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

这项小企业创新研究（SBIR）一期项目的广泛影响/商业潜力将在癌症治疗中开发个性化的临床决策。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费用总计超过900亿美元，癌症患者自掏腰包支付的医疗保健费用超过40亿美元。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，针对肿瘤的治疗策略选择和临床试验研究变得指数级复杂。提出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）第一阶段项目将侧重于三个目标。1)我们将开发和验证迁移学习模型，利用来自高发病率癌症类型的大数据集来改进具有稀疏数据的癌症类型的结果。2)我们将在疾病诊断平台中利用这些数据，使用递归神经网络来解释时间变化以预测生存能力。3)开发可视化工具，帮助临床医生理解因果关系。该系统将使用几个创新：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，因此我们将在此背景下演示迁移学习的可行性。b)单个递归神经网络：我们将实现一个递归神经网络来提高性能，并允许单个网络在所有癌症类型和患者群体特征上进行训练。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理解的报告，用于临床决策。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.