Machine Learning Methods to Predict Cancer Progression and Estimate Treatment Effectiveness

预测癌症进展和估计治疗效果的机器学习方法

• 批准号: 10384213
• 负责人: Zeshan M Hussain
• 金额: $ 5.18万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10384213
• 关键词: Address; Beds; Benchmarking; Biological Markers; Cancer Biology; Cancer Patient; Caring; Case Study; Cause of Death; Cessation of life; Chronic Disease; Chronic Kidney Failure; Clinical; Combination Drug Therapy; Combined Modality Therapy; Confidence Intervals; Cystic Fibrosis; Data; Data Set; Decision Making; Diagnosis; Disease; Early Diagnosis; Effectiveness; Elements; Ethics; Evaluation; Future; Goals; Knowledge; Machine Learning; Malignant Neoplasms; Methodology; Methods; Modeling; Multiple Myeloma; Observational Study; Outcome; Output; Parkinson Disease; Patients; Physicians; Plasma Cells; Progression-Free Survivals; Randomized Controlled Trials; Recurrence; Research; Running; Sample Size; Sampling; Serum; Severities; Signal Transduction; Testing; Time; Training; Treatment Effectiveness; Treatment Efficacy; Triplet Multiple Birth; Visualization; Work; base; cancer care; cancer cell; cancer heterogeneity; cancer survival; cancer therapy; clinical care; clinical decision support; clinical decision-making; clinical predictors; common treatment; cost effective; disease registry; improved; innovation; insight; learning algorithm; machine learning method; machine learning model; method development; multiple myeloma M Protein; neoplasm registry; novel; patient biomarkers; prevent; prognostication; prospective; rare cancer; response; support tools; survival prediction; tool; transcriptome sequencing; treatment effect; treatment response; treatment strategy; tumor heterogeneity; tumor progression

Project Summary Cancer is a leading cause of death worldwide. In the past few years, an average of around 18.1 million new cases of cancer (per year) were diagnosed. Physicians often decide which treatment to give a patient with the goals of prolonging overall survival, preventing recurrence, and minimizing complications. Generally, Randomized Controlled Trials (RCTs) are used to determine the efficacy of one therapy versus another therapy but are untenable in many situations due to ethical and financial constraints. Recent work has leveraged observational data to develop machine learning models that capture the progression of chronic diseases such as Cystic Fibrosis and Parkinson’s. However, using machine learning to determine treatment efficacy and predict important clinical endpoints, such as overall survival (OS) or progression free survival (PFS), in cancer has not been well studied. This gap in knowledge is due to a lack of benchmark cancer datasets, limited sample sizes for rare cancers, and challenges specific to cancer management, such as tumor heterogeneity within patients leading to differential treatment response. In spite of these challenges, recent methodological improvements in machine learning, such as the use of inductive biases and auxiliary data to improve prediction in data-scarce settings as well as improved treatment effect estimation methods, present an opportunity to test the promise of machine learning in the cancer setting. Therefore, the overarching goal of the proposed work is to develop methods that will enable training of machine learning models that capture the signal in longitudinal, observational cancer data and ultimately improve prediction of clinical endpoints as well as estimation of cancer treatment effects. As a case study and evaluation bed for my development of these methods, I will focus on multiple myeloma, an incurable plasma cell cancer. Aim 1 of this proposal will focus on improving prediction of survival endpoints and depth of treatment response. I will train a latent variable model with a novel learning algorithm that will leverage auxiliary longitudinal data to improve the power of the model, enabling better prediction of clinical endpoints. Aim 2 will tackle the related, yet distinct, question of treatment effect estimation, particularly with respect to different combination chemotherapies. Meta-learner models will be used to estimate average and conditional average treatment effects. A sensitivity analysis framework with clinically-interpretable sensitive parameters will be used to assess reliability of the estimates. Finally, aim 3 will provide a machine learning decision support tool to augment physician decision making in cancer management. A user study will be conducted with the tool to determine if it improves physician assessment of patients. This proposal provides a general methodological framework that can be applied to any cancer dataset and improves understanding of how to effectively use machine learning models trained on observational data to improve care of cancer patients.

项目摘要 癌症是世界范围内的主要死亡原因。在过去的几年里，平均约1810万新 诊断出癌症的病例（每年）。医生经常决定给病人什么样的治疗， 目的是延长总生存期，防止复发，并尽量减少并发症。一般而言， 随机对照试验（RCT）用于确定一种治疗与另一种治疗的疗效 但由于道德和财政限制，在许多情况下是站不住脚的。最近的工作利用了 观察数据来开发机器学习模型，以捕捉慢性疾病的进展， 囊性纤维化和帕金森病然而，使用机器学习来确定治疗效果， 预测癌症的重要临床终点，如总生存期（OS）或无进展生存期（PFS） 还没有得到很好的研究。这种知识差距是由于缺乏基准癌症数据集， 罕见癌症的样本量，以及癌症管理特有的挑战，如肿瘤异质性 导致不同的治疗反应。尽管存在这些挑战， 机器学习的改进，例如使用归纳偏差和辅助数据来改进预测 在数据稀缺的情况下，以及改进的治疗效果估计方法，提供了一个检验的机会。 机器学习在癌症治疗中的前景。因此，拟议的总体目标 工作是开发能够训练捕获信号的机器学习模型的方法 在纵向、观察性癌症数据中， 以及癌症治疗效果的估计。作为一个案例研究和评估床，我的发展， 除了这些方法之外，我将重点关注多发性骨髓瘤，一种无法治愈的浆细胞癌。本提案的目标1将 重点是改善生存终点的预测和治疗反应的深度。我会训练一个 变量模型与新的学习算法，将利用辅助纵向数据，以提高 模型的功效，能够更好地预测临床终点。目标2将解决相关但不同的问题， 治疗效果评估问题，特别是关于不同的联合化疗。 元学习者模型将用于估计平均和条件平均治疗效果。灵敏度 将使用具有临床可解释敏感参数的分析框架来评估 估算最后，aim3将提供一个机器学习决策支持工具，以增强医生的决策 在癌症管理中的作用。将对该工具进行用户研究，以确定其是否有所改进 医生对患者的评估。该建议提供了一个总体方法框架， 适用于任何癌症数据集，并提高对如何有效使用机器学习模型的理解 接受观察数据培训，以改善癌症患者的护理。