Noninvasive bladder cancer diagnostics via machine learning analysis of nanoscale surface images of epithelial cells extracted from voided urine samples

通过机器学习分析从排泄尿液样本中提取的上皮细胞的纳米级表面图像进行非侵入性膀胱癌诊断

• 批准号: 10276838
• 负责人: Eugene Demidenko
• 金额: $ 67.46万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276838
• 关键词: Adhesions; Atomic Force Microscopy; Benign; Biochemical Genetics; Biochemical Markers; Biological Markers; Biopsy; Bladder; Body Fluids; Burning Pain; Cancer Detection; Cancer Diagnostics; Cancer Patient; Cell Extracts; Cell surface; Cells; Cervical Smears; Cessation of life; Characteristics; Clinical; Collaborations; Collection; Colorectal Cancer; Confusion; Control Groups; Cystoscopy; Cytology; Data; Detection; Diagnosis; Disease; Dysuria; Early Diagnosis; Epithelial Cells; Evaluation; Excision; Feces; Goals; Gold; Hematuria; Hospitals; Image; Individual; Infection; Judgment; Lead; Liquid substance; Machine Learning; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of urinary bladder; Mechanics; Medical Oncology; Membrane; Methods; Modality; Modeling; Monitor; Nature; Optics; Pathology; Patient Monitoring; Patient Participation; Patients; Preparation; Procedures; Property; Protocols documentation; ROC Curve; Recording of previous events; Recurrence; Reproducibility; Research; Resources; Risk; Sampling; Sampling Errors; Screening for cancer; Sputum; Statistical Data Interpretation; Statistical Methods; Subgroup; Surface; Surface Properties; Survivors; Technology; Testing; Time; Tissues; Urine; Urologic Cancer; Urology; Urothelium; Visual; Work; algorithmic methodologies; base; cancer diagnosis; cell fixation; cellular imaging; clinical implementation; cohort; compliance behavior; cost; diagnosis standard; digital; flexibility; follow-up; genetic analysis; high risk; imaging modality; improved; innovative technologies; machine learning method; method development; microscopic imaging; nanoscale; novel; physical property; programs; sample fixation; screening; screening participation; standard of care; tumor; ultra high resolution; viscoelasticity

PROJECT SUMMARY/ABSTRACT Bladder cancer is common cancer with an estimated 81,190 new cases and 17,240 deaths in 2018 (with > 500,000 survivors) only in the US. The gold standard for diagnosis of bladder cancer includes an invasive optical bladder examination (cystoscopy) and tumor resection for pathology examination. Because of a high recurrence rate of this cancer (50-80%), frequent (once every 3-6-12 months) costly and invasive cystoscopy exams are required to monitor patients for recurrence and/or progression to a more advanced stage. It makes bladder cancer the most expensive cancer to monitor/follow up and treat per patient. Moreover, the invasive nature of the current standard of care, cystoscopy, causes rather low compliance of patient to follow this procedure. There is an urgent unmet need for a bladder cancer screening and monitoring test, which will be noninvasive, rapid, objective, reproducible, easy to perform and interpret, and highly accurate. Such a test will reduce the need in frequent cystoscopies and greatly expand the participation of patients in screening and early detection programs because it decreases the patient discomfort and post-procedural complications. Here we propose to develop such a test for identification of the presence of bladder cancer and its aggressiveness (grade). It will be based on non-invasive analysis of individual cells extracted from urine (extraction technology already exists in hospitals for voided urine cytology tests, (VUC) the current standard-of- care, a non-invasive examination of cells in urine used to assist with cancer diagnosis and surveillance). A novel modality of Atomic Force Microscopy (AFM) will be used for nanoscale imaging of cells extracted from urine, mapping/imaging of the physical properties of the cell surface. The collected images will further be analyzed using machine-learning methods and novel advanced statistical approaches to identify a “digital signature” of cancer. The proposed technology is fundamentally different from previously studied urine biomarkers and all existing physical methods because it is based on the analysis of physical properties of the cell surface, not cell bulk or presence of biochemical markers or genetic analysis. Our strong preliminary results demonstrate the feasibility of the proposed approach, its presumed superiority compared to the currently used non-invasive methods, and lead us to the central hypothesis that bladder cancer can be identified by analyzing a small number of cells randomly chosen from urine samples, with a low sampling error. This is a substantial departure from VUC tests, which require a visual analysis of many cells. Supported by the preliminary data, we propose (1) to optimize and expand the method, (2) to define the accuracy of cancer detection on a large cohort of patients, and (3) to assess the accuracy of identification of aggressiveness (low versus high grade) of bladder cancer. Our long-term goal is to develop a non-invasive clinical method for accurate detecting of presence and monitoring bladder cancer as well as many other cancers, in which cells can be extracted from easily accessible bodily fluids without the need for tissue biopsy (e.g urine-bladder & upper urinary tract cancer, stool- colorectal cancer, sputum-aerodigestive cancer, cervical smears-cervical cancer etc.), using methods based on the analysis of physical characteristics of the cell surface. The proposed research, which is the first step in pursuit of this overarching goal.

