Neural network approach to detection of metastatic melanoma from chromatographic analysis of urine.
Chromatographic analysis of sera or urine is important in medicine for the evaluation of patients whose clinical status is associated with the presence of specific biochemical markers. Malignant melanoma has been a model for such studies due to the elaboration of melanin precursors and pigment as the tumor metastasizes. Computer-assisted methods for categorizing chromatographic data and clinical status are imperative due to the large number of detectable compounds and possible correlations. In addition, computer-based analysis of the data can readily extract patterns that are not obvious by visual inspection. In this paper, we present a neural network analysis of melanoma chromatographic and clinical data that categorizes subjects into normals, NED patients (No Evidence of Disease), and metastatic patients. The set of marker compounds for metastatic disease represents a significant advance over the correlations derived by visual inspection.
Cohen
M E
ME
California State University, Fresno.
Hudson
D L
DL
Banda
P W
PW
Blois
M S
MS
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000652:urine*; D002852:Chromatography, Ion Exchange; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007211:Indoles / Q000652:urine; D008433:Mathematics; D008543:Melanins / Q000652:urine; D008545:Melanoma / Q000175:diagnosis / Q000556:secondary / Q000652:urine*; D016571:Neural Networks, Computer
D014408:Biomarkers, Tumor; D007211:Indoles; D008543:Melanins; C009904:melanogen