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: 10669124
• 负责人: Eugene Demidenko
• 金额: $ 61.29万
• 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/10669124
• 关键词: Adhesions; Atomic Force Microscopy; Benign; Biochemical Genetics; Biochemical Markers; Biological Markers; Biopsy; Bladder; Body Fluids; Cancer Detection; Cancer Diagnostics; Cancer Patient; Cell Extracts; Cell surface; Cells; Cervical Smears; Cessation of life; Characteristics; Clinical; Collaborations; Collecting Cell; Collection; Colorectal Cancer; Confusion; Control Groups; Cystoscopy; Cytology; Data; Detection; Diagnosis; Disease; Dysuria; Early Diagnosis; Epithelial Cells; Evaluation; Excision; Feces; Goals; Hematuria; Hospitals; Image; Individual; Infection; Judgment; Machine Learning; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of urinary bladder; Maps; Mechanics; Medical Oncology; Membrane; Methods; Modality; Modeling; Monitor; Nature; Optics; Pain; Pathology; Patient Monitoring; Patient Participation; Patients; Preparation; Procedures; Property; Protocols documentation; ROC Curve; Random Allocation; 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; cancer diagnosis; cell fixation; cell fixing; 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.

项目摘要/摘要 膀胱癌是常见的癌症，2018年估计有81,190例新病例和17,240例死亡（有> 仅在美国500,000个冲浪者）。膀胱癌诊断的黄金标准包括侵入性 光学膀胱检查（膀胱镜检查）和肿瘤切除术进行病理检查。因为很高 这种癌症的复发率（50-80％），经常（每3-6-12个月一次）昂贵和侵入性膀胱镜检查 需要检查以监测患者的复发和/或进展到更高级的阶段。它做成 膀胱癌是最昂贵的癌症，监测/随访和治疗每名患者。而且，侵入性 当前护理标准，膀胱镜检查的性质导致患者的依从性相当低。 程序。紧急未满足膀胱癌筛查和监测测试的需求，这将是 无创，快速，客观，可重现，易于执行和解释，并且高度准确。这样的测试将 减少经常膀胱镜的需求，并大大扩展患者参与筛查和 早期检测程序是因为它减少了患者的不适和后手术并发症。 在这里，我们建议开发这样的测试，以鉴定膀胱癌的存在及其的存在 侵略性（等级）。它将基于对从尿液中提取的单个细胞的无创分析 （提取技术已经存在于用于尿液细胞学测试的医院中，（VUC）当前的标准标准 护理，对用于癌症诊断和监测的尿液中细胞的无创检查）。 原子力显微镜（AFM）的新型模态将用于从从中提取的细胞的纳米级成像 尿液，映射/成像细胞表面的物理特性。收集的图像将进一步 使用机器学习方法和新型的高级统计方法分析，以识别“数字 癌症的签名。拟议的技术从根本上不同于先前研究的尿液 生物标志物和所有现有物理方法是基于对物理特性的分析 细胞表面，而不是细胞大量或生化标记或遗传分析的存在。 我们强大的初步结果证明了拟议方法的可行性，其假定是 与当前使用的非侵入性方法相比 可以通过分析从尿液样本中随机选择的少数细胞来鉴定膀胱癌， 采样误差较低。这是与VUC测试的实质性不同，它需要对 许多细胞。在初步数据的支持下，我们建议（1）优化和扩展方法，（2） 定义大量患者癌症检测的准确性，（3）评估的准确性 鉴定膀胱癌的侵略性（低级别）。 我们的长期目标是开发一种非侵入性临床方法，以准确检测存在和 监测膀胱癌以及许多其他癌症，可以轻松地从中提取细胞 无需进行组织活检的无障碍身体液（例如尿液和上尿路癌，凳子 - 结直肠癌，痰液 - 大小消化性癌，宫颈涂片宫颈癌等），使用基于方法的 关于细胞表面的物理特征的分析。拟议的研究，这是第一步 追求这个总体目标。

项目成果

Acceleration of imaging in atomic force microscopy working in sub-resonance tapping mode.

在亚共振敲击模式下工作的原子力显微镜成像加速。

• DOI: 10.1063/5.0089806
• 发表时间: 2022
• 期刊: The Review of scientific instruments
• 作者: Echols-Jones,Piers;Messner,William;Sokolov,Igor
• 通讯作者: Sokolov,Igor

Mechanical Way To Study Molecular Structure of Pericellular Layer.

• DOI: 10.1021/acsami.3c06341
• 发表时间: 2023-08-02
• 期刊: ACS APPLIED MATERIALS & INTERFACES
• 影响因子: 9.5
• 作者: Makarova, Nadezda;Lekka, Malgorzata;Gnanachandran, Kajangi;Sokolov, Igor
• 通讯作者: Sokolov, Igor

Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps.

• DOI: 10.3390/cells12212536
• 发表时间: 2023-10-28
• 期刊: Cells
• 影响因子: 6

One-Sided Multidimensional Statistical Significance Testing: A New Method of Calculating the Statistical Significance of Spectra Used to Demonstrate Magnetic Nanoparticle Sensitivity.

单侧多维统计显着性测试：一种计算用于证明磁性纳米粒子敏感性的光谱统计显着性的新方法。

• DOI: 10.1088/1361-6463/ac7012
• 发表时间: 2022
• 期刊: Journal of physics D: Applied physics
• 作者: Weaver,JohnB;Weaver,ClaireV;Ness,DylanB;Gordon-Wylie,ScottW;Demidenko,Eugene
• 通讯作者: Demidenko,Eugene