DEBUT Winner: QPath @NIBIBgov
DEBUT Winner: QPath  @NIBIBgov
Uploaded February 2013 | Updated September 2026, 1 week ago
QPath: A Flow-Through High-Throughput Quantitative Histology Platform

In the category of Diagnostic Devices the winning project was Q-Path, submitted by Armin Arshi, David Kuo, Robert Lee, Elizabeth Ng, and Andrew Tan from the University of California Los Angeles. The project addressed the most common form of bladder cancer, transitional cell carcinoma (TCC), which is the fourth most common and ninth most deadly form of cancer in men. The team developed a high-throughput, flow-through microfluidic platform combined with automated image analysis software, which allows for systematic screening of patients' urine samples in order to noninvasively diagnose TCC. The system provides the pathologist with a quantitative analysis of the sample and an index to differentiate between healthy, low-grade malignancy, and high-grade malignancy. The device has the potential to be applied to a broader range of bodily fluid samples, including blood and pleural fluids; hence it could play a key role in the early diagnosis of various types of cancers.

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DEBUT Winner: QPath

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