BACKGROUND: Patients with non-invasive (Ta/T1) transitional cell carcinoma (TCC) of the urinary bladder are often observed without progression in the long-term follow-up period, although many of them experience recurrence of disease. It is difficult to accurately predict the disease outcome of each patient with Ta/T1 TCC using conventional prognostic criteria. In this study, we examined the usefulness of artificial neural networks (ANNs) to predict the long-term disease outcome of patients with TCC of the urinary bladder. METHODS: A retrospective, prognostic study of 90 patients with Ta/T1 TCC of the urinary bladder, diagnosed by transurethral resection of the bladder tumor between April 1981 and March 1985, and then followed up for 15 years or longer, was carried out. Data were analyzed using the Bayesian network tool of SPSS Neural Connection 2.1. The input neural data consisted of tumor stage, grade, tumor number, age, gender, tumor architecture and estimates of mean nuclear volume. The data set was randomly divided into 68 training and 22 testing examples for the prediction of disease progression and tumor recurrence within 15 years. RESULTS: During 15 years follow-up, tumor recurrence was noted in 42/90 (47%) Ta/T1 tumors. The ANN model could not predict tumor recurrence. Conversely, disease progression was noted in 17/90 (19%) Ta/T1 tumors, and, in the test set, 4/22 (18%) Ta/T1 tumors underwent disease progression. The sensitivity of the ANN model to predict progression was 100% (specificity 67%; positive predictive value 40%; negative predictive value 100%). Patients who were judged to have a favorable prognosis using ANN analysis did not progress within the 15-year follow-up period. CONCLUSION: The results of the ANN study indicate that long-term progression-free survival of patients with non-invasive TCC of the urinary bladder can be precisely predicted. A favorable prognosis using ANNs would be one of the exclusion criteria for immediate or future total cystectomy.
BACKGROUND:Patients with non-invasive (Ta/T1) transitional cell carcinoma (TCC) of the urinary bladder are often observed without progression in the long-term follow-up period, although many of them experience recurrence of disease. It is difficult to accurately predict the disease outcome of each patient with Ta/T1 TCC using conventional prognostic criteria. In this study, we examined the usefulness of artificial neural networks (ANNs) to predict the long-term disease outcome of patients with TCC of the urinary bladder. METHODS: A retrospective, prognostic study of 90 patients with Ta/T1 TCC of the urinary bladder, diagnosed by transurethral resection of the bladder tumor between April 1981 and March 1985, and then followed up for 15 years or longer, was carried out. Data were analyzed using the Bayesian network tool of SPSS Neural Connection 2.1. The input neural data consisted of tumor stage, grade, tumor number, age, gender, tumor architecture and estimates of mean nuclear volume. The data set was randomly divided into 68 training and 22 testing examples for the prediction of disease progression and tumor recurrence within 15 years. RESULTS: During 15 years follow-up, tumor recurrence was noted in 42/90 (47%) Ta/T1 tumors. The ANN model could not predict tumor recurrence. Conversely, disease progression was noted in 17/90 (19%) Ta/T1 tumors, and, in the test set, 4/22 (18%) Ta/T1 tumors underwent disease progression. The sensitivity of the ANN model to predict progression was 100% (specificity 67%; positive predictive value 40%; negative predictive value 100%). Patients who were judged to have a favorable prognosis using ANN analysis did not progress within the 15-year follow-up period. CONCLUSION: The results of the ANN study indicate that long-term progression-free survival of patients with non-invasive TCC of the urinary bladder can be precisely predicted. A favorable prognosis using ANNs would be one of the exclusion criteria for immediate or future total cystectomy.
Authors: S B Villadsen; J B Bramsen; M S Ostenfeld; E D Wiklund; N Fristrup; S Gao; T B Hansen; T I Jensen; M Borre; T F Ørntoft; L Dyrskjøt; J Kjems Journal: Br J Cancer Date: 2011-11-22 Impact factor: 7.640
Authors: Marco Bicchetti; Giuseppe Simone; Gianluca Giannarini; Rossano Girometti; Alberto Briganti; Eugenio Brunocilla; Gianpiero Cardone; Francesco De Cobelli; Caterina Gaudiano; Francesco Del Giudice; Simone Flammia; Costantino Leonardo; Martina Pecoraro; Riccardo Schiavina; Carlo Catalano; Valeria Panebianco Journal: Radiol Med Date: 2022-06-28 Impact factor: 6.313