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A new liquid biopsy called NETest2.0 uses machine learning and a 51-gene signature to detect neuroendocrine tumors (NETs) with 92.5% accuracy. Tested in nearly 900 patients, it also reliably tracked disease progression over time. The test's developer is now seeking FDA approval.
A blood-based, AI-enhanced multigene assay called NETest2.0 is showing impressive results in detecting and monitoring neuroendocrine tumors (NETs). Presented at the 2026 NANETS Multidisciplinary NET Medical Symposium, the test measures a 51-gene neuroendocrine-specific expression signature in whole blood and generates a standardized score from 0 to 100. Researchers say it fills a critical gap — NETs are notoriously slow-growing, making early and accurate detection a persistent challenge.
The study drew on data from RegisterNET, a large multicenter observational registry. Investigators compared NETest2.0 results against imaging findings across nearly 900 patients and also tracked score changes over time in over 400 patients. The assay performed consistently regardless of tumor grade, location, or imaging type used for comparison.
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Why it matters: Reliable biomarkers for NETs have long been elusive. NETest2.0's ability to detect disease and flag progression through a simple blood draw — without being affected by tumor grade or location — could transform how clinicians monitor NET patients between imaging scans. Wren Laboratories is currently pursuing FDA approval.
