Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence
Φόρτωση...
Ημερομηνία
Συγγραφείς
Tasoulis, D. K.
Spyridonos, P.
Pavlidis, N. G.
Plagianakos, V. P.
Ravazoula, P.
Nikiforidis, G.
Vrahatis, M. N.
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Περίληψη
Τύπος
Είδος δημοσίευσης σε συνέδριο
Είδος περιοδικού
peer-reviewed
Είδος εκπαιδευτικού υλικού
Όνομα συνεδρίου
Όνομα περιοδικού
Artif Intell Med
Όνομα βιβλίου
Σειρά βιβλίου
Έκδοση βιβλίου
Συμπληρωματικός/δευτερεύων τίτλος
Περιγραφή
OBJECTIVE: The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed. MATERIAL AND METHODS: A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets. RESULTS: UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm. CONCLUSIONS: FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.
Περιγραφή
Λέξεις-κλειδιά
Algorithms, Cell Nucleus/*pathology, Fuzzy Logic, Humans, *Models, Biological, Neoplasm Recurrence, Local/*diagnosis/pathology/therapy, Neoplasm Staging, Prognosis, Urinary Bladder Neoplasms/classification/*diagnosis/*pathology/therapy
Θεματική κατηγορία
Παραπομπή
Σύνδεσμος
http://www.ncbi.nlm.nih.gov/pubmed/17008071
http://ac.els-cdn.com/S0933365706001102/1-s2.0-S0933365706001102-main.pdf?_tid=f5f2553eecc4d25e2cd02cc843f46af1&acdnat=1333614085_d41b9367541f7348f16eee6d2d0bb0ac
http://ac.els-cdn.com/S0933365706001102/1-s2.0-S0933365706001102-main.pdf?_tid=f5f2553eecc4d25e2cd02cc843f46af1&acdnat=1333614085_d41b9367541f7348f16eee6d2d0bb0ac
Γλώσσα
en
Εκδίδον τμήμα/τομέας
Όνομα επιβλέποντος
Εξεταστική επιτροπή
Γενική Περιγραφή / Σχόλια
Ίδρυμα και Σχολή/Τμήμα του υποβάλλοντος
Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής