Semi-automated bladder cancer screening using machine learning: clinical validation and implementation.

项目概要/摘要： 膀胱癌是世界上第7大最常见的恶性肿瘤，并且在所有癌症中复发率最高 (70 1 -3例有危险因素（吸烟、砷/化学染料暴露）和/或血尿的患者， 通过分析尿液筛查膀胱癌。尿液中的细胞成分沉积在 玻璃载玻片，染色，并由细胞病理学家使用金标准检查膀胱癌的特征 巴黎系统用于尿液细胞病理学。4然而，巴黎系统是主观的， 细胞是高度变化的，这使得该过程变得困难，并且容易出现观察者之间的高度变异性和人为错误 一种更定量、自动化的评估尿细胞病理学的方法， 膀胱癌是必需的。事实证明，机器学习（ML）技术在基于图像的应用中非常有效 病理学分类，因为ML模型可重复运行且无偏差（除非训练数据是 有偏见）或疲劳。巴氏涂片已经常规处理的半自动ML系统（BD FocalPoint），并与尿细胞学标本有许多共同特征，因为两者都是癌症筛查 依赖于细胞和核形态并通过液基制备（LBP，例如ThinPrep）制备的测试 方法.然而，到目前为止，还没有开发出一种系统来以这种方式利用ML治疗膀胱癌，这是我打算的一个事实。 改变虽然我坚信病理学作为一门学科， 数字服务，有很大的惯性克服，以取代目前的模拟显微镜技术。我们 必须超越简单地通过增强病理学家的ML技能来提供数字替代方案 这些算法使他们能够更高效、快速和安全地工作。膀胱尿细胞学筛查 癌症是一个理想的使用案例。因此，我们试图创建一个基于ML的原型算法，称为AutoParis， 将使巴黎系统的制表自动化。AutoParis的最初原型被证明是高度 通过对与细胞核/细胞质比率相关的统计数据制表对尿细胞学标本进行有效风险分层 (NC比率，肿瘤形成的一个非常重要的指标）和细胞/核形态学异常。 AutoParis作为细胞病理学家的诊断辅助工具需要几个额外的步骤。虽然我很熟练 足够的代码模型的第一次迭代，我达到了我可以完成的极限，作为一个自学成才的人。 程序员和数据科学家。为了完成我在AutoParis上的工作并继续在该领域进行创新 数字病理学和机器学习，我需要一个更正规的教育，在专业的数学，统计学，机器学习理论 和编程。通过这个奖项，我将继续在达特茅斯学院的课程， 专家导师团队。我的导师和合作者也因为他们帮助测试的能力而被选中 数字决策辅助工具的验证、资助和手稿准备以及实验室管理。我会从这里走出来 我需要成为未来ML开发及其在临床应用中的领导者的技能经验 药

Project Summary / Abstract: Bladder cancer is the 7th most common malignancy worldwide and has the highest recurrence rate of any cancer (70%).1–3 Patients with risk factors (smoking, arsenic / chemical dye exposure) and / or hematuria are routinely screened for bladder cancer via analysis of voided urine. The cellular elements of the urine are deposited to glass slides, stained, and examined by a cytopathologist for features of bladder cancer using the gold standard Paris System for Urine Cytopathology.4 However, the Paris System is subjective and the morphology of urothelial cells is highly varied, making the process difficult and prone to high interobserver variability and human errors borne of fatigue and overwork.5,6 A more quantitative, automated method of assessing urine cytopathology for bladder cancer is needed. Machine learning (ML) technologies have proven to be highly effective in image based classification in pathology, in that ML models operate reproducibly and without bias (unless the training data is biased) or fatigue. Pap smears are already routinely processed by a semi-automated ML system (BD FocalPoint), and share many common features with urine cytology specimens in that both are cancer screening tests relying on cellular and nuclear morphology and prepared by Liquid Based Preparation (LBP, e.g. ThinPrep) methods. Yet to date no system has been developed to harness ML for bladder cancer in this way, a fact I intend to change. While it is my strong belief that pathology as a discipline is poised to make the transition to a 100% digital service, there is significant inertia to overcome to replace the current analog microscope technology. We must go beyond simply providing a digital alternative by augmenting the skills of the pathologist with ML algorithms that empower them to work more efficiently, quickly and safely. Urine cytology screening for bladder cancer is an ideal use case. Thus we sought to create a prototype ML based algorithm, dubbed AutoParis, that would automate the tabulation of the Paris System. The initial prototype of AutoParis proved to be highly effective at risk stratifying urine cytology specimens by tabulating statistics related to nuclear to cytoplasmic ratio (NC ratio, a very important indicator of neoplasia) and cellular / nuclear morphological atypia.7 Deploying AutoParis as a diagnostic aid to the cytopathologist will require several additional steps. Although I was skilled enough to code the first iteration of the model, I am reaching the limits of what I can accomplish as a self-taught programmer and data scientist. In order to complete my work on AutoParis and continue to innovate in the field of digital pathology and ML, I need a more formalized education in specialized mathematics, statistics, ML theory and programming. Through this award I will pursue a curriculum of courses at Dartmouth College guided by a team of expert mentors. My mentors and collaborators were also selected for their ability to help with the testing and validation of digital decision aids, grant and manuscript prep and lab management. I will emerge from this experience with the skills I need to be a leader in the future of ML development and its adoption in clinical medicine.