PURPOSE: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. MATERIALS AND METHODS: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. RESULTS: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). CONCLUSIONS: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.
PURPOSE: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. MATERIALS AND METHODS: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. RESULTS: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). CONCLUSIONS: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.
Authors: A Vlahou; P F Schellhammer; S Mendrinos; K Patel; F I Kondylis; L Gong; S Nasim; G L Wright Journal: Am J Pathol Date: 2001-04 Impact factor: 4.307
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