ARCHERY : a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer : study protocol

IntroductionFifty 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 expa...

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Hauptverfasser: Aggarwal, Ajay, Court, Laurence Edward, Hoskin, Peter, Jacques, Isabella, Kroiss, Mariana, Laskar, Sarbani, Lievens, Yolande, Mallick, Indranil, Malik, Rozita Abdul, Miles, Elizabeth, Mohamad, Issa, Murphy, Claire, Nankivell, Matthew, Parkes, Jeannette, Parmar, Mahesh, Roach, Carol, Simonds, Hannah, Torode, Julie, Vanderstraeten, Barbara, Langley, Ruth
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creator Aggarwal, Ajay
Court, Laurence Edward
Hoskin, Peter
Jacques, Isabella
Kroiss, Mariana
Laskar, Sarbani
Lievens, Yolande
Mallick, Indranil
Malik, Rozita Abdul
Miles, Elizabeth
Mohamad, Issa
Murphy, Claire
Nankivell, Matthew
Parkes, Jeannette
Parmar, Mahesh
Roach, Carol
Simonds, Hannah
Torode, Julie
Vanderstraeten, Barbara
Langley, Ruth
description IntroductionFifty 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.MethodsARCHERY 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 disseminationThe 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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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.MethodsARCHERY 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 disseminationThe 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. 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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.MethodsARCHERY 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 disseminationThe 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.</description><subject>Adult oncology</subject><subject>COUNTRIES</subject><subject>DAHANCA</subject><subject>EORTC</subject><subject>HKNPCSG</subject><subject>Medicine and Health Sciences</subject><subject>NCIC CTG</subject><subject>NCRI</subject><subject>ONCOLOGY</subject><subject>RADIATION-THERAPY</subject><subject>RADIOTHERAPY</subject><subject>RISK</subject><subject>TARGET VOLUME DELINEATION</subject><subject>TIME</subject><issn>2044-6055</issn><issn>2044-6055</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ADGLB</sourceid><recordid>eNqtj9FKw0AQRYMoWLT_MB9gII3ZoL7VGlkQBGNB-xQmm0myuu6G3WmhH-U_ulUf_ADnZS534Nw7R8ksz4oiLTMhjv_o02QewlsWpxDXQuSz5HNZr2RVb-AGECbvwkSK9Y7AtYH8Dlk7iwYCb7s9uB7Qs-610tHTlskYPZBVlLYYqAOPnXY8ksdpD-wJ-YMsw2TQWm0H6J0HFbFaobmAkbADtB1YUu_f4lCAkQkURqiPpX6Co89OOXOenPRoAs1_91lS3VfrlUyHMeY0RreeFHLjUDfo1RgfabbD4dRSky1kXa7r1yf5IK6yF_m4eV7mi7tS3l7-F-cLlR57bw</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Aggarwal, Ajay</creator><creator>Court, Laurence Edward</creator><creator>Hoskin, Peter</creator><creator>Jacques, Isabella</creator><creator>Kroiss, Mariana</creator><creator>Laskar, Sarbani</creator><creator>Lievens, Yolande</creator><creator>Mallick, Indranil</creator><creator>Malik, Rozita Abdul</creator><creator>Miles, Elizabeth</creator><creator>Mohamad, Issa</creator><creator>Murphy, Claire</creator><creator>Nankivell, Matthew</creator><creator>Parkes, Jeannette</creator><creator>Parmar, Mahesh</creator><creator>Roach, Carol</creator><creator>Simonds, Hannah</creator><creator>Torode, Julie</creator><creator>Vanderstraeten, Barbara</creator><creator>Langley, Ruth</creator><general>BMJ Publishing Group</general><scope>ADGLB</scope></search><sort><creationdate>2023</creationdate><title>ARCHERY : a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer : study protocol</title><author>Aggarwal, Ajay ; Court, Laurence