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

项目摘要/摘要： 膀胱癌是全球第7大最常见的恶性肿瘤，是所有癌症中复发率最高的。 (70%)1-3有危险因素(吸烟、砷/化学染料暴露)和/或血尿的患者通常 通过分析排尿进行膀胱癌筛查。尿液中的细胞成分被沉积到 玻璃片，染色，由细胞病理学家使用黄金标准检查膀胱癌的特征 PARIS系统用于尿液细胞病理学检查。4然而，PARIS系统是主观的，尿路上皮的形态 像元是高度多样化的，这使得该过程很困难，并且容易出现很高的观察者之间的变异性和人为错误 因疲劳和过度工作而承受。5，6一种更定量、更自动化的尿液细胞病理学评估方法 膀胱癌是必要的。机器学习(ML)技术已被证明在基于图像的 病理学中的分类，因为ML模型可重复且无偏差地运行(除非训练数据是 有偏见的)或疲劳。巴氏涂片已经由半自动ML系统(BD)进行常规处理 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由液体制剂(LBP，例如ThinPrep)准备的测试 方法：研究方法。然而，到目前为止，还没有开发出以这种方式利用ML治疗膀胱癌的系统，这是我打算的事实 去改变。虽然我坚信病理学作为一门学科正准备向100%的 数字化服务，有很大的惯性需要克服，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加ML病理学家的技能来提供数字替代方案 使他们能够更高效、更快速、更安全地工作的算法。尿液细胞学检查对膀胱癌的诊断价值 癌症是一个理想的用例。因此，我们试图创建一个基于ML的原型算法，称为AutoParis，它 将使巴黎系统的制表自动化。AutoParis的最初原型被证明是高度 通过将与核浆比相关的统计数据制表，有效地对尿细胞学标本进行风险分层 (NC比率，肿瘤形成的一个非常重要的指标)和细胞/核形态非典型性。 AutoParis作为对细胞病理学家的诊断辅助，还需要几个额外的步骤。虽然我很熟练 足以编写模型的第一次迭代，作为一个自学成才的人，我正在达到我所能完成的极限 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创新 关于数字病理学和ML，我需要一个更正规的教育，在专业数学，统计学，ML理论 和编程。通过这个奖项，我将在达特茅斯学院攻读一门课程，课程由 专家导师团队。我的导师和合作者也是因为他们帮助测试的能力而被选中 以及数字决策辅助工具、赠款和手稿准备以及实验室管理的验证。我会从这一切中走出来 我所需要的技能的经验，使我成为未来ML开发和临床采用的领导者 医药。

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.