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Hernandez S, Burger H, Nguyen C, Paulino AC, Lucas JT, Faught AM, Duryea J, Netherton T, Rhee DJ, Cardenas C, Howell R, Fuentes D, Pollard-Larkin J, Court L, Parkes J. Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy. Front Oncol 2023; 13:1221792. [PMID: 37810961 PMCID: PMC10556471 DOI: 10.3389/fonc.2023.1221792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/06/2023] [Indexed: 10/10/2023] Open
Abstract
Purpose Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children's Research Hospital. Methods Sixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total. Results The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan. Conclusions We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy.
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Affiliation(s)
- Soleil Hernandez
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hester Burger
- Department Medical Physics, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Callistus Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Arnold C. Paulino
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - John T. Lucas
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Austin M. Faught
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Jack Duryea
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tucker Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carlos Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Rebecca Howell
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Julianne Pollard-Larkin
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence Court
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jeannette Parkes
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
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Jaffray DA, Knaul F, Baumann M, Gospodarowicz M. Harnessing progress in radiotherapy for global cancer control. NATURE CANCER 2023; 4:1228-1238. [PMID: 37749355 DOI: 10.1038/s43018-023-00619-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/22/2023] [Indexed: 09/27/2023]
Abstract
The pace of technological innovation over the past three decades has transformed the field of radiotherapy into one of the most technologically intense disciplines in medicine. However, the global barriers to access this highly effective treatment are complex and extend beyond technological limitations. Here, we review the technological advancement and current status of radiotherapy and discuss the efforts of the global radiation oncology community to formulate a more integrative 'diagonal approach' in which the agendas of science-driven advances in individual outcomes and the sociotechnological task of global cancer control can be aligned to bring the benefit of this proven therapy to patients with cancer everywhere.
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Affiliation(s)
- David A Jaffray
- Departments of Radiation Physics and Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Felicia Knaul
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | | | - Mary Gospodarowicz
- Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
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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: 3] [Impact Index Per Article: 3.0] [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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Schmidt MC, Abraham CD, Huang J, Robinson CG, Hugo G, Knutson NC, Sun B, Raranje C, Sajo E, Zygmanski P, Jandel M, Szentivanyi P, Hilliard J, Hamilton J, Reynoso FJ. Clinical application of a template-guided automated planning routine. J Appl Clin Med Phys 2023; 24:e13837. [PMID: 36347220 PMCID: PMC10018666 DOI: 10.1002/acm2.13837] [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: 03/20/2022] [Revised: 06/06/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application. METHODS A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan. RESULTS The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″. CONCLUSIONS Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.
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Affiliation(s)
- Matthew C Schmidt
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.,Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Christopher D Abraham
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jiayi Huang
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Clifford G Robinson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Geoffrey Hugo
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Nels C Knutson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Baozhou Sun
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Chipo Raranje
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Erno Sajo
- Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Piotr Zygmanski
- Brigham and Women's/Dana Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts, USA
| | - Marian Jandel
- Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | | | - Jessica Hilliard
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jessica Hamilton
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Francisco J Reynoso
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
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Hernandez S, Nguyen C, Parkes J, Burger H, Rhee DJ, Netherton T, Mumme R, Vega JGDL, Duryea J, Leone A, Paulino AC, Cardenas C, Howell R, Fuentes D, Pollard-Larkin J, Court L. Automating the treatment planning process for 3D-conformal pediatric craniospinal irradiation therapy. Pediatr Blood Cancer 2023; 70:e30164. [PMID: 36591994 DOI: 10.1002/pbc.30164] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 01/03/2023]
Abstract
PURPOSE Pediatric patients with medulloblastoma in low- and middle-income countries (LMICs) are most treated with 3D-conformal photon craniospinal irradiation (CSI), a time-consuming, complex treatment to plan, especially in resource-constrained settings. Therefore, we developed and tested a 3D-conformal CSI autoplanning tool for varying patient lengths. METHODS AND MATERIALS Autocontours were generated with a deep learning model trained:tested (80:20 ratio) on 143 pediatric medulloblastoma CT scans (patient ages: 2-19 years, median = 7 years). Using the verified autocontours, the autoplanning tool generated two lateral brain fields matched to a single spine field, an extended single spine field, or two matched spine fields. Additional spine subfields were added to optimize the corresponding dose distribution. Feathering was implemented (yielding nine to 12 fields) to give a composite plan. Each planning approach was tested on six patients (ages 3-10 years). A pediatric radiation oncologist assessed clinical acceptability of each autoplan. RESULTS The autocontoured structures' average Dice similarity coefficient ranged from .65 to .98. The average V95 for the brain/spinal canal for single, extended, and multi-field spine configurations was 99.9% ± 0.06%/99.9% ± 0.10%, 99.9% ± 0.07%/99.4% ± 0.30%, and 99.9% ± 0.06%/99.4% ± 0.40%, respectively. The average maximum dose across all field configurations to the brainstem, eyes (L/R), lenses (L/R), and spinal cord were 23.7 ± 0.08, 24.1 ± 0.28, 13.3 ± 5.27, and 25.5 ± 0.34 Gy, respectively (prescription = 23.4 Gy/13 fractions). Of the 18 plans tested, all were scored as clinically acceptable as-is or clinically acceptable with minor, time-efficient edits preferred or required. No plans were scored as clinically unacceptable. CONCLUSION The autoplanning tool successfully generated pediatric CSI plans for varying patient lengths in 3.50 ± 0.4 minutes on average, indicating potential for an efficient planning aid in a resource-constrained settings.
