Abstract:
:The objective of our study was to define a neural network for predicting recurrence and progression-free probability in patients affected by recurrent pTaG3 urothelial bladder cancer to use in everyday clinical practice. Among all patients who had undergone transurethral resection for bladder tumors, 143 were finally selected and enrolled. Four follow-ups for recurrence, progression or survival were performed at 6, 9, 12 and 108 months. The data were analyzed by using the commercially available software program NeuralWorks Predict. These data were compared with univariate and multivariate analysis results. The use of Artificial Neural Networks (ANN) in recurrent pTaG3 patients showed a sensitivity of 81.67% and specificity of 95.87% in predicting recurrence-free status after transurethral resection of bladder tumor at 12 months follow-up. Statistical and ANN analyses allowed selection of the number of lesions (multiple, HR=3.31, p=0.008) and the previous recurrence rate (>or=2/year, HR=3.14, p=0.003) as the most influential variables affecting the output decision in predicting the natural history of recurrent pTaG3 urothelial bladder cancer. ANN applications also included selection of the previous adjuvant therapy. We demonstrated the feasibility and reliability of ANN applications in everyday clinical practice, reporting a good recurrence predicting performance. The study identified a single subgroup of pTaG3 patients with multiple lesions, >or=2/year recurrence rate and without any response to previous Bacille Calmette-Guérin adjuvant therapy, that seem to be at high risk of recurrence.
journal_name
Oncol Repjournal_title
Oncology reportsauthors
Cai T,Conti G,Nesi G,Lorenzini M,Mondaini N,Bartoletti Rsubject
Has Abstractpub_date
2007-10-01 00:00:00pages
959-64issue
4eissn
1021-335Xissn
1791-2431journal_volume
18pub_type
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