Wappingers Falls

This research aims to evaluate an in vitro diagnostic test designed to identify multiple early-stage cancers, including lung, ovarian, breast, pancreatic, and colorectal cancers. The test uses deep learning to analyze Raman spectroscopic profiles of extracellular vesicles (EVs) extracted from human plasma. The study focuses on detecting the presence of these cancers and determining their tissue of origin, which can include one or more sources. Participants will undergo diagnostic testing using the EXoPred device developed by EXoPERT. This device analyzes the Raman spectral data of EVs to provide cancer-related information. Blood samples are collected before any systemic or definitive cancer therapy, and the test evaluates the samples using artificial intelligence to identify cancer signals. The study duration for evaluating test performance and sample sufficiency extends up to 36 months from enrollment. Participants will provide blood samples prior to treatment, and researchers will monitor the test's accuracy and ability to detect cancer over a period of up to 36 months. The study involves collecting clinical and laboratory information, including biopsy results and other diagnostic findings. Safety and compliance are tracked through participant consent and adherence to study requirements. The outcomes measured include the sufficiency of samples for testing and the overall performance of the diagnostic device in detecting cancers during the study period.