Informatics Techniques to Optimize Mammography for Aging Populations

描述（由申请人提供）：本研究的目标是开发、整合和评估新的信息学技术，以优化老年女性乳腺癌的乳房X线诊断。65岁以上患乳腺癌的妇女死亡率更高，预后更差。这些妇女还承受着“过度诊断”的最沉重负担，这种现象是筛查发现的癌症可能不会继续引起症状或死亡。由于认识到2000年至2050年期间65岁以上的妇女人数预计将增加一倍以上（从2 000万增加到4 000万），这些发人深省的统计数字变得更加紧迫。在这项研究计划中，我们建议开发工具，以改善浸润性乳腺癌的早期诊断，最大限度地减少不必要的侵入性程序（减少假阳性），并同时解决过度诊断问题。具体来说，我们的目标是：1）开发一种由新型机器学习算法训练的概率计算机模型，使用逻辑预测模型（PLUM），为老年女性量身定制乳腺癌风险估计; 2）使用决策分析模型来确定最佳乳腺活检阈值，其使用重要的病理预后指标，在年龄背景下的细胞学分级，以及3）使用比较有效性分析来确定这些个性化的风险预测策略和最佳行动阈值将如何改善65岁以上女性的当前乳腺癌筛查政策。我们的多学科团队拥有创新研究的记录（包括NIH资助和医学，工程和计算机科学文献中的出版物），该研究整合了最先进的信息学算法以改善乳腺癌诊断。利用以前的经验和基础设施，我们正在构建一个全新的机器学习算法PLUM，它使用1）归纳逻辑编程（ILP）来准确地从多关系数据中学习; 2）使用年龄作为分区的提升建模，以及3）将规则纳入我们的概率模型以进行准确的风险预测。我们必须使用独特丰富的临床数据，对疾病过程的深刻理解，以及这些计算工具的创造性整合。此次重新提交的新初步数据预示着科学成功和临床转化。如果得到支持，该项目将证明风险预测和决策分析工具可以准确评估乳腺癌风险，确定最佳的个性化活检阈值，并提供比美国目前针对65岁以上女性的上级乳腺癌筛查政策。

DESCRIPTION (provided by applicant): The goal of this research is to develop, integrate, and evaluate novel informatics techniques to optimize the mammographic diagnosis of breast cancer in aging women. Women over age 65 who develop breast cancers have greater death rates and poorer outcomes. These women also bear the heaviest burden of "overdiagnosis" a phenomenon in which screening identifies cancer which may not go on to cause symptoms or death. These sobering statistics are made more urgent by the realization that the number of women > 65 is projected to more than double (from 20-40 million) between 2000 and 2050. In this research program, we propose to develop tools that will improve the early diagnosis of invasive breast cancer, minimize unnecessary invasive procedures (decrease false positives) and concomitantly address overdiagnosis. Specifically we aim to 1) develop a probabilistic computer model trained by a novel machine learning algorithm, Prediction using Logical Uplift Modeling (PLUM), that tailors breast cancer risk estimations to aging women; 2) use a decision analytic model to determine the optimal breast biopsy threshold using an important pathologic prognostic indicator, cytologic grade in the context of age, and 3) use comparative effectiveness analysis to determine how these personalized risk prediction strategies and optimal thresholds for action will improve on current breast cancer screening policies in women > 65. Our multidisciplinary team has a track record (including NIH funding and publications in the medical, engineering, and computer science literature) of innovative research that integrates state-of-the-art informatics algorithms to improve breast cancer diagnosis. Using prior experience and infrastructure, we are building a completely new machine learning algorithm, PLUM, which uses 1) inductive logic programming (ILP) to accurately learn from multi-relational data; 2) uplift modeling that uses age as a partition, and 3) rule incorporation into our probabilistic model for accurate risk prediction. We necessarily use a uniquely rich clinical data, a deep understanding of disease processes, and creative integration of these computational tools. New preliminary data presented in this resubmission foreshadow scientific success and clinical translation. If supported, this project will prove that risk prediction and decision analytic tools can accurately assess breast cancer risk, determine an optimal, personalized biopsy threshold, and provide a superior breast cancer screening policy than is currently employed in the US for women over age 65.