I-Corps: Noninvasive detection of bladder cancer using ringing modality of atomic force microscopy
I-Corps: Noninvasive detection of bladder cancer using ringing modality of atomic force microscopy
批准号:
2041813
负责人:
Igor Sokolov
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-01 至 2022-07-31
中文摘要
这个I-Corps项目更广泛的影响/商业潜力是开发一种非侵入性诊断测试,以检测和监测膀胱癌的存在并评估其等级。 仅在美国,就有80万名幸存者生活在膀胱癌复发率高(50 - 80%)的恐惧之下。大多数患者需要每3至6个月重复一次的办公室侵入性监测程序。这种手术产生高的终身成本、疼痛和可能的尿道创伤并发症,这导致患者依从率低(40%)。拟议的技术将为接受常规标准细胞学检查作为随访预约一部分的患者提供无缝集成到标准护理中。 我们的目标是提供一种无创、快速、可重复、高精度的测试,易于执行和解释。这个I-Corps项目是基于先进的成像模式和机器学习方法的组合的开发,以处理这些图像来检测膀胱癌。成像模式利用原子力显微镜(AFM)的所谓振铃模式,其提供从患者尿液中提取的细胞表面的高分辨率图像。通过机器学习算法处理这些图像可以分配诊断评分,这将有助于泌尿科医生/肿瘤学家监测癌症的复发和进展。该方法可以无缝地结合到现有的临床实践中,通过使用目前在细胞细胞学中检查的细胞。细胞学检测是一种非侵入性程序,在检测癌症时准确性低。初步结果表明,所提出的技术可能具有更高的准确性(94%)。这一准确性甚至超过了目前使用的实践标准,膀胱的侵入性光学检查(膀胱镜检查)。该奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
英文摘要
The broader impact/commercial potential of this I-Corps project is the development of a noninvasive diagnostic test to detect and monitor the presence of bladder cancer and to assess its grade. In the U.S. alone, there are 800,000 survivors living under the fear of a high chance of recurrence (50-80%) from bladder cancer. The majority of the patients require an in-office invasive monitoring procedure repeated every 3 to 6 months. This procedure yields high lifetime costs, pain, and possible complications from trauma to the urethra, which leads to a low patient compliance rate (40%). The proposed technology will provide seamless integration into the standard of care for patients who undergo regular standard cytology as part of their follow-up appointments. The goal is to provide a noninvasive, rapid, reproducible, high accuracy test that is easy to perform and interpret.This I-Corps project is based on the development of a combination of an advanced imaging modality and machine learning methods to process those images for the detection of bladder cancer. The imaging modality utilizes the so-called ringing mode of atomic force microscopy (AFM), which provides high-resolution images of the surface of cells extracted from a patient’s urine. Processing these images through machine learning algorithms permits the assignment of a diagnostic score, which will help urologist/oncologists to monitor recurrence and progression of the cancer. The method may seamlessly be incorporated into the existing clinical practice, by using cells currently examined in cell cytology. Cytology testing is a noninvasive procedure that suffers from low accuracy when detecting cancer. Preliminary results have shown that the proposed technology may have a much higher accuracy (94%). This accuracy exceeds even the currently used standard of practice, an invasive optical examination of the bladder (cystoscopy).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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