title: Neural networks as a prognostic tool for patients with non-small cell carcinoma of the lung.
abstract: Patients with non-small cell carcinoma of the lung (NSCLC) have a poor prognosis (64 and 41% survival rates in Stages I and II). It is currently not possible to predict which patients with Stage I or II NSCLC will survive the disease. Sixty-seven patients with NSCLC, including 49 patients with Stage I NSCLC and 18 with Stage II disease (11 with squamous cell carcinomas, 35 with adenocarcinomas, and 21 with large cell carcinomas) were treated with lobectomy and followed for a minimum of 5 years. The tumors were studied with DNA flow cytometry and quantitative immunocytochemical studies for proliferation cell nuclear antigen, p53 protein, and MIB-1. The data were analyzed with backpropagation neural networks, univariate analysis of variance, the Kaplan-Meier survival method, and Cox proportional hazards model. The dependent variables were "free of disease" and "recurrence or dead from disease." Twenty neural network models were trained, using all cases but one, after 1883 to 2000 training cycles. At 5 years, 30 patients were free of disease and 37 were dead or had recurrence. Proliferating cell nuclear antigen was the only statistically significant prognostic factor by univariate analysis of variance and Cox proportional hazards analysis. The S phase was statistically significant by univariate analysis of variance (P <.05). All of the 20 models classified the test cases correctly. Study with backpropagation neural networks using multiple prognostic features from patients with NSCLC suggests that this technology might be useful for prediction of survival. This preliminary study must be validated with data from a larger group of patients with NSCLC before its clinical adequacy is established.
authors.affiliation: Department of Pathology, Hospital de Clinicas José de San Martin, Buenos Aires, Argentina.
authors.forename: M
authors.initials: M
authors.lastname: Bellotti
authors.forename: B
authors.initials: B
authors.lastname: Elsner
authors.forename: A
authors.initials: A
authors.lastname: Paez De Lima
authors.forename: H
authors.initials: H
authors.lastname: Esteva
authors.forename: A M
authors.initials: AM
authors.lastname: Marchevsky
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis; D034961:Antigens, Nuclear; D014408:Biomarkers, Tumor / Q000032:analysis; D018287:Carcinoma, Large Cell / Q000175:diagnosis; D002289:Carcinoma, Non-Small-Cell Lung / Q000737:chemistry / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis; D005434:Flow Cytometry; D006801:Humans; D007150:Immunohistochemistry; D019394:Ki-67 Antigen; D008175:Lung Neoplasms / Q000737:chemistry / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality; D016571:Neural Networks, Computer; D009687:Nuclear Proteins / Q000032:analysis; D011003:Ploidies; D011379:Prognosis; D018809:Proliferating Cell Nuclear Antigen / Q000032:analysis; D015996:Survival Rate; D016159:Tumor Suppressor Protein p53 / Q000032:analysis
chemical_list: D034961:Antigens, Nuclear; D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D009687:Nuclear Proteins; D018809:Proliferating Cell Nuclear Antigen; D016159:Tumor Suppressor Protein p53