Semi-automated bladder cancer screening using machine learning: clinical validation and implementation.
使用机器学习的半自动膀胱癌筛查:临床验证和实施。
基本信息
- 批准号:10349701
- 负责人:
- 金额:$ 23.71万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:AdoptionAgeAlgorithmsArsenicAtypiaAutomationAwardBeliefBenignCellsChemicalsClassificationClinicalClinical MedicineClinical ServicesCodeComputer softwareCourse ContentCustomCystoscopyCytologyCytopathologyDataData ScientistData SetDecision AidDemographic FactorsDepositionDevelopmentDiagnosisDiagnosticDisciplineDyesEducationElderlyElementsEnvironmentEvaluationFatigueFutureGlassGoldGrantHealth systemHematuriaHigh Performance ComputingHospitalsHumanHuman CharacteristicsImageInflammatoryInstitutionInterobserver VariabilityK-Series Research Career ProgramsLiquid substanceMachine LearningMalignant NeoplasmsMalignant neoplasm of urinary bladderManuscriptsMathematicsMentorsMethodsMicroscopeMicroscopicModelingModernizationMorphologyNeoplasmsNuclearOnline SystemsOutputPap smearParis, FrancePathologistPathologyPatientsPeriodicityPlayPreparationPrivatizationProcessRecurrenceReproducibilityResource-limited settingRiskRisk FactorsRunningRuralSamplingScreening for cancerServicesSlideSmokingSpecimenSquamous CellStainsStatutes and LawsSystemTechniquesTechnologyTestingTrainingTransitional Cell CarcinomaUrineUrothelial CellUrotheliumValidationWorkanalogbasecancer carecancer diagnosisclinical diagnosticscluster computingcollegecombatcomputer programdesigndiagnostic algorithmdiagnostic criteriadiagnostic platformdigitaldigital pathologyexperienceflexibilitygraphical user interfacehead-to-head comparisonhigh riskhuman errorimprovedinnovationmachine learning algorithmmachine learning modelprototyperisk stratificationroutine screeningscreeningskillsstatisticstheoriesvirtualweb appwhole slide imaging
项目摘要
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.
项目摘要/摘要:
膀胱癌是全球第七大常见恶性肿瘤,并且是所有癌症中复发率最高的
(70%).1–3 存在危险因素(吸烟、砷/化学染料暴露)和/或血尿的患者是常规的
通过分析尿液中的细胞成分来筛查膀胱癌。
载玻片,由细胞病理学家使用金标准染色和检查膀胱癌的特征
尿液细胞病理学巴黎系统。4 然而,巴黎系统是主观的,尿路上皮的形态学
细胞差异很大,使得整个过程变得困难,并且容易出现观察者间的高度变异性和人为错误
5,6 一种更定量、自动化的尿液细胞病理学评估方法
膀胱癌所需的机器学习 (ML) 技术已被证明在基于图像的方面非常有效。
病理学分类,因为 ML 模型可重复且无偏差地运行(除非训练数据是
半自动机器学习系统 (BD) 已对子宫颈抹片检查进行常规处理。
FocalPoint),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体制备(LBP,例如 ThinPrep)进行的测试
然而迄今为止,还没有开发出以这种方式利用机器学习治疗膀胱癌的系统,这是我的意图。
虽然我坚信病理学作为一门学科有望实现 100% 的转变。
数字服务,我们要克服取代当前模拟显微镜技术的巨大惯性。
必须不仅仅是通过机器学习增强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、更安全地进行膀胱尿细胞学筛查的算法。
癌症是一个理想的用例,因此我们试图创建一个基于 ML 的算法原型,称为 AutoParis。
AutoParis 的最初原型被证明是高度自动化的。
通过制表与核质比相关的统计数据,有效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/核形态异型性。7 部署
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,尽管我很熟练。
足以编写模型的第一次迭代,我已经达到了自学所能完成的极限
程序员和数据科学家,以完成我在 AutoParis 上的工作并继续在该领域进行创新。
数字病理学和 ML 的相关知识,我需要在专业数学、统计学、ML 理论方面接受更正规的教育
通过这个奖项,我将在达特茅斯学院学习由导师指导的课程。
我的导师和合作者也因其帮助测试的能力而被选中。
以及数字决策辅助的验证、拨款和稿件准备以及实验室管理。
拥有成为未来 ML 开发及其临床应用领导者所需的技能经验
药品。
项目成果
期刊论文数量(0)
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