Towards personalized medicine with theranostics: quantitative molecular imaging and artificial intelligence

通过治疗诊断学实现个性化医疗：定量分子成像和人工智能

• 批准号: RGPIN-2021-02965
• 负责人: Uribe, Carlos
• 金额: $ 1.75万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764465
• 关键词: Towards; personalized; medicine; theranostics; quantitative

The exciting field of theranostics (i.e. therapy + diagnostics) in nuclear medicine involves usage of radiopharmaceutical imaging in tandem with (before, during or after) radiopharmaceutical therapy (RPT). Imaging is done with single photon computed emission tomography/computed tomography (SPECT/CT) or positron emission tomography (PET), while RPT uses alpha- or beta-emitting radioisotopes. Contrary to what is routine for external beam radiation therapy (EBRT), RPT is presently not planned based on individual patient characteristics. Dosimetry in RPT is believed to be difficult and time-consuming, and thus, radiation doses delivered to tumors/healthy organs are commonly not quantified. Basic science research involving physics, applied math, engineering, and computer science, while working closely with physicians and technologists, are essential in closing the gap between RPT and EBRT. My proposed program aims at bringing RPT on par with EBRT, utilizing quantitative imaging and artificial intelligence (AI), to allow for personalized dose assessments in RPT. My short-term objectives are: 1)Develop PET and SPECT imaging protocols to measure radiopharmaceutical biodistribution using theranostic pairs even for radioisotopes that cannot be directly imaged. 2)Develop robust image segmentation algorithms to accurately quantify tumor/organ radioactivity and mass (both required in dosimetry) from SPECT/CT and PET/CT images, and enable automated segmentation using AI. 3)Study contributions to uncertainties in the dose estimates from quantitative image generation and dosimetry method (e.g. organ level vs. voxelized methods). 4)Combine quantitative, texture, shape, and intensity image features (radiomics) from diagnostic PET, therapy SPECT, and dose images to improve/simplify dose estimates and build predictive models for doses/responses. My program is designed with a strong component in training of highly qualified personnel (HQP). HQP are mentored to become experts/leaders, whether they pursue a career in industry, academia, or healthcare practice. HQP will develop their own thinking and decision-making, while actively mentored and supported. They participate in and enhance our industry-academic collaborations. HQP will network and disseminate our work at scientific conferences and are encouraged to join committees of societies in our field. Scientific excellence will be pursued and propelled in the context of a multidisciplinary, multicultural, diverse, and inclusive team environment. Our experience with quantitative imaging, dosimetry, AI, and the support from both BC Cancer and the University of British Columbia make this program a great venue and opportunity to put Canada at the forefront of personalized RPT. The program will make critical contributions to enable simplified dosimetry protocols, establish tools for automatic segmentation, enable personalized RPT planning, train HQP, and expand collaborations with industry and academic partners.

核医学中令人兴奋的治疗诊断学（即治疗+诊断）领域涉及放射性药物成像与放射性药物治疗（RPT）（之前、期间或之后）的结合使用。成像通过单光子计算机发射断层扫描/计算机断层扫描 (SPECT/CT) 或正电子发射断层扫描 (PET) 完成，而 RPT 使用 α 或 β 发射放射性同位素。与外照射放射治疗 (EBRT) 的常规相反，RPT 目前并未根据患者个体特征进行规划。 RPT 中的剂量测定被认为是困难且耗时的，因此，传递到肿瘤/健康器官的辐射剂量通常无法量化。涉及物理、应用数学、工程和计算机科学的基础科学研究，同时与医生和技术人员密切合作，对于缩小 RPT 和 EBRT 之间的差距至关重要。我提出的计划旨在利用定量成像和人工智能 (AI)，使 RPT 与 EBRT 同等水平，以便在 RPT 中进行个性化剂量评估。我的短期目标是： 1) 开发 PET 和 SPECT 成像方案，以使用治疗诊断对测量放射性药物的生物分布，即使是无法直接成像的放射性同位素。 2）开发强大的图像分割算法，以准确量化 SPECT/CT 和 PET/CT 图像中的肿瘤/器官放射性和质量（两者都是剂量测定所需的），并使用 AI 实现自动分割。 3）研究定量图像生成和剂量测定方法（例如器官水平与体素化方法）对剂量估计不确定性的贡献。 4) 结合来自诊断 PET、治疗 SPECT 和剂量图像的定量、纹理、形状和强度图像特征（放射组学），以改进/简化剂量估计并建立剂量/反应的预测模型。我的课程在设计时就包含了高素质人才 (HQP) 培训的重要组成部分。无论 HQP 是在工业界、学术界还是医疗保健实践领域从事职业生涯，他们都会接受指导成为专家/领导者。总部将发展自己的思维和决策，同时积极指导和支持。他们参与并加强我们的产学合作。 HQP 将在科学会议上联网和传播我们的工作，并鼓励加入我们领域的学会委员会。将在多学科、多文化、多元化和包容性的团队环境中追求和推动科学卓越。我们在定量成像、剂量测定、人工智能方面的经验以及 BC Cancer 和不列颠哥伦比亚大学的支持使该计划成为使加拿大处于个性化 RPT 前沿的绝佳场所和机会。该计划将为简化剂量测定方案、建立自动分割工具、实现个性化 RPT 规划、培训 HQP 以及扩大与行业和学术合作伙伴的合作做出重要贡献。