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Affiliation(s)
- Soleil Hernandez
- 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
| | - Callistus Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jeannette Parkes
- Department of Radiation Oncology, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Hester Burger
- Department Medical Physics, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker Netherton
- 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
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jean Gumma-De La Vega
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jack Duryea
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexandrea Leone
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Arnold C Paulino
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Rebecca Howell
- 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
| | - David Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Julianne Pollard-Larkin
- 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
| | - Laurence Court
- 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
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Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med 2022; 5:162. [PMID: 36307479 PMCID: PMC9614192 DOI: 10.1038/s41746-022-00700-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/29/2022] [Indexed: 02/08/2023] Open
Abstract
In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.
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Mehrens H, Douglas R, Gronberg M, Nealon K, Zhang J, Court L. Statistical process control to monitor use of a web-based autoplanning tool. J Appl Clin Med Phys 2022; 23:e13803. [PMID: 36300872 PMCID: PMC9797174 DOI: 10.1002/acm2.13803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). METHODS Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. RESULTS For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, -2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62% ± 0.34%, 0.32% ± 0.23%, and -0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC-TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC-TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.
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Affiliation(s)
- Hunter Mehrens
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Raphael Douglas
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Mary Gronberg
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Kelly Nealon
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Joy Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Laurence Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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8
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WATANABE HIROYUKI, SUGIMOTO SATORU, KAWABATA TORU, NAGATA HIRONORI, KUROKAWA CHIE, USUI KEISUKE, INOUE TATSUYA, TAKATSU JUN, KATO KYOICHI, SASAI KEISUKE. Semiautomatic Treatment Planning for the Field-in-field Technique in Whole Brain Irradiation. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2022; 68:375-386. [PMID: 39021429 PMCID: PMC11250019 DOI: 10.14789/jmj.jmj22-0003-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/01/2022] [Indexed: 07/20/2024]
Abstract
Objectives In radiation therapy, the field-in-field (FIF) technique is used to prevent the administration of unnecessarily high doses to reduce toxicity. Recently, the FIF technique has been used for whole brain irradiation (WBI). Using the FIF technique, the volume that receives a higher than prescribed dose (hotspot) can be largely reduced; however, the treatment planning requires time. Therefore, to reduce the burden on the treatment planners, we propose a semiautomatic treatment planning method for the FIF technique. Methods In the semiautomatic FIF technique, hotspot regions in a treatment plan without the FIF technique are identified three-dimensionally, and beams with blocks that cover the hotspot regions using a multileaf collimator (sub-beams) are automatically created. The sub-beams are added to the original plan, and weights are assigned based on the maximum dose of the original plan to decrease the doses in the hotspot regions. This method was applied to 22 patients previously treated with WBI, wherein treatment plans were originally created without the FIF technique. Results In the semiautomatic FIF plans, the hotspots almost disappeared. The dose to 95% of the volume and the volume receiving at least 95% of the prescribed dose in the planning target volume decreased by only 0.3% ± 0.2% and 0.0% ± 0.1%, respectively, on average compared with those in the original plan. The average semiautomatic FIF processing time was 28 ± 4 s. Conclusions The proposed method reduced the hotspot regions with a slight change in the target coverage.
