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Image analysis and diagnostic classification of hepatocellular carcinoma using neural networks and multivariate discriminant functions. 1994

B S Erler, and L Hsu, and H M Truong, and L M Petrovic, and S S Kim, and M H Huh, and L D Ferrell, and S N Thung, and S A Geller, and A M Marchevsky
Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California.

BACKGROUND Hepatocellular carcinoma (HCC) is often difficult to diagnose in cytologic material and small tissue biopsies since histomorphologic information is minimal or absent. The potential for misdiagnosis is greatest in attempting to discriminate well-differentiated HCC from dysplastic hepatocytes in cirrhosis. We investigated the feasibility of developing artificial intelligence classification methods based on nuclear image analysis data for use as adjuncts to the morphologic diagnosis of HCC. METHODS Ninety hematoxylin-eosin stained histologic slides including 56 with well- to poorly differentiated HCC and 34 showing a morphologic continuum from normal to markedly dysplastic benign hepatocytes were assembled from four laboratories. A relatively inexpensive PC-based image analysis system was used to measure 35 nuclear morphometric and densitometric parameters of 100 nuclei in each specimen. The data were randomized into classification training and testing sets containing equal numbers of benign and HCC samples. Objective diagnostic classification criteria for HCC based on neural networks and multivariate discriminant functions (DFs) were developed for the most discriminatory subsets of morphometric, densitometric, and combined morphometric/densitometric variables as selected by stepwise discriminant analysis of training data. RESULTS Morphometric parameters provided the best results with the following testing data positive and negative predictive values (PV+ and PV-) for HCC classification: 86.2% PV+ and 81.3% PV- for a linear DF, 85.7% PV+ and 76.5% PV- for a quadratic DF and 100% PV+ and 85.0% PV- for a neural network. CONCLUSIONS Our results demonstrate that nuclear image analysis-based objective classification criteria for HCC can be developed using artificial intelligence methods and that histologic material prepared at different institutions can be reliably classified. Neural networks for HCC classification were superior to linear and quadratic DFs. Morphometric data yielded the best results compared with densitometric or combined morphometric/densitometric data.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D008113 Liver Neoplasms Tumors or cancer of the LIVER. Cancer of Liver,Hepatic Cancer,Liver Cancer,Cancer of the Liver,Cancer, Hepatocellular,Hepatic Neoplasms,Hepatocellular Cancer,Neoplasms, Hepatic,Neoplasms, Liver,Cancer, Hepatic,Cancer, Liver,Cancers, Hepatic,Cancers, Hepatocellular,Cancers, Liver,Hepatic Cancers,Hepatic Neoplasm,Hepatocellular Cancers,Liver Cancers,Liver Neoplasm,Neoplasm, Hepatic,Neoplasm, Liver
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D006528 Carcinoma, Hepatocellular A primary malignant neoplasm of epithelial liver cells. It ranges from a well-differentiated tumor with EPITHELIAL CELLS indistinguishable from normal HEPATOCYTES to a poorly differentiated neoplasm. The cells may be uniform or markedly pleomorphic, or form GIANT CELLS. Several classification schemes have been suggested. Hepatocellular Carcinoma,Hepatoma,Liver Cancer, Adult,Liver Cell Carcinoma,Liver Cell Carcinoma, Adult,Adult Liver Cancer,Adult Liver Cancers,Cancer, Adult Liver,Cancers, Adult Liver,Carcinoma, Liver Cell,Carcinomas, Hepatocellular,Carcinomas, Liver Cell,Cell