alexa Prognosis of intravesical bacillus Calmette-Guerin therapy for superficial bladder cancer by immunological urinary measurements: statistically weighted syndrome analysis.
Mathematics

Mathematics

Journal of Generalized Lie Theory and Applications

Author(s): Jackson AM, Ivshina AV, Senko O, Kuznetsova A, Sundan A, , Jackson AM, Ivshina AV, Senko O, Kuznetsova A, Sundan A,

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Abstract PURPOSE: The goal of this research was to discover new biological indicators in urine which could be used for short-term prognosis of local Bacillus Calmette-Guerin (BCG) therapy outcome in patients with superficial bladder cancer. PATIENTS AND METHODS: We measured and statistically evaluated soluble immunological molecules in urine from bladder cancer patients (n = 34) receiving BCG intravesically. Urine was collected following each of 6 weekly treatments, processed and assayed. The data base included measurements of interleukin-1 (IL-2, IL-4, IL-6, IL-10, IL-12, soluble intercellular adhesion molecule-1 (sICAM-1), tumour necrosis factor-alpha (TNF alpha), soluble CD14 (sCD14), interferon-gamma (IFN gamma), GM-CSF, volume of urine and its pH. The clinical response was evaluated by urine histology and random quadrant biopsy 3 months after the start of therapy. Patients were divided into 2 groups, with good and poor therapeutic effect. The initial complete response rate was 62\% (21/34). The data base was analyzed using traditional multivariate statistical methods and a pattern recognition method which deals with combinatorial-statistical analysis (statistically weighted syndromes (SWS) method) of the gradated features. The SWS method is capable of identifying robust patterns in small "fuzzy" sets with high dimensional objects and some missing values. RESULTS: Only one parameter gave significant differences at p < 0.05, GM-CSF at instillation 6. Repeated measurement analysis of variance, backward stepwise multiple logistic regression and linear discriminant analysis failed to show any significance. However, significant differences in the structure of correlation between features in the groups with and without therapeutic effect were observed and four highly informative variables (the masses of sICAM-1, TNF alpha, sCD14 and pH) relating to 5th-6th installations were selected by SWS. These features provided accurate individual prediction of therapeutic outcome for all our patients. Cross-validation analysis and computer simulation showed the statistically significant stability of the prediction. CONCLUSION: We have selected a set of urinary variables that could be considered as a perspective combination of indicators (syndromes) of outcome of pre-operation BCG therapy of patients with superficial bladder cancer. A larger patient database will provide testing and evaluation of the biological and clinical significance of selected features. The computational syndrome-disease approach should be applicable for the solution of decision-making problems for management of cancer.
This article was published in J Urol and referenced in Journal of Generalized Lie Theory and Applications

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