Computer Program Refines Cancer Diagnosis

The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data.

Applying the algorithm to data obtained by mass spectrometer examination of blood samples, the investigators were able to identify patients with ovarian cancer with virtually 100% accuracy and those with prostate cancer with approximately 95% accuracy.

"Our algorithm, named Q5, works on the assumption that the molecular composition of the blood changes between healthy and disease states,” explained senior author Dr. Bruce Donald, professor of computer science at Dartmouth College. "The goal of our work is to develop minimally invasive diagnostic methods with high predictive accuracy, and this is a promising first step.”



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