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Affiliation(s)
- HIROYUKI WATANABE
- Corresponding author: Hiroyuki Watanabe, Department of Radiation Oncology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan, TEL: +81-3-6426-3055 FAX: +81-3-3784-8404 E-mail:
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9
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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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10
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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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11
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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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12
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Nealon KA, Court LE, Douglas RJ, Zhang L, Han EY. Development and validation of a checklist for use with automatically generated radiotherapy plans. J Appl Clin Med Phys 2022; 23:e13694. [PMID: 35775105 PMCID: PMC9512344 DOI: 10.1002/acm2.13694] [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: 03/14/2022] [Revised: 05/19/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans. Methods A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence‐generated resident treatment plans from the RPA for safety and plan quality, five of which contained errors. Physicists performed plan checks, recorded errors, and rated each plan's clinical acceptability. Following a 2‐week break, physicists reviewed 10 additional plans with a similar distribution of errors using our customized checklist. Participants then provided feedback on the usability of the checklist and it was modified accordingly. In a second study, this process was repeated with 14 senior medical physics residents who were randomly assigned to checklist or no checklist for their reviews. Each reviewed 10 plans, five of which contained errors, and completed the corresponding survey. Results In the first study, the checklist significantly improved the rate of error detection from 3.4 ± 1.1 to 4.4 ± 0.74 errors per participant without and with the checklist, respectively (p = 0.02). Error detection increased by 20% when the custom checklist was utilized. In the second study, 2.9 ± 0.84 and 3.5 ± 0.84 errors per participant were detected without and with the revised checklist, respectively (p = 0.08). Despite the lack of statistical significance for this cohort, error detection increased by 18% when the checklist was utilized. Conclusion Our results indicate that the use of a customized checklist when reviewing automated treatment plans will result in improved patient safety.
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Affiliation(s)
- Kelly A Nealon
- University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.,Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laurence E Court
- University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.,Department of Radiation Physics - Patient Care, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raphael J Douglas
- Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lifei Zhang
- Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Eun Young Han
- University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.,Department of Radiation Physics - Patient Care, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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13
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Petragallo R, Bardach N, Ramirez E, Lamb JM. Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists. J Appl Clin Med Phys 2022; 23:e13568. [PMID: 35239234 PMCID: PMC9121037 DOI: 10.1002/acm2.13568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/22/2021] [Accepted: 02/03/2022] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Little is known about the scale of clinical implementation of automated treatment planning techniques in the United States. In this work, we examine the barriers and facilitators to adoption of commercially available automated planning tools into the clinical workflow using a survey of medical dosimetrists. METHODS/MATERIALS Survey questions were developed based on a literature review of automation research and cognitive interviews of medical dosimetrists at our institution. Treatment planning automation was defined to include auto-contouring and automated treatment planning. Survey questions probed frequency of use, positive and negative perceptions, potential implementation changes, and demographic and institutional descriptive statistics. The survey sample was identified using both a LinkedIn search and referral requests sent to physics directors and senior physicists at 34 radiotherapy clinics in our state. The survey was active from August 2020 to April 2021. RESULTS Thirty-four responses were collected out of 59 surveys sent. Three categories of barriers to use of automation were identified. The first related to perceptions of limited accuracy and usability of the algorithms. Eighty-eight percent of respondents reported that auto-contouring inaccuracy limited its use, and 62% thought it was difficult to modify an automated plan, thus limiting its usefulness. The second barrier relates to the perception that automation increases the probability of an error reaching the patient. Third, respondents were concerned that automation will make their jobs less satisfying and less secure. Large majorities reported that they enjoyed plan optimization, would not want to lose that part of their job, and expressed explicit job security fears. CONCLUSION To our knowledge this is the first systematic investigation into the views of automation by medical dosimetrists. Potential barriers and facilitators to use were explicitly identified. This investigation highlights several concrete approaches that could potentially increase the translation of automation into the clinic, along with areas of needed research.
