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: 10454232
• 负责人: Eugene Demidenko
• 金额: $ 61.94万
• 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/10454232
• 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.

项目总结/摘要 膀胱癌是常见的癌症，2018年估计有81，190例新发病例和17，240例死亡（其中> 500，000幸存者）仅在美国。诊断膀胱癌的金标准包括侵入性检查。 光学膀胱检查（膀胱镜检查）和肿瘤切除进行病理检查。由于高 这种癌症的复发率（50-80%），频繁（每3-6-12个月一次）昂贵和侵入性膀胱镜检查 需要进行检查以监测患者的复发和/或进展到更晚期。它使 膀胱癌是每名患者监测/随访和治疗费用最高的癌症。此外，入侵 当前护理标准膀胱镜检查的性质导致患者遵循该标准的依从性相当低。 procedure.膀胱癌筛查和监测测试的迫切需求尚未得到满足， 无创、快速、客观、可重复、易于执行和解释，并且高度准确。这样的测试将 减少了频繁膀胱镜检查的需要，大大扩大了患者对筛查的参与， 早期检测计划，因为它减少了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，用于识别膀胱癌的存在及其 侵略性（等级）。它将基于对从尿液中提取的单个细胞的非侵入性分析 （提取技术已经存在于医院的尿液细胞学检测，（VUC）目前的标准， 护理，一种非侵入性的尿液细胞检查，用于协助癌症诊断和监测）。一 原子力显微镜（AFM）的新模式将用于从细胞中提取的细胞的纳米级成像。 尿液，细胞表面物理性质的映射/成像。收集的图像将进一步 使用机器学习方法和新型先进统计方法进行分析，以识别“数字” 癌症的“标志”。这项拟议中的技术与先前研究的尿液有着根本的不同 生物标志物和所有现有的物理方法，因为它是基于分析的物理性质的 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表明，所提出的方法的可行性，其假定 与目前使用的非侵入性方法相比， 膀胱癌可以通过分析从尿样中随机选择的少量细胞来鉴定， 具有低采样误差。这与VUC测试有很大的不同，VUC测试需要对 许多细胞。在初步数据的支持下，我们建议（1）优化和扩展该方法，（2） 定义癌症检测在大型患者队列中的准确性，以及（3）评估 膀胱癌侵袭性（低级别与高级别）的鉴定。 我们的长期目标是开发一种非侵入性的临床方法，用于准确检测存在和 监测膀胱癌以及许多其他癌症，其中细胞可以很容易地从 可获得的体液而不需要组织活检（例如膀胱和上尿路癌，粪便， 结肠直肠癌、直肠癌-呼吸消化道癌、宫颈涂片-宫颈癌等），使用基于 对细胞表面物理特性的分析。拟议的研究，这是第一步 为实现这一总体目标。