title: New morphological features for grading pancreatic ductal adenocarcinomas.
abstract: Pathological diagnosis is influenced by subjective factors such as the individual experience and knowledge of doctors. Therefore, it may be interpreted in different ways for the same symptoms. The appearance of digital pathology has created good foundation for objective diagnoses based on quantitative feature analysis. Recently, numerous studies are being done to develop automated diagnosis based on the digital pathology. But there are as of yet no general automated methods for pathological diagnosis due to its specific nature. Therefore, specific methods according to a type of disease and a lesion could be designed. This study proposes quantitative features that are designed to diagnose pancreatic ductal adenocarcinomas. In the diagnosis of pancreatic ductal adenocarcinomas, the region of interest is a duct that consists of lumen and epithelium. Therefore, we first segment the lumen and epithelial nuclei from a tissue image. Then, we extract the specific features to diagnose the pancreatic ductal adenocarcinoma from the segmented objects. The experiment evaluated the classification performance of the SVM learned by the proposed features. The results showed an accuracy of 94.38% in the experiment distinguishing between pancreatic ductal adenocarcinomas and normal tissue and a classification accuracy of 77.03% distinguishing between the stages of pancreatic ductal adenocarcinomas.
authors.affiliation: Department of Computer & Information Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Republic of Korea.
authors.forename: Jae-Won
authors.initials: JW
authors.lastname: Song
authors.forename: Ju-Hong
authors.initials: JH
authors.lastname: Lee
mesh_terms: D019540:Area Under Curve; D021441:Carcinoma, Pancreatic Ductal / Q000175:diagnosis / Q000473:pathology*; D002454:Cell Differentiation; D002467:Cell Nucleus / Q000473:pathology; D004847:Epithelial Cells / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D060787:Neoplasm Grading; D010190:Pancreatic Neoplasms / Q000175:diagnosis / Q000473:pathology*; D012372:ROC Curve; D060388:Support Vector Machine