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Affiliation(s)
- Rachel Petragallo
- Department of Radiation OncologyUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Naomi Bardach
- Department of PediatricsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ezequiel Ramirez
- Department of Radiation OncologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - James M. Lamb
- Department of Radiation OncologyUniversity of CaliforniaLos AngelesCaliforniaUSA
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14
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Shin MB, Liu G, Mugo N, Garcia PJ, Rao DW, Bayer CJ, Eckert LO, Pinder LF, Wasserheit JN, Barnabas RV. A Framework for Cervical Cancer Elimination in Low-and-Middle-Income Countries: A Scoping Review and Roadmap for Interventions and Research Priorities. Front Public Health 2021; 9:670032. [PMID: 34277540 PMCID: PMC8281011 DOI: 10.3389/fpubh.2021.670032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/19/2021] [Indexed: 12/14/2022] Open
Abstract
The World Health Organization announced an ambitious call for cervical cancer elimination worldwide. With existing prevention and treatment modalities, cervical cancer elimination is now within reach for high-income countries. Despite limited financing and capacity constraints in low-and-middle-income countries (LMICs), prevention and control efforts can be supported through integrated services and new technologies. We conducted this scoping review to outline a roadmap toward cervical cancer elimination in LMICs and highlight evidence-based interventions and research priorities to accelerate cervical cancer elimination. We reviewed and synthesized literature from 2010 to 2020 on primary and secondary cervical cancer prevention strategies. In addition, we conducted expert interviews with gynecologic and infectious disease providers, researchers, and LMIC health officials. Using these data, we developed a logic model to summarize the current state of science and identified evidence gaps and priority research questions for each prevention strategy. The logic model for cervical cancer elimination maps the needs for improved collaboration between policy makers, production and supply, healthcare systems, providers, health workers, and communities. The model articulates responsibilities for stakeholders and visualizes processes to increase access to and coverage of prevention methods. We discuss the challenges of contextual factors and highlight innovation needs. Effective prevention methods include HPV vaccination, screening using visual inspection and HPV testing, and thermocoagulation. However, vaccine coverage remains low in LMICs. New strategies, including single-dose vaccination could enhance impact. Loss to follow-up and treatment delays could be addressed by improved same-day screen-and-treat technologies. We provide a practical framework to guide cervical cancer elimination in LMICs. The scoping review highlights existing and innovative strategies, unmet needs, and collaborations required to achieve elimination across implementation contexts.
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Affiliation(s)
- Michelle B. Shin
- School of Nursing, University of Washington, Seattle, WA, United States
| | - Gui Liu
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Nelly Mugo
- Department of Global Health, University of Washington, Seattle, WA, United States
- Center for Clinical Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Patricia J. Garcia
- Department of Global Health, University of Washington, Seattle, WA, United States
- School of Public Health, Cayetano Heredia University, Lima, Peru
| | - Darcy W. Rao
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Department of Global Health, University of Washington, Seattle, WA, United States
| | - Cara J. Bayer
- Department of Global Health, University of Washington, Seattle, WA, United States
| | - Linda O. Eckert
- Department of Global Health, University of Washington, Seattle, WA, United States
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States
| | - Leeya F. Pinder
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States
- Department of Obstetrics and Gynecology, University of Zambia, Lusaka, Zambia
| | - Judith N. Wasserheit
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Department of Global Health, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
| | - Ruanne V. Barnabas
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Department of Global Health, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
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15
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Netherton TJ, Rhee DJ, Cardenas CE, Chung C, Klopp AH, Peterson CB, Howell RM, Balter PA, Court LE. Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images. Med Phys 2020; 47:5592-5608. [PMID: 33459402 PMCID: PMC7756475 DOI: 10.1002/mp.14415] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 06/11/2020] [Accepted: 07/12/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE The purpose of this work was to evaluate the performance of X-Net, a multiview deep learning architecture, to automatically label vertebral levels (S2-C1) in palliative radiotherapy simulation CT scans. METHODS For each patient CT scan, our automated approach 1) segmented spinal canal using a convolutional-neural network (CNN), 2) formed sagittal and coronal intensity projection pairs, 3) labeled vertebral levels with X-Net, and 4) detected irregular intervertebral spacing using an analytic methodology. The spinal canal CNN was trained via fivefold cross validation using 1,966 simulation CT scans and evaluated on 330 CT scans. After labeling vertebral levels (S2-C1) in 897 palliative radiotherapy simulation CT scans, a volume of interest surrounding the spinal canal in each patient's CT scan was converted into sagittal and coronal intensity projection image pairs. Then, intensity projection image pairs were augmented and used to train X-Net to automatically label vertebral levels using fivefold cross validation (n = 803). Prior to testing upon the final test set (n = 94), CT scans of patients with anatomical abnormalities, surgical implants, or other atypical features from the final test set were placed in an outlier group (n = 20), whereas those without these features were placed in a normative group (n = 74). The performance of X-Net, X-Net Ensemble, and another leading vertebral labeling architecture (Btrfly Net) was evaluated on both groups using identification rate, localization error, and other metrics. The performance of our approach was also evaluated on the MICCAI 2014 test dataset (n = 60). Finally, a method to detect irregular intervertebral spacing was created based on the rate of change in spacing between predicted vertebral body locations and was also evaluated using the final test set. Receiver operating characteristic analysis was used to investigate the performance of the method to detect irregular intervertebral spacing. RESULTS The spinal canal architecture yielded centroid coordinates spanning S2-C1 with submillimeter accuracy (mean ± standard deviation, 0.399 ± 0.299 mm; n = 330 patients) and was robust in the localization of spinal canal centroid to surgical implants and widespread metastases. Cross-validation testing of X-Net for vertebral labeling revealed that the deep learning model performance (F1 score, precision, and sensitivity) improved with CT scan length. The X-Net, X-Net Ensemble, and Btrfly Net mean identification rates and localization errors were 92.4% and 2.3 mm, 94.2% and 2.2 mm, and 90.5% and 3.4 mm, respectively, in the final test set and 96.7% and 2.2 mm, 96.9% and 2.0 mm, and 94.8% and 3.3 mm, respectively, within the normative group of the final test set. The X-Net Ensemble yielded the highest percentage of patients (94%) having all vertebral bodies identified correctly in the final test set when the three most inferior and superior vertebral bodies were excluded from the CT scan. The method used to detect labeling failures had 67% sensitivity and 95% specificity when combined with the X-Net Ensemble and flagged five of six patients with atypical vertebral counts (additional thoracic (T13), additional lumbar (L6) or only four lumbar vertebrae). Mean identification rate on the MICCAI 2014 dataset using an X-Net Ensemble was increased from 86.8% to 91.3% through the use of transfer learning and obtained state-of-the-art results for various regions of the spine. CONCLUSIONS We trained X-Net, our unique convolutional neural network, to automatically label vertebral levels from S2 to C1 on palliative radiotherapy CT images and found that an ensemble of X-Net models had high vertebral body identification rate (94.2%) and small localization errors (2.2 ± 1.8 mm). In addition, our transfer learning approach achieved state-of-the-art results on a well-known benchmark dataset with high identification rate (91.3%) and low localization error (3.3 mm ± 2.7 mm). When we pre-screened radiotherapy CT images for the presence of hardware, surgical implants, or other anatomic abnormalities prior to the use of X-Net, it labeled the spine correctly in more than 97% of patients and 94% of patients when scans were not prescreened. Automatically generated labels are robust to widespread vertebral metastases and surgical implants and our method to detect labeling failures based on neighborhood intervertebral spacing can reliably identify patients with an additional lumbar or thoracic vertebral body.
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Affiliation(s)
- Tucker J. Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
- The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTX77030USA
| | - Dong Joo Rhee
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
- The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTX77030USA
| | - Carlos E. Cardenas
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Caroline Chung
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Ann H. Klopp
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Christine B. Peterson
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Rebecca M. Howell
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Peter A. Balter
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
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16
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Rhee DJ, Jhingran A, Rigaud B, Netherton T, Cardenas CE, Zhang L, Vedam S, Kry S, Brock KK, Shaw W, O’Reilly F, Parkes J, Burger H, Fakie N, Trauernicht C, Simonds H, Court LE. Automatic contouring system for cervical cancer using convolutional neural networks. Med Phys 2020; 47:5648-5658. [PMID: 32964477 PMCID: PMC7756586 DOI: 10.1002/mp.14467] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. METHODS An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. RESULTS The average DSC, mean surface distance, and Hausdorff distance of our CNN-based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. CONCLUSIONS Our CNN-based auto-contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.
