SBIR Phase I: Advanced Cancer Analytics Platform for Highly Accurate and Scalable Survival Models to Personalize Oncology Strategies

SBIR 第一阶段：先进的癌症分析平台，用于高精度和可扩展的生存模型，以个性化肿瘤策略

• 批准号: 2012214
• 负责人: Thomas Luechtefeld
• 金额: $ 22.45万
• 依托单位: INSILICA, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012214&HistoricalAwards=false
• 关键词: SBIR; Phase; Advanced; Cancer; Analytics

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will develop personalized clinical decision-making in cancer care. An estimated 17 million cases of cancer are diagnosed globally each year. Over $90 billion per year is spent in total on cancer-related health care in the U.S., and cancer patients pay over $4 billion out of pocket for health care. Therapeutic strategy selection and clinical trial research targeted to oncology become exponentially complex when unique types of cancer are considered, as well as how they may uniquely impact gender, race, ethnicity, and age of affected populations. The proposed technology will develop advanced bioinformatics models and visualization tools to guide decision-making by oncologists. It will develop and use advanced survival models targeting cancer types, other biological and chemical factors, and patient demographics. This Small Business Innovation Research (SBIR) Phase I project will focus on three objectives. 1) We will develop and validate transfer learning models that leverage large data sets from high-incidence cancer types to improve results of cancer types with sparse data. 2) We will leverage these data in a disease-agnostic platform using a recurrent neural network to account for temporal variation to predict survivability. 3) We will develop visualization tools for clinicians to understand causal relationships. This system will use several innovations: a) Transfer Learning to Scale Available Data: Since cancer survival modeling is limited in many cancer types due to lack of data, we will demonstrate the feasibility of transfer learning in this context. b) Single Recurrent Neural Network: We will implement a recurrent neural network to improve performance and allow a single network to be trained across all cancer types and patient population characteristics. c) Control Feature Mediation Analysis: We will develop accurate survival models with an understanding of the sensitivity to inputs. d) Clinician-Driven Interpretation and Visualization Tools: The framework needs interpretation and visualization features to reduce data into reports easily digestible for clinical decision-making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将开发癌症护理的个性化临床决策。据估计，全球每年诊断出1700万例癌症病例。在美国，每年用于癌症相关医疗保健的总费用超过900亿美元，癌症患者自掏腰包支付超过40亿美元的医疗费用。当考虑到独特类型的癌症以及它们如何独特地影响受影响人群的性别、种族、民族和年龄时，针对肿瘤学的治疗策略选择和临床试验研究变得指数级复杂。拟议的技术将开发先进的生物信息学模型和可视化工具，以指导肿瘤学家的决策。 它将开发和使用针对癌症类型，其他生物和化学因素以及患者人口统计学的先进生存模型。这个小企业创新研究（SBIR）第一阶段项目将集中在三个目标。1)我们将开发和验证迁移学习模型，这些模型利用来自高发病率癌症类型的大型数据集，以改善具有稀疏数据的癌症类型的结果。 2)我们将在一个疾病不可知的平台上利用这些数据，使用递归神经网络来解释时间变化，以预测生存率。 3)我们将为临床医生开发可视化工具，以了解因果关系。该系统将使用几项创新：a）迁移学习扩展可用数据：由于缺乏数据，癌症生存建模在许多癌症类型中受到限制，我们将在此背景下证明迁移学习的可行性。B）单一循环神经网络：我们将实施循环神经网络以提高性能，并允许在所有癌症类型和患者人群特征中训练单一网络。c）控制特征中介分析：我们将开发准确的生存模型，了解对输入的敏感性。d）临床医生驱动的解释和可视化工具：该框架需要解释和可视化功能，以将数据简化为易于消化的报告，供临床决策使用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。