ARCHERY: Artificial Intelligence based Radiotherapy treatment planning for Cervical and Head and Neck cancer

ARCHERY：基于人工智能的宫颈癌和头颈癌放射治疗计划

• 批准号: 10415314
• 负责人: Ajay Aggarwal
• 金额: $ 45.74万
• 依托单位: UNIVERSITY COLLEGE LONDON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415314
• 关键词: Address; Anatomy; Area; Artificial Intelligence; Automation; Cancer Burden; Cancer Center; Cancer Hospital; Cancer Patient; Cervical; Clinical; Computer software; Cost Savings; Country; Disease; Effectiveness; Ensure; European; Future; Head Cancer; Head and Neck Cancer; Health Status; Human Resources; Improve Access; Incidence; Income; India; International; Jordan; Low income; Malaysia; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of esophagus; Malignant neoplasm of lung; Manuals; Medical; Modality; Modeling; Multicenter Studies; Neck Cancer; Oncologist; Organ; Outcome Measure; Pathway interactions; Patient Representative; Patients; Positioning Attribute; Process; Prospective Studies; Protocols documentation; Public Health; Public Sector; Quality of life; Radiation; Radiation Oncology; Radiation induced damage; Radiation therapy; Resources; Risk; Sample Size; Shapes; Societies; South Africa; Specialist; State Hospitals; Surveys; Time; Woman; X-Ray Computed Tomography; anticancer research; base; burden of illness; cancer therapy; cancer type; cost; curative treatments; design; economic impact; improved; low and middle-income countries; low income country; malignant breast neoplasm; meetings; men; mortality; palliation; prospective; radiation risk; secondary outcome; time use; treatment optimization; treatment planning; tumor; web services

PROJECT SUMMARY 50% of cancer patients require radiotherapy during their disease course, however, only 10-40% of patients in low and middle-income countries (LMICs), have access to it. A shortfall in the specialised workforce to deliver radiotherapy has been identified as the most significant barrier to expanding radiotherapy capacity. The current radiotherapy workflow is inefficient requiring several labor intensive processes and takes weeks to months to deliver in LMICs. The growing demand for cancer treatment means that the ratio of incidence to mortality will continue to worsen without a scalable solution. Artificial intelligence (AI) based software has been developed to automate two components of the radiotherapy planning pathway 1. Delineation of anatomical areas that are at risk of tumour spread and at risk of radiation damage. 2. 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 less than a day) and human resources needed to deliver radiotherapy. We propose a non-randomised prospective study to evaluate the quality and economic impact of AI based automated radiotherapy treatment for cervical cancer and head and neck cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 706 patients (353 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 to establish the cost and resource impact of automation 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 ensure we include a broad range of patients and the representativeness of the findings will support implementation of the software in LMICs. 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, and affordability of this key modality of cancer cure and control.

项目摘要 50％的癌症患者需要放疗，但只有10-40％ 在低收入和中等收入国家（LMIC）中，可以使用它。专业劳动力短缺 提供放射疗法已被确定为扩大放射疗法能力的最重要障碍。 当前的放射治疗工作流量效率低下，需要几个劳动密集型过程，并且需要 数周到几个月来运送LMIC。对癌症治疗的需求不断增长意味着 没有可扩展解决方案的情况下，死亡率的发病率将继续恶化。 已经开发了基于人工智能（AI）的软件来自动化两个组件 放射治疗规划途径1。描述有肿瘤蔓延风险的解剖区域 辐射损坏的风险。 2。辐射梁的位置，大小和形状的定义。建议的 优点包括提高的治疗精度，以及时间的减少（从几周到更少 比一天）和提供放疗所需的人力资源。 我们提出了一项非随机的前瞻性研究，以评估基于AI的质量和经济影响 宫颈癌和头颈癌的自动放疗治疗 LMIC，放射治疗是主要的治疗方法。样本量为706 根据估计的95％治疗计划，已经计算出患者（每种癌症类型为353） 可接受率。节省时间和成本将作为建立次要结果措施分析 使用基于时间驱动活动的成本成本成本模型对自动化的成本和资源影响。 这项为期48个月的研究将在印度的六家公共部门癌症医院（n = 2），约旦（n = 1），马来西亚 （n = 1）和南非（n = 2），以确保我们包括广泛的患者和代表性 这些发现将支持LMIC中软件的实现。 如果满足研究目标，则将以AI为基础的软件作为非营利性网络服务 LMIC的公共部门州立医院支持扩大高质量放射疗法的能力，改善 癌症治疗和控制的这种关键方式的访问和负担能力。