GTC reports on validation of targeted transcriptome algorithms for diagnostic use

Artificial Intelligence Ai Data

Molecular testing company Genomic Testing Cooperative (GTC) on Thursday announced that two of its artificial intelligence (AI) algorithms have been validated for use in the diagnosis and interpretation of molecular findings through genomic profiling.

The results of the validation were published on Wednesday in the American Journal of Pathology.

GTC’s RNAnalysis algorithm is used to distinguish between 45 different diagnostic classes, providing probability scores. The algorithm is complemented by a second one called TraceWork. When needed, TraceWork is used to distinguish between two diagnostic entities determined by RNAnalysis to be of similar high probability score, GTC said.

According to the study, RNAnalysis demonstrated correct first-choice diagnosis in 100% of acute lymphoblastic leukemia cases, 88% of acute myeloid leukemia cases, 85% of diffuse large B-cell lymphoma cases, 82% of colorectal cancer cases, 49% of lung cancer cases, 88% of chronic lymphocytic leukemia cases, and 72% of follicular lymphoma cases.

TraceWork distinguished between lung cancer and colorectal cancer with 97.2% sensitivity and 94.5% specificity; between Hodgkin's lymphoma and normal lymph node with 95.4% sensitivity and 100% specificity; between follicular lymphoma and diffuse large B-cell lymphoma with 95.9% sensitivity and 93.1% specificity; and between breast cancer and ovarian cancer with 100% sensitivity and 94.2% specificity.

“We believe that transcriptomic data when combined with AI provides an efficient and effective information that can replace the need for large number immunohistochemical staining and flow cytometry testing, especially when tissue samples are scant,” Dr. Maher Albitar, founder, chief medical officer, and CEO of GTC, said in a statement.

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