title: Application of neural networks to the classification of pancreatic intraductal proliferative lesions.
abstract: The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back-propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.
authors.affiliation: Chair of Pathomorphology Jagiellonian University, Kraków, Poland.
authors.forename: K
authors.initials: K
authors.lastname: Okoń
authors.forename: R
authors.initials: R
authors.lastname: Tomaszewska
authors.forename: K
authors.initials: K
authors.lastname: Nowak
authors.forename: J
authors.initials: J
authors.lastname: Stachura
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology / Q000503:physiopathology; D000465:Algorithms; D021441:Carcinoma, Pancreatic Ductal / Q000473:pathology* / Q000503:physiopathology; D002452:Cell Count; D002467:Cell Nucleus / Q000473:pathology; D003937:Diagnosis, Differential; D018450:Disease Progression; D006801:Humans; D006965:Hyperplasia / Q000473:pathology* / Q000503:physiopathology; D019044:Image Cytometry / Q000295:instrumentation / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D016571:Neural Networks, Computer; D010190:Pancreatic Neoplasms / Q000473:pathology* / Q000503:physiopathology; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D012307:Risk Factors