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Douglas R, Olanrewaju A, Mumme R, Zhang L, Beadle BM, Court LE. Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning. J Appl Clin Med Phys 2024:e14338. [PMID: 38610118 DOI: 10.1002/acm2.14338] [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: 05/24/2023] [Revised: 03/05/2024] [Accepted: 03/15/2024] [Indexed: 04/14/2024] Open
Abstract
PURPOSE Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool. METHODS For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria. RESULTS For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively. CONCLUSIONS This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.
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Affiliation(s)
- Raphael Douglas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Adenike Olanrewaju
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California, USA
| | - Laurence Edward Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Aggarwal A, Court LE, Hoskin P, Jacques I, Kroiss M, Laskar S, Lievens Y, Mallick I, Abdul Malik R, Miles E, Mohamad I, Murphy C, Nankivell M, Parkes J, Parmar M, Roach C, Simonds H, Torode J, Vanderstraeten B, Langley R. ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol. BMJ Open 2023; 13:e077253. [PMID: 38149419 PMCID: PMC10711912 DOI: 10.1136/bmjopen-2023-077253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/17/2023] [Indexed: 12/28/2023] Open
Abstract
INTRODUCTION Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy. METHODS ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs. ETHICS AND DISSEMINATION The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.
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Affiliation(s)
- Ajay Aggarwal
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Peter Hoskin
- Department of Oncology, The Christie NHS Foundation Trust, Manchester, UK
- National Radiotherapy Trials Quality Assurance Group, Mount Vernon Hospital, Northwood, UK
| | - Isabella Jacques
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
| | - Mariana Kroiss
- National Radiotherapy Trials Quality Assurance Group, Mount Vernon Hospital, Northwood, UK
| | - Sarbani Laskar
- Department of Radiation Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | | | - Indranil Mallick
- Department of Radiation Oncology, Tata Memorial Center, Kolkata, West Bengal, India
| | | | - Elizabeth Miles
- National Radiotherapy Trials Quality Assurance Group, Mount Vernon Hospital, Northwood, UK
| | | | - Claire Murphy
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
| | - Matthew Nankivell
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
| | | | - Mahesh Parmar
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
| | - Carol Roach
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
| | - Hannah Simonds
- Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | | | | | - Ruth Langley
- Institute of Clinical Trials and Methodology - MRC CTU at UCL, University College London, London, UK
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Gay SS, Kisling KD, Anderson BM, Zhang L, Rhee DJ, Nguyen C, Netherton T, Yang J, Brock K, Jhingran A, Simonds H, Klopp A, Beadle BM, Court LE, Cardenas CE. Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy. J Appl Clin Med Phys 2023; 24:e14131. [PMID: 37670488 PMCID: PMC10691634 DOI: 10.1002/acm2.14131] [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: 04/16/2023] [Revised: 07/08/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
PURPOSE Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.
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Affiliation(s)
- Skylar S. Gay
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | | | | | - Lifei Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Dong Joo Rhee
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Callistus Nguyen
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tucker Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jinzhong Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Kristy Brock
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Hannah Simonds
- University Hospitals Plymouth NHS TrustPlymouthUnited Kingdom
| | - Ann Klopp
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Beth M. Beadle
- Department of Radiation OncologyStanford UniversityPalo AltoCaliforniaUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
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Court L, Aggarwal A, Burger H, Cardenas C, Chung C, Douglas R, du Toit M, Jaffray D, Jhingran A, Mejia M, Mumme R, Muya S, Naidoo K, Ndumbalo J, Nealon K, Netherton T, Nguyen C, Olanrewaju N, Parkes J, Shaw W, Trauernicht C, Xu M, Yang J, Zhang L, Simonds H, Beadle BM. Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant. JCO Glob Oncol 2023; 9:e2200431. [PMID: 37471671 PMCID: PMC10581646 DOI: 10.1200/go.22.00431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/08/2023] [Accepted: 04/24/2023] [Indexed: 07/22/2023] Open
Abstract
PURPOSE Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.
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Affiliation(s)
- Laurence Court
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ajay Aggarwal
- Guy's and St Thomas' Hospital, London, United Kingdom
| | - Hester Burger
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | | | - Christine Chung
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Raphael Douglas
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Monique du Toit
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - David Jaffray
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anuja Jhingran
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Mejia
- Benavides Cancer Institute, University of Santo Tomas, Manila, Philippines
| | - Raymond Mumme
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Komeela Naidoo
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | | | - Kelly Nealon
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Niki Olanrewaju
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jeannette Parkes
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Willie Shaw
- University of the Free State, Bloemfontein, South Africa
| | | | - Melody Xu
- University of California San Francisco, San Francisco, CA
| | - Jinzhong Yang
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lifei Zhang
- The University of Texas MD Anderson Cancer Center, Houston, TX
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Xiao Y, Cardenas C, Rhee DJ, Netherton T, Zhang L, Nguyen C, Douglas R, Mumme R, Skett S, Patel T, Trauernicht C, Chung C, Simonds H, Aggarwal A, Court L. Customizable landmark-based field aperture design for automated whole-brain radiotherapy treatment planning. J Appl Clin Med Phys 2023; 24:e13839. [PMID: 36412092 PMCID: PMC10018662 DOI: 10.1002/acm2.13839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design. METHODS The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals. RESULTS The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans. CONCLUSION This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.
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Affiliation(s)
- Yao Xiao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos Cardenas
- Department of Radiation Oncology, The University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Callistus Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raphael Douglas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen Skett
- Department of Medical Physics, Guy's & St Thomas NHS Foundation Trust, London, UK
| | - Tina Patel
- Department of Medical Physics, Guy's & St Thomas NHS Foundation Trust, London, UK
| | - Chris Trauernicht
- Division of Medical Physics, Stellenbosch University and Tygerberg Academic Hospital, Stellenbosch, South Africa
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hannah Simonds
- Division of Radiation Oncology, Stellenbosch University and Tygerberg Academic Hospital, Stellenbosch, South Africa
| | - Ajay Aggarwal
- Department of Medical Physics, Guy's & St Thomas NHS Foundation Trust, London, UK
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Kump PM, Xia J, Yaddanapudi S, Bai E. A Deep-Learning Error Detection System in Radiation Therapy. ANNALS OF BIOMEDICAL RESEARCH 2023; 5:126. [PMID: 38179070 PMCID: PMC10766422 DOI: 10.61545/abr-5-126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Delivering radiation therapy based on erroneous or corrupted treatment plan data has previously and unfortunately resulted in severe, sometimes grave patient harm. Aiming to prevent such harm and improve safety in radiation therapy treatment, this work introduces a novel, yet intuitive algorithm for strategically structuring the complex and unstructured data typical of modern treatment plans so their treatment sites may automatically be verified with deep-learning architectures. The proposed algorithm utilizes geometric and dose plan parameters to represent each plan's data as a heat map to feed a deep-learning classifier that will predict the plan's treatment site. Once it is returned by the classifier, a plan's predicted site can be compared to its documented intended site, and a warning raised should the two differ. Using real head-neck, breast, and prostate treatment plan data retrieved at two hospitals in the United States, the algorithm is evaluated by observing the accuracy of convolutional neural networks (ConvNets) in correctly classifying the structured heat map data. Many well-known ConvNet architectures are tested, and ResNet-18 performs the best with a testing accuracy of 97.8% and 0.979 F-1 score. Clearly, the heat maps generated by the proposed algorithm, despite using only a few of the many available plan parameters, retain enough information for correct treatment site classification. The simple construction and ease of interpretation make the heat maps an attractive choice for classification and error detection.
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Affiliation(s)
- PM Kump
- Department of Electrical and Computer Engineering, College of Engineering, Kansas State University, Manhattan, KS, USA
| | - J Xia
- Department of Radiation Oncology, Mount Sinai Hospital, New York City, NY, USA
| | - S Yaddanapudi
- Department of Radiation Oncology, College of Medicine, University of Iowa, Iowa City, IA, USA
| | - E Bai
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA, USA
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Kump PM, Xia J, Yaddanapudi S, Bai E. An automated treatment plan alert system to safeguard cancer treatments in radiation therapy. MACHINE LEARNING WITH APPLICATIONS 2022; 10:100437. [PMID: 36643849 PMCID: PMC9835963 DOI: 10.1016/j.mlwa.2022.100437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In radiation oncology, the intricate process of delivering radiation to a patient is detailed by the patient's treatment plan, which is data describing the geometry, construction and strength of the radiation machine and the radiation beam it emits. The patient's life depends upon the accuracy of the treatment plan, which is left in the hands of the vendor-specific software automatically generating the plan after an initial patient consultation and planning with a medical professional. However, corrupted and erroneous treatment plan data have previously resulted in severe patient harm when errors go undetected and radiation proceeds. The aim of this paper is to develop an automatic error-checking system to prevent the accidental delivery of radiation treatment to an area of the human body (i.e., the treatment site) that differs from the plan's documented intended site. To this end, we develop a method for structuring treatment plan data in order to feed machine-learning (ML) classifiers and predict a plan's treatment site. In practice, a warning may be raised if the prediction disagrees with the documented intended site. The contribution of this paper is in the strategic structuring of the complex, intricate, and nonuniform data of modern treatment planning and from multiple vendors in order to easily train ML algorithms. A three-step process utilizing up- and down-sampling and dimension reduction, the method we develop in this paper reduces the thousands of parameters comprising a single treatment plan to a single two-dimensional heat map that is independent of the specific vendor or construction of the machine used for treatment. Our heat-map structure lends itself well to feed well-established ML algorithms, and we train-test random forest, softmax, k-nearest neighbors, shallow neural network, and support vector machine using real clinical treatment plans from several hospitals in the United States. The paper demonstrates that the proposed method characterizes treatment sites so well that ML classifiers may predict head-neck, breast, and prostate treatment sites with an accuracy of about 94%. The proposed method is the first step towards a thorough, fully automated error-checking system in radiation therapy.
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Affiliation(s)
- Paul M. Kump
- Department of Electrical Engineering, SUNY Maritime College, 6 Pennyfield Ave., Bronx, NY 10465, USA,Corresponding author. (P.M. Kump)
| | - Junyi Xia
- Department of Radiation Oncology, Mount Sinai Hospital, New York City, NY, USA
| | | | - Erwei Bai
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
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Rhee DJ, Jhingran A, Huang K, Netherton TJ, Fakie N, White I, Sherriff A, Cardenas CE, Zhang L, Prajapati S, Kry SF, Beadle BM, Shaw W, O'Reilly F, Parkes J, Burger H, Trauernicht C, Simonds H, Court LE. Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques. Med Phys 2022; 49:5742-5751. [PMID: 35866442 PMCID: PMC9474595 DOI: 10.1002/mp.15868] [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: 11/05/2021] [Revised: 06/16/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose To fully automate CT‐based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. Methods We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4‐field‐box (4‐field‐box), 3D conformal radiotherapy (3D‐CRT), and volumetric modulated arc therapy (VMAT). These auto‐planning algorithms were combined with a previously developed auto‐contouring system. To improve the quality of the 4‐field‐box and 3D‐CRT plans, we used an in‐house, field‐in‐field (FIF) automation program. Thirty‐five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5‐point Likert scale. Results Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4‐field‐box, 3D‐CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. Conclusion Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource‐constrained radiotherapy departments in low‐ and middle‐income countries.
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Affiliation(s)
- Dong Joo Rhee
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, USA.,Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kai Huang
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, USA.,Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker J Netherton
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nazia Fakie
- Division of Radiation Oncology and Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Ingrid White
- Radiotherapy Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Alicia Sherriff
- Department of Oncology, University of the Free State, Bloemfontein, South Africa
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Lifei Zhang
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Surendra Prajapati
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen F Kry
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - William Shaw
- Department of Medical Physics (G68), University of the Free State, Bloemfontein, South Africa
| | - Frederika O'Reilly
- Department of Medical Physics (G68), University of the Free State, Bloemfontein, South Africa
| | - Jeannette Parkes
- Division of Radiation Oncology and Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Hester Burger
- Division of Radiation Oncology and Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Chris Trauernicht
- Division of Medical Physics, Stellenbosch University, Tygerberg Academic Hospital, Cape Town, South Africa
| | - Hannah Simonds
- Division of Radiation Oncology, Stellenbosch University, Tygerberg Academic Hospital, Cape Town, South Africa
| | - Laurence E Court
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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9
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Huang K, Das P, Olanrewaju AM, Cardenas C, Fuentes D, Zhang L, Hancock D, Simonds H, Rhee DJ, Beddar S, Briere TM, Court L. Automation of radiation treatment planning for rectal cancer. J Appl Clin Med Phys 2022; 23:e13712. [PMID: 35808871 PMCID: PMC9512348 DOI: 10.1002/acm2.13712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 11/22/2022] Open
Abstract
Purpose To develop an automated workflow for rectal cancer three‐dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward‐planning algorithms. Methods We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field‐in‐field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior–anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5‐point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end‐to‐end workflow was tested and scored by a physician on another 39 patients. Results The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto‐plan was clinically acceptable for all patients. Wedged and non‐wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end‐to‐end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients. Conclusion We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.
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Affiliation(s)
- Kai Huang
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Prajnan Das
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Adenike M Olanrewaju
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donald Hancock
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hannah Simonds
- Department of Radiation Oncology, Tygerberg Hospital Stellenbosch University, Stellenbosch, South Africa
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sam Beddar
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tina M Briere
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Douglas RJ, Olanrewaju A, Zhang L, Beadle BM, Court LE. Assessing the practicality of using a single knowledge‐based planning model for multiple linac vendors. J Appl Clin Med Phys 2022; 23:e13704. [PMID: 35791594 PMCID: PMC9359004 DOI: 10.1002/acm2.13704] [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: 08/24/2021] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 12/03/2022] Open
Abstract
Purpose Knowledge‐based planning (KBP) has been shown to be an effective tool in quality control for intensity‐modulated radiation therapy treatment planning and generating high‐quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. Materials and methods This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric‐modulated arc therapy plans for a cohort of 50 patients treated for head‐neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically‐acceptable baseline plans by a margin of 1.8 Gy or 3% dose‐volume. The quality of these plans were also compared through the use of common clinical dose‐volume histogram criteria. Results The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. Conclusions These results support the use of a head‐neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine‐tuning for targets may be necessary.
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Affiliation(s)
- Raphael J. Douglas
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Adenike Olanrewaju
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Lifei Zhang
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Beth M. Beadle
- Department of Radiation Oncology Stanford University Palo Alto California USA
| | - Laurence E. Court
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
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Nealon KA, Balter PA, Douglas RJ, Fullen DK, Nitsch PL, Olanrewaju AM, Soliman M, Court LE. Using Failure Mode and Effects Analysis to Evaluate Risk in the Clinical Adoption of Automated Contouring and Treatment Planning Tools. Pract Radiat Oncol 2022; 12:e344-e353. [DOI: 10.1016/j.prro.2022.01.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/09/2021] [Accepted: 01/07/2022] [Indexed: 12/13/2022]
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12
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Olanrewaju A, Court LE, Zhang L, Naidoo K, Burger H, Dalvie S, Wetter J, Parkes J, Trauernicht CJ, McCarroll RE, Cardenas C, Peterson CB, Benson KRK, du Toit M, van Reenen R, Beadle BM. Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant. Pract Radiat Oncol 2021; 11:177-184. [PMID: 33640315 DOI: 10.1016/j.prro.2020.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. METHODS AND MATERIALS The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion. RESULTS Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable. CONCLUSIONS Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.
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Affiliation(s)
- Adenike Olanrewaju
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lifei Zhang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Komeela Naidoo
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Hester Burger
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Sameera Dalvie
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Julie Wetter
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Jeannette Parkes
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Christoph J Trauernicht
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Rachel E McCarroll
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine B Peterson
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kathryn R K Benson
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Monique du Toit
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Ricus van Reenen
- Department of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California.
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