Treatment decisions in axillary node-negative breast cancer patients.
Treatment decisions must be made on 9000 axillary node-negative breast cancer patients each month in the United States. Which of these patients will benefit from adjuvant therapy is a major question. Valid methods are needed to distinguish those patients who are "cured" from those who will suffer a cancer recurrence. A complex network of prognostic variables enters into the treatment decision, together with a risk-versus-benefit assessment. We are using a neural-network-based form of artificial intelligence that, once "trained" with data representing an event and its outcome, can identify subsets of patients with low recurrence risks. Larger data sets are being evaluated with the hope of introducing the neural-network technique to routine clinical practice.
McGuire
W L
WL
Department of Medicine/Oncology, University of Texas Health Science Center, San Antonio 78284-7884.
Tandon
A K
AK
Allred
D C
DC
Chamness
G C
GC
Ravdin
P M
PM
Clark
G M
GM
D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology / Q000628:therapy*; D002402:Cathepsin D / Q000502:physiology; D002455:Cell Division / Q000502:physiology; D002467:Cell Nucleus / Q000473:pathology; D002985:Clinical Protocols; D005260:Female; D006360:Heat-Shock Proteins / Q000502:physiology; D006801:Humans; D008207:Lymphatic Metastasis; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology; D016571:Neural Networks, Computer; D009857:Oncogenes / Q000235:genetics; D011003:Ploidies; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis; D012307:Risk Factors
D006360:Heat-Shock Proteins; D011960:Receptors, Estrogen; D002402:Cathepsin D