Novel Tree-based Statistical Methods for Cancer Risk Prediction

用于癌症风险预测的新的基于树的统计方法

• 批准号: 8373032
• 负责人: ANNETTE M MOLINARO
• 金额: $ 33.56万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-10 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8373032
• 关键词: Accounting; Anxiety; Breast; Carcinoma; Clinical; Code; Cohort Studies; Communities; Computer software; Decision Making; Diagnosis; Epidemiology; Face; Face Processing; Foundations; Goals; Health Benefit; Incidence; Individual; Intervention Studies; Label; Learning; Left; Lesion; Machine Learning; Malignant Neoplasms; Mammography; Mastectomy; Measures; Methods; Modeling; Noninfiltrating Intraductal Carcinoma; Outcome; Patients; Performance; Public Health; Publishing; Radiation; Radiation therapy; Recurrence; Research Design; Risk; Risk Estimate; Screening for cancer; Statistical Methods; Stratification; Techniques; Trees; Validation; Woman; Work; anticancer research; base; breast lumpectomy; cancer risk; clinically relevant; cohort; design; expectation; experience; flexibility; high risk; indexing; loss of function; malignant breast neoplasm; mortality; novel; open source; population based; predictive modeling; prevent; programs; research study; simulation; tool

DESCRIPTION (provided by applicant): The contradiction of early cancer detection is that while some benefit others receive a detrimental diagnosis. A definitive example is mammography and ductal carcinoma in situ (DCIS), a noninvasive breast cancer. DCIS, which most frequently presents as a non-palpable lesion, was rarely detected before the advent of modern mammography. Since 1983 there has been a 290% increase in DCIS incidence in women under 50 and 500% in those over 50. Given that only 5-10% of DCIS cases progress to invasive cancer with a 10-year mortality rate of 1-2%, DCIS experts suggest breast conservation for the majority of patients. However, these women continue to be overtreated with mastectomy and radiation, at rates comparable to those with invasive cancer. The inability to discern those at low vs. high risk is due in part to non-reproducible study results as well as inadequate statistical methods for risk prediction and validation. We have collected a population-based DCIS cohort with the goal of delineating those women least likely to recur with invasive cancer and, hence, appropriate candidates for less aggressive treatments. Recently we established risk indices and published the corresponding absolute risk estimates for type of recurrence. However, two features of the study design, namely the presence of competing risks and the use of a stratified case-cohort design, constrained us to using crude empirical methods for analysis and left us unable to validate the clinical utility of our models. The overarching goal of this proposal is to develop a unified, principled statistical framework for building, selecting, and evaluating clinically relevant risk indices, permitting refinement and validation of existing risk prediction models in our DCIS study as well as beyond. We face multiple challenges including how to objectively build risk indices with relevant variables; how to estimate the corresponding risks (competing or not) in various subsample study designs; and, how to validate the resulting risk prediction models. Recently, we developed partDSA, a tree-based method which affords tremendous flexibility in building predictive models and provides an ideal foundation for developing a clinician- friendly tool for accurate stratification and risk prediction. In its curret form, partDSA is unable to estimate absolute risk in the presence of competing risks accounting for subsample study designs. Here we extend partDSA for such clinically relevant scenarios (Aim 1). We also propose aggregate learning for risk prediction to increase prediction accuracy and subsequently to build more stable but easily interpretable risk models (Aim 2). Finally, we propose the necessary methods for validating the resulting models (Aim 3). Our proposal has two immediate public health benefits: first, these novel statistical methods will result in a clinician-friendly, publicly available tool for accurate risk prediction, stratification and validaion in numerous clinical settings; second, current DCIS risk models will be refined and validated with the expectation of better delineating those at low risk, hence strong candidates for conservative treatments including active surveillance. PUBLIC HEALTH RELEVANCE: Our proposal has two public health components: first, our novel statistical methods will provide a clinician- friendly, publicly available tool for accurate isk prediction, stratification and validation in numerous clinical settings; second, current ductal carcinoma in situ risk models will be refined and validated, helping facilitate the decision-making process faced by patients and their clinicians.

描述（由申请人提供）：早期癌症检测的矛盾在于，有些人受益，而另一些人却得到了有害的诊断。一个明确的例子是乳房x光检查和导管原位癌（DCIS），一种非侵入性乳腺癌。DCIS通常表现为不可触及的病变，在现代乳房x光检查出现之前很少被发现。自1983年以来，50岁以下妇女DCIS发病率增加了290%，50岁以上妇女DCIS发病率增加了500%。考虑到只有5-10%的DCIS病例发展为浸润性癌症，10年死亡率为1-2%，DCIS专家建议大多数患者保留乳房。然而，这些妇女继续接受乳房切除术和放疗的过度治疗，其比率与浸润性癌症相当。无法区分高风险和低风险的部分原因是研究结果不可重复，以及用于风险预测和验证的统计方法不充分。我们收集了一个以人群为基础的DCIS队列，目的是描述那些最不可能复发的浸润性癌症的妇女，因此，合适的候选人进行不那么积极的治疗。最近，我们建立了风险指数，并发表了相应的复发类型的绝对风险估计。然而，研究设计的两个特点，即存在竞争风险和使用分层病例队列设计，限制了我们使用粗糙的经验方法进行分析，使我们无法验证我们模型的临床实用性。本提案的总体目标是建立一个统一的、有原则的统计框架，用于建立、选择和评估临床相关的风险指标，允许在我们的DCIS研究中改进和验证现有的风险预测模型。如何客观地构建具有相关变量的风险指标；在不同的子样本研究设计中，如何估计相应的风险（竞争或不竞争）；如何验证所得到的风险预测模型。最近，我们开发了partDSA，这是一种基于树的方法，在建立预测模型方面提供了极大的灵活性，为开发临床友好的工具提供了理想的基础，用于准确的分层和风险预测。在目前的形式下，partDSA无法在考虑子样本研究设计的竞争风险的情况下估计绝对风险。在此，我们将部分dsa扩展到此类临床相关场景（目的1）。我们还提出了用于风险预测的聚合学习，以提高预测准确性，并随后建立更稳定但易于解释的风险模型（目标2）。最后，我们提出了验证所得模型的必要方法（目标3）。我们的建议有两个直接的公共卫生效益：首先，这些新颖的统计方法将导致临床医生友好，公开可用的工具，用于准确的风险预测，分层和验证在许多临床环境中；其次，目前的DCIS风险模型将被完善和验证，期望更好地描述那些低风险的人，因此包括主动监测在内的保守治疗的强有力的候选者。