Gy (6.6-8.8 Gy) in the adaptive plan vs 8.1 Gy (7.7-8.4 Gy) in the standard workflow plan. All plans required adaptation at first fraction due to both a failing hippocampal constraint (6/10 adaptive fractions) and sub-optimal target coverage (6/10 adaptive fractions). Median time for the adaptive session was 45.2 min (34.0-53.8 min). Conclusions Simulation-free HA-WBRT, with commercially available systems, was clinically feasible via plan-quality metrics and timing, in silico.
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Affiliation(s)
- Alex T. Price
- Corresponding author at: Department of Radiation Oncology, University Hospitals Seidman Cancer Center, 11100 Euclid Ave, Cleveland OH 44106, USA
| | - Kylie H. Kang
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Francisco J. Reynoso
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Eric Laugeman
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Christopher D. Abraham
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Jiayi Huang
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Jessica Hilliard
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
| | - Nels C. Knutson
- Department of Radiation Oncology, Washington University School of Medicine, 4511 Forest Park Ave, St. Louis, MO 63108, USA
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10
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Liu H, Schaal D, Curry H, Clark R, Magliari A, Kupelian P, Khuntia D, Beriwal S. Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction. Radiat Oncol 2023; 18:144. [PMID: 37660057 PMCID: PMC10475190 DOI: 10.1186/s13014-023-02340-2] [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: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 09/04/2023] Open
Abstract
Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.
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Affiliation(s)
- Hefei Liu
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, USA
- Varian Medical Systems Inc, Palo Alto, CA, USA
| | | | | | - Ryan Clark
- Varian Medical Systems Inc, Palo Alto, CA, USA
| | | | | | | | - Sushil Beriwal
- Varian Medical Systems Inc, Palo Alto, CA, USA.
- Allegheny Health Network Cancer Institute, Pittsburgh, PA, USA.
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11
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Price AT, Schiff JP, Laugeman E, Maraghechi B, Schmidt M, Zhu T, Reynoso F, Hao Y, Kim T, Morris E, Zhao X, Hugo GD, Vlacich G, DeSelm CJ, Samson PP, Baumann BC, Badiyan SN, Robinson CG, Kim H, Henke LE. Initial clinical experience building a dual CT- and MR-guided adaptive radiotherapy program. Clin Transl Radiat Oncol 2023; 42:100661. [PMID: 37529627 PMCID: PMC10388162 DOI: 10.1016/j.ctro.2023.100661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/12/2023] [Accepted: 07/20/2023] [Indexed: 08/03/2023] Open
Abstract
Introduction Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period. Materials and Methods The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform. Results From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART. Conclusions We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.
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Affiliation(s)
- Alex T. Price
- University Hospitals/Case Western Reserve University, Department of Radiation Oncology, Cleveland, OH, USA
| | - Joshua P. Schiff
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Eric Laugeman
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Borna Maraghechi
- City of Hope Orange County, Department of Radiation Oncology, Irvine, CA, USA
| | - Matthew Schmidt
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Tong Zhu
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Francisco Reynoso
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Yao Hao
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Taeho Kim
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Eric Morris
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Xiaodong Zhao
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Geoffrey D. Hugo
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Gregory Vlacich
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Carl J. DeSelm
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Pamela P. Samson
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Brian C. Baumann
- Springfield Clinic, Department of Radiation Oncology, Springfield, IL, USA
| | - Shahed N. Badiyan
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, USA
| | - Clifford G. Robinson
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Hyun Kim
- Washington University School of Medicine in St. Louis, Department of Radiation Oncology, St. Louis, MO, USA
| | - Lauren E. Henke
- University Hospitals/Case Western Reserve University, Department of Radiation Oncology, Cleveland, OH, USA
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