Artificial Intelligence-Based Quality Assurance for Online Adaptive Radiotherapy

基于人工智能的在线自适应放射治疗质量保证

• 批准号: 10445135
• 负责人: Steve Bin Jiang
• 金额: $ 63.2万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-09 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445135
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Anatomy; Artificial Intelligence; Body Weight decreased; Clinical; Clinical Research; Communication; Complex; Coupled; Development; Dose; Ensure; Evaluation; Expert Systems; Goals; Human; Institution; Intelligence; Intervention; Knowledge; Linear Accelerator Radiotherapy Systems; Logistics; Magnetic Resonance Imaging; Measurement; Modeling; Modernization; Normal tissue morphology; Organ; Patient imaging; Patients; Physicians; Procedures; Process; Radiation Dose Unit; Radiation therapy; Risk; Safety; Schedule; System; Systems Development; Systems Integration; Testing; Therapeutic; Time; Toxic effect; Training; Translations; Validation; Variant; Vendor; base; cancer care; cancer radiation therapy; data acquisition; imaging capabilities; learning algorithm; patient safety; patient variability; preference; preservation; pressure; quality assurance; radiation response; sample fixation; soft tissue; tool; transfer learning; treatment planning; tumor

PROJECT SUMMARY Recently, the development of MR-LINACs has made high-quality online adaptative radiotherapy a clinical reality to account for the daily anatomical variations to preserve the treatment quality. MR-LINACs, combining modern radiotherapy linear accelerators (LINACs) with on-board magnetic resonance imaging (MRI), offer excellent soft-tissue contrast to allow accurate organ and tumor segmentation to precisely capture the daily anatomical changes of each patient. Coupled with advanced adaptive treatment planning systems, MR-LINAC is the ideal platform for online adaptive radiotherapy and will bring cancer radiotherapy to a new level of precision and personalization. However, this new format of radiotherapy also comes with new challenges for patient safety and plan quality checks that cannot be satisfactorily addressed with traditional quality assurance (QA) tools: 1) With the patient lying on the treatment couch waiting for the treatment to start, there is mounting pressure on the team to move through the workflow as fast as possible, which may increase the likelihood of making mistakes and thus an effective QA procedure is even more important. 2) Each adapted plan warrants a new QA process, adding substantial burdens to an already extremely time-constrained process. A QA process with high efficiency is needed. 3) Conventional QA procedures are quite complex, involving inputs from many stakeholders, and thus are human-power demanding and error-prone. An automatic QA procedure requiring minimal human interventions and communications is highly desired. 4) In addition to checking the quality of the adapted segmentation and treatment plan, it is also crucial for a QA procedure to ensure their consistency with the physician’s intentions/preferences in the original plan. 5) A QA tool that is able to predict the plan deliverability prior to treatments, without actually irradiating the patients, is needed for online adaptive radiotherapy. The overarching goal of this project is to develop an Artificial Intelligence (AI)-based QA system to address these urgent unmet clinical needs for MR-LINAC online adaptive radiotherapy, with four main components to: 1) intelligently assess the quality of the adapted target and organ-at-risk segmentations and their consistency with those in the original plan; 2) intelligently assess the quality of the adapted plan and its consistency with the original plan; 3) efficiently perform 2nd dose check with an AI-based near real-time independent dose engine; and 4) predict the measurement-based QA results of plan deliverability using prior knowledge and new adapted plan information. We have two Specific Aims: 1) System development, including data acquisition for AI model training, and development of four AI models; and 2) System translation and validation at multiple institutions, including developing transfer learning algorithm and package for automated model commissioning; and translation, fine-tuning and evaluation of the developed AI systems. The successful conduct of the proposed project will result in the first intelligent, efficient, reliable, and independent QA system to facilitate unleashing the full potential of MR-LINAC online adaptive radiotherapy to advance cancer care.

项目总结