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是，虽然有些受益，但另一些受益者得到了有害的诊断。一个明确的例子是乳腺癌和导管癌（DCIS），一种无创乳腺癌。在现代乳房X线摄影问世之前，很少发现DCI，最常见的是不可堵塞的病变。自1983年以来，在50岁以上的女性中，DCIS的发病率增加了290％。鉴于DCIS的10年死亡率为1-2％，DCIS病例中只有5-10％的DCIS病例进展为侵袭性癌症，DCIS专家认为大多数患者的乳房保存。但是，这些妇女继续以乳房切除术和放射线过度治疗，速度与侵入性癌症患者相当。无法辨别低风险和高风险的人的一部分是由于不可再生的研究结果以及风险预测和验证的统计方法不足。 我们已经收集了一个基于人群的DCIS队列，目的是描述那些最不可能因侵入性癌症而复发的女性，因此，适当的候选人进行了侵略性较低的治疗。最近，我们建立了风险指数，并发布了复发类型的相应的绝对风险估计。但是，研究设计的两个特征，即存在竞争风险和使用分层的案例 - 霍特设计，使我们限制了使用粗略的经验方法进行分析，并且使我们无法验证模型的临床实用性。该提案的总体目标是开发一个统一的，原则性的统计框架，用于建立，选择和评估临床相关的风险指数，允许在我们的DCIS研究以及其他方面对现有风险预测模型进行改进和验证。 我们面临多个挑战，包括如何客观地建立具有相关变量的风险指数；如何在各种子样本研究设计中估计相应的风险（是否竞争）；并且，如何验证由此产生的风险预测模型。最近，我们开发了PartDSA，这是一种基于树木的方法，它在构建预测模型方面具有巨大的灵活性，并为开发临床医生友好的工具提供了理想的基础，以进行准确的分层和风险预测。 PartDSA以其居民形式无法估计在存在子样本研究设计的竞争风险的情况下，绝对风险。在这里，我们将PARTDSA扩展到此类相关方案（AIM 1）。我们还建议进行总体学习，以提高预测准确性，然后建立更稳定但易于解释的风险模型（AIM 2）。最后，我们提出了验证结果模型的必要方法（AIM 3）。 我们的建议具有两个直接的公共卫生益处：首先，这些新颖的统计方法将导致临床医生友好的公开使用工具，以在许多临床环境中进行准确的风险预测，分层和有效性；其次，当前的DCIS风险模型将通过更好地描绘出低风险的人的期望进行完善和验证，因此，包括积极监视在内的保守治疗方法的强大候选人。 公共卫生相关性：我们的建议有两个公共卫生组成部分：首先，我们的新颖统计方法将为临床医生提供友好的公开工具，以在许多临床环境中进行准确的ISK预测，分层和验证；其次，当前的导管癌原位风险模型将得到完善和验证，有助于促进决策 患者及其临床医生面临的过程。