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Affiliation(s)
- Dong Joo Rhee
- MD Anderson UTHealth Graduate SchoolHoustonTXUSA
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Bastien Rigaud
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Tucker Netherton
- MD Anderson UTHealth Graduate SchoolHoustonTXUSA
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Carlos E. Cardenas
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Lifei Zhang
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Sastry Vedam
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Stephen Kry
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Kristy K. Brock
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - William Shaw
- Department of Medical Physics (G68)University of the Free StateBloemfonteinSouth Africa
| | - Frederika O’Reilly
- Department of Medical Physics (G68)University of the Free StateBloemfonteinSouth Africa
| | - Jeannette Parkes
- Division of Radiation Oncology and Medical PhysicsUniversity of Cape Town and Groote Schuur HospitalCape TownSouth Africa
| | - Hester Burger
- Division of Radiation Oncology and Medical PhysicsUniversity of Cape Town and Groote Schuur HospitalCape TownSouth Africa
| | - Nazia Fakie
- Division of Radiation Oncology and Medical PhysicsUniversity of Cape Town and Groote Schuur HospitalCape TownSouth Africa
| | - Chris Trauernicht
- Division of Medical PhysicsStellenbosch UniversityTygerberg Academic HospitalCape TownSouth Africa
| | - Hannah Simonds
- Division of Radiation OncologyStellenbosch UniversityTygerberg Academic HospitalCape TownSouth Africa
| | - Laurence E. Court
- Department of Radiation PhysicsDivision of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
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17
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Kisling K, Cardenas C, Anderson BM, Zhang L, Jhingran A, Simonds H, Balter P, Howell RM, Schmeler K, Beadle BM, Court L. Automatic Verification of Beam Apertures for Cervical Cancer Radiation Therapy. Pract Radiat Oncol 2020; 10:e415-e424. [PMID: 32450365 PMCID: PMC8133770 DOI: 10.1016/j.prro.2020.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 03/16/2020] [Accepted: 05/03/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE Automated tools can help identify radiation treatment plans of unacceptable quality. To this end, we developed a quality verification technique to automatically verify the clinical acceptability of beam apertures for 4-field box treatments of patients with cervical cancer. By comparing the beam apertures to be used for treatment with a secondary set of beam apertures developed automatically, this quality verification technique can flag beam apertures that may need to be edited to be acceptable for treatment. METHODS AND MATERIALS The automated methodology for creating verification beam apertures uses a deep learning model trained on beam apertures and digitally reconstructed radiographs from 255 clinically acceptable planned treatments (as rated by physicians). These verification apertures were then compared with the treatment apertures using spatial comparison metrics to detect unacceptable treatment apertures. We tested the quality verification technique on beam apertures from 80 treatment plans. Each plan was rated by physicians, where 57 were rated clinically acceptable and 23 were rated clinically unacceptable. RESULTS Using various comparison metrics (the mean surface distance, Hausdorff distance, and Dice similarity coefficient) for the 2 sets of beam apertures, we found that treatment beam apertures rated acceptable had significantly better agreement with the verification beam apertures than those rated unacceptable (P < .01). Upon receiver operating characteristic analysis, we found the area under the curve for all metrics to be 0.89 to 0.95, which demonstrated the high sensitivity and specificity of our quality verification technique. CONCLUSIONS We found that our technique of automatically verifying the beam aperture is an effective tool for flagging potentially unacceptable beam apertures during the treatment plan review process. Accordingly, we will clinically deploy this quality verification technique as part of a fully automated treatment planning tool and automated plan quality assurance program.
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Affiliation(s)
- Kelly Kisling
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hannah Simonds
- Division of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kathleen Schmeler
- Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Beth M Beadle
- Department of Radiation Oncology - Radiation Therapy, Stanford University, Stanford, California
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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18
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Rhee DJ, Jhingran A, Kisling K, Cardenas C, Simonds H, Court L. Automated Radiation Treatment Planning for Cervical Cancer. Semin Radiat Oncol 2020; 30:340-347. [PMID: 32828389 DOI: 10.1016/j.semradonc.2020.05.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The radiation treatment-planning process includes contouring, planning, and reviewing the final plan, and each component requires substantial time and effort from multiple experts. Automation of treatment planning can save time and reduce the cost of radiation treatment, and potentially provides more consistent and better quality plans. With the recent breakthroughs in computer hardware and artificial intelligence technology, automation methods for radiation treatment planning have achieved a clinically acceptable level of performance in general. At the same time, the automation process should be developed and evaluated independently for different disease sites and treatment techniques as they are unique from each other. In this article, we will discuss the current status of automated radiation treatment planning for cervical cancer for simple and complex plans and corresponding automated quality assurance methods. Furthermore, we will introduce Radiation Planning Assistant, a web-based system designed to fully automate treatment planning for cervical cancer and other treatment sites.
