title: A demonstration that breast cancer recurrence can be predicted by neural network analysis.
abstract: Neural Network Analysis, a form of artificial intelligence, was successfully used to predict the clinical outcome of node-positive breast cancer patients. A Neural Network was trained to predict clinical outcome using prognostic information from 1008 patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as length of clinical followup, relapse status, and time of relapse. The ability of the trained Network to determine relapse probability was then validated in a separate set of 960 patients. The Neural Network was as powerful as Cox Regression Modeling in identifying breast cancer patients at high and low risk for relapse.
authors.affiliation: Department of Medicine, University of Texas Health Science Center, San Antonio 78284-7884.
authors.forename: P M
authors.initials: PM
authors.lastname: Ravdin
authors.forename: G M
authors.initials: GM
authors.lastname: Clark
authors.forename: S G
authors.initials: SG
authors.lastname: Hilsenbeck
authors.forename: M A
authors.initials: MA
authors.lastname: Owens
authors.forename: P
authors.initials: P
authors.lastname: Vendely
authors.forename: M R
authors.initials: MR
authors.lastname: Pandian
authors.forename: W L
authors.initials: WL
authors.lastname: McGuire
mesh_terms: D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000628:therapy; D003658:Decision Making, Computer-Assisted; D005500:Follow-Up Studies; D006801:Humans; D008207:Lymphatic Metastasis; D009362:Neoplasm Metastasis; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D012306:Risk; D013997:Time Factors