Beyond Body Mass Index: Bringing Body Composition into Oncology Practice

项目总结/摘要 这个职业发展奖将支持伊丽莎白费利西亚诺，科学博士，在她过渡到独立， 利用电子数据来改善癌症护理的翻译研究人员。她的长期目标是阐明 身体成分如何影响癌症结果;开发使用常规收集数据的电子工具， 改进说明和指导临床干预;并合作使用这些电子工具， 改善癌症预后的干预措施。 肌肉减少症（低骨骼肌质量）和肌肉减少性肥胖症（肌肉减少症伴过度肥胖）是 常见于癌症患者，一般为隐匿性，并伴有手术并发症，治疗毒性大，不良反应少， 生活质量，并降低各种癌症类型的生存率。尽管具有很强的预测性， 可修改的身体组成数据在临床决策中未得到充分利用。 为了解决这个问题，Feliciano博士将开发具有成本效益的，临床友好的方法来识别患者， 肌肉减少症下一步，她将开发一种工具，将身体成分信息传达给癌症护理人员。 团队使用来自近6,000名非转移性癌症患者的丰富电子病历和成像数据， 结直肠癌和乳腺癌患者，她将：验证软件，自动评估身体成分 从临床获得的成像（目标1）;开发一个预测模型，以确定基于电子肌肉减少症 医疗数据，用于不接受成像的癌症患者（目标2）;并整合身体成分 信息纳入临床实践中使用的结直肠癌风险预测模型， 这些信息以可理解和可操作的格式提供给临床医生（目标3）。 费利西亚诺博士非常适合进行这项研究的基础上，她的经验，身体组成 评估和癌症流行病学，一个特殊的指导团队谁将确保科学的严谨性， 临床相关性，以及在综合医疗保健系统中的独特环境， 电子医疗记录和410万成员的种族多样化的患者群体。她的机构， 凯撒永久北方加州，提供了一个理想的培训基地，她获得所需的技能，通过 课程，会议，研讨会，并在以下领域的体验式学习：高级分析 方法（预测建模，生物医学图像分析和数据可视化）;癌症生物学和临床 癌症护理;和独立研究者的核心能力（赠款写作，负责任的行为， 研究和临床研究翻译）。通过拟议的研究和培训，她将获得 初步的数据、知识以及临床和科学合作伙伴关系，她需要开发具有竞争力的R 01 建议，并建立一个成功的研究计划，重点是优化使用电子数据， 改善癌症护理。

PROJECT SUMMARY/ABSTRACT This Career Development Award will support Elizabeth Feliciano, ScD, in her transition to independence as a translational researcher utilizing electronic data to improve cancer care. Her long-term goals are to elucidate how body composition influences cancer outcomes; develop electronic tools that use routinely collected data to improve prognostication and guide clinical interventions; and collaborate to use these electronic tools in interventions that improve cancer outcomes. Sarcopenia (low skeletal muscle mass) and sarcopenic obesity (sarcopenia with excess adiposity) are common in cancer patients, typically occult, and associated with surgical complications, treatment toxicity, poor quality of life, and reduced survival across a variety of cancer types. Despite being strongly prognostic and modifiable, body composition data is under-utilized in clinical decision-making. To address this, Dr. Feliciano will develop cost-effective, clinic-friendly methods to identify patients with sarcopenia. Next, she will develop a tool that communicates body composition information to a cancer care team. Using rich electronic medical record and imaging data from a cohort of nearly 6,000 non-metastatic colorectal and breast cancer patients, she will: validate software to automatically assess body composition from clinically-acquired imaging (Aim 1); develop a predictive model to identify sarcopenia based on electronic medical data, for use in cancer patients who do not undergo imaging (Aim 2); and integrate body composition information into established risk prediction models for colorectal cancer used in clinical practice and present this information in an understandable and actionable format to clinicians (Aim 3). Dr. Feliciano is well suited to perform this research based on her experience in body composition assessment and cancer epidemiology, an exceptional mentoring team who will ensure scientific rigor and clinical relevance, and the unique setting within an integrated healthcare system with a comprehensive electronic medical record and an ethnically diverse patient population of 4.1-million members. Her institution, Kaiser Permanente Northern California, provides an ideal training ground for her to gain needed skills through coursework, conferences, seminars, and experiential learning in the following domains: advanced analytic methods (predictive modeling, biomedical image analysis, and data visualization); cancer biology and clinical cancer care; and core competencies of an independent investigator (grant-writing, responsible conduct of research, and clinical translation of research).Through the proposed research and training, she will acquire the preliminary data, knowledge, and clinical and scientific partnerships she needs to develop a competitive R01 proposal and to establish a successful research program focused on optimizing use of electronic data to improve cancer care.