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Affiliation(s)
- Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Kelly Kisling
- Department of Radiation Medicine and Applied Sciences, The University of California, San Diego, San Diego, CA
| | - Carlos Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Hannah Simonds
- Department of Radiation Oncology, Tygerberg Hospital/University of Stellenbosch, Stellenbosch, South Africa
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
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19
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Rositch AF, Loffredo C, Bourlon MT, Pearlman PC, Adebamowo C. Creative Approaches to Global Cancer Research and Control. JCO Glob Oncol 2020; 6:4-7. [PMID: 32716656 PMCID: PMC7846070 DOI: 10.1200/go.20.00237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Anne F Rositch
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Christopher Loffredo
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Maria T Bourlon
- Hemato-Oncology Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Paul C Pearlman
- National Cancer Institute Center for Global Health, Rockville, MD
| | - Clement Adebamowo
- Institute of Human Virology, Department of Epidemiology and Public Health, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD.,Institute of Human Virology, Abuja, Nigeria.,Center for Bioethics and Research, Ibadan, Nigeria
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20
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Wang C, Zhu X, Hong JC, Zheng D. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future. Technol Cancer Res Treat 2020; 18:1533033819873922. [PMID: 31495281 PMCID: PMC6732844 DOI: 10.1177/1533033819873922] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works.
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Affiliation(s)
- Chunhao Wang
- 1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Xiaofeng Zhu
- 2 Department of Radiation Oncology, Georgetown University Hospital, Rockville, MD, USA
| | - Julian C Hong
- 1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,3 Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | - Dandan Zheng
- 4 Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
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21
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 PMCID: PMC7055429 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/12/2020] [Accepted: 01/13/2020] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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22
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Kisling K, Zhang L, Shaitelman SF, Anderson D, Thebe T, Yang J, Balter PA, Howell RM, Jhingran A, Schmeler K, Simonds H, du Toit M, Trauernicht C, Burger H, Botha K, Joubert N, Beadle BM, Court L. Automated treatment planning of postmastectomy radiotherapy. Med Phys 2019; 46:3767-3775. [PMID: 31077593 PMCID: PMC6739169 DOI: 10.1002/mp.13586] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/01/2019] [Accepted: 05/05/2019] [Indexed: 11/23/2022] Open
Abstract
Purpose Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). Methods Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high‐ and low‐energy photon beams and field‐in‐field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three‐tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. Results The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. Conclusions We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments.
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Affiliation(s)
- Kelly Kisling
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Simona F Shaitelman
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - David Anderson
- Department of Radiation Oncology, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Tselane Thebe
- Department of Radiation Oncology, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Peter A Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kathleen Schmeler
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Hannah Simonds
- Division of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Monique du Toit
- Division of Medical Physics, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Christoph Trauernicht
- Division of Medical Physics, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Hester Burger
- Division of Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Kobus Botha
- Division of Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Nanette Joubert
- Division of Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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23
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Affiliation(s)
- Kristy K Brock
- Department of Imaging Physics, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center.
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24
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Kisling K, Johnson JL, Simonds H, Zhang L, Jhingran A, Beadle BM, Burger H, du Toit M, Joubert N, Makufa R, Shaw W, Trauernicht C, Balter P, Howell RM, Schmeler K, Court L. A risk assessment of automated treatment planning and recommendations for clinical deployment. Med Phys 2019; 46:2567-2574. [PMID: 31002389 PMCID: PMC6561826 DOI: 10.1002/mp.13552] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 04/04/2019] [Accepted: 04/05/2019] [Indexed: 12/20/2022] Open
Abstract
Purpose To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA. Methods We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four‐field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program. Results In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top‐ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician. Conclusions Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.
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Affiliation(s)
- Kelly Kisling
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jennifer L Johnson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hannah Simonds
- Division of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Anuja Jhingran
- Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Beth M Beadle
- Department of Radiation Oncology - Radiation Therapy, Stanford University, Stanford, CA, 94305, USA
| | - Hester Burger
- Division of Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Monique du Toit
- Division of Medical Physics, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Nanette Joubert
- Division of Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, 8000, South Africa
| | - Remigio Makufa
- Department of Medical Physics, Gaborone Private Hospital, Gaborone, Botswana
| | - William Shaw
- Department of Medical Physics (G68), University of the Free State, Bloemfontein, 9301, South Africa
| | - Christoph Trauernicht
- Division of Medical Physics, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kathleen Schmeler
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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