Edward ; Hoskin, Peter ; Jacques, Isabella ; Kroiss, Mariana ; Laskar, Sarbani ; Lievens, Yolande ; Mallick, Indranil ; Malik, Rozita Abdul ; Miles, Elizabeth ; Mohamad, Issa ; Murphy, Claire ; Nankivell, Matthew ; Parkes, Jeannette ; Parmar, Mahesh ; Roach, Carol ; Simonds, Hannah ; Torode, Julie ; Vanderstraeten, Barbara ; Langley, Ruth</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ghent_librecat_oai_archive_ugent_be_01HR6TRXQHK580WHNYSA21D6HB3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adult oncology</topic><topic>COUNTRIES</topic><topic>DAHANCA</topic><topic>EORTC</topic><topic>HKNPCSG</topic><topic>Medicine and Health Sciences</topic><topic>NCIC CTG</topic><topic>NCRI</topic><topic>ONCOLOGY</topic><topic>RADIATION-THERAPY</topic><topic>RADIOTHERAPY</topic><topic>RISK</topic><topic>TARGET VOLUME DELINEATION</topic><topic>TIME</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aggarwal, Ajay</creatorcontrib><creatorcontrib>Court, Laurence Edward</creatorcontrib><creatorcontrib>Hoskin, Peter</creatorcontrib><creatorcontrib>Jacques, Isabella</creatorcontrib><creatorcontrib>Kroiss, Mariana</creatorcontrib><creatorcontrib>Laskar, Sarbani</creatorcontrib><creatorcontrib>Lievens, Yolande</creatorcontrib><creatorcontrib>Mallick, Indranil</creatorcontrib><creatorcontrib>Malik, Rozita Abdul</creatorcontrib><creatorcontrib>Miles, Elizabeth</creatorcontrib><creatorcontrib>Mohamad, Issa</creatorcontrib><creatorcontrib>Murphy, Claire</creatorcontrib><creatorcontrib>Nankivell, Matthew</creatorcontrib><creatorcontrib>Parkes, Jeannette</creatorcontrib><creatorcontrib>Parmar, Mahesh</creatorcontrib><creatorcontrib>Roach, Carol</creatorcontrib><creatorcontrib>Simonds, Hannah</creatorcontrib><creatorcontrib>Torode, Julie</creatorcontrib><creatorcontrib>Vanderstraeten, Barbara</creatorcontrib><creatorcontrib>Langley, Ruth</creatorcontrib><collection>Ghent University Academic Bibliography</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aggarwal, Ajay</au><au>Court, Laurence Edward</au><au>Hoskin, Peter</au><au>Jacques, Isabella</au><au>Kroiss, Mariana</au><au>Laskar, Sarbani</au><au>Lievens, Yolande</au><au>Mallick, Indranil</au><au>Malik, Rozita Abdul</au><au>Miles, Elizabeth</au><au>Mohamad, Issa</au><au>Murphy, Claire</au><au>Nankivell, Matthew</au><au>Parkes, Jeannette</au><au>Parmar, Mahesh</au><au>Roach, Carol</au><au>Simonds, Hannah</au><au>Torode, Julie</au><au>Vanderstraeten, Barbara</au><au>Langley, Ruth</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ARCHERY : a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer : study protocol</atitle><date>2023</date><risdate>2023</risdate><issn>2044-6055</issn><eissn>2044-6055</eissn><abstract>IntroductionFifty 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.MethodsARCHERY 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 disseminationThe 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.</abstract><pub>BMJ Publishing Group</pub><oa>free_for_read</oa></addata></record>
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source BMJ Open Access Journals; DOAJ Directory of Open Access Journals; PubMed Central Open Access; Ghent University Academic Bibliography; EZB-FREE-00999 freely available EZB journals; PubMed Central
subjects Adult oncology
COUNTRIES
DAHANCA
EORTC
HKNPCSG
Medicine and Health Sciences
NCIC CTG
NCRI
ONCOLOGY
RADIATION-THERAPY
RADIOTHERAPY
RISK
TARGET VOLUME DELINEATION
TIME
title ARCHERY : a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer : study protocol
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