项目概要/摘要 膀胱癌是一种常见癌症，2018 年估计有 81,190 例新发病例和 17,240 例死亡（其中 > 500,000 名幸存者）仅在美国诊断膀胱癌的黄金标准包括侵入性检查。 光学膀胱检查（膀胱镜检查）和肿瘤切除后进行病理学检查。 这种癌症的复发率 (50-80%)、频繁（每 3-6-12 个月一次）、昂贵且侵入性膀胱镜检查 需要进行检查来监测患者是否复发和/或进展至更晚期。 膀胱癌是每名患者监测/随访和治疗费用最高的癌症。 当前膀胱镜检查标准的性质导致患者遵循这一标准的依从性相当低 膀胱癌筛查和监测测试的迫切需求尚未得到满足，这将是 这种测试无创、快速、客观、可重复、易于执行和解释，并且高度准确。 减少频繁膀胱镜检查的需要，并大大扩大患者对筛查和检查的参与度 早期检测计划，因为它可以减少患者的不适和术后并发症。 在这里，我们建议开发这样一种测试来识别膀胱癌的存在及其 它将基于从尿液中提取的单个细胞的非侵入性分析。 （医院中已经存在用于废尿细胞学检测的提取技术，（VUC）当前的标准- 护理，对尿液中的细胞进行无创检查，用于协助癌症诊断和监测）A。 原子力显微镜 (AFM) 的新模式将用于对从细胞中提取的细胞进行纳米级成像 尿液、细胞表面物理特性的绘图/成像将进一步进行。 使用机器学习方法和新颖的高级统计方法进行分析，以确定“数字 所提出的技术与之前研究的尿液根本不同。 生物标记物和所有现有的物理方法，因为它是基于对生物标记物的物理特性的分析 细胞表面，而不是细胞体积或生化标记或遗传分析的存在。 我们强有力的初步结果证明了所提出方法的可行性，其假设 与目前使用的非侵入性方法相比具有优越性，并引导我们得出中心假设： 膀胱癌可以通过分析从尿液样本中随机选择的少量细胞来识别， 这与 VUC 测试有很大不同，VUC 测试需要对数据进行可视化分析。 在初步数据的支持下，我们建议（1）优化和扩展该方法，（2） 定义对大量患者进行癌症检测的准确性，以及 (3) 评估癌症检测的准确性 识别膀胱癌的侵袭性（低级别与高级别）。 我们的长期目标是开发一种非侵入性临床方法来准确检测是否存在和 膀胱癌监测以及许多其他癌症，可以轻松地从其中提取细胞 无需组织活检即可获取体液（例如膀胱癌和上尿路癌、粪便癌） 结直肠癌、痰-呼吸消化癌、宫颈涂片-宫颈癌等），使用基于 所提出的研究的第一步是分析细胞表面的物理特性。 追求这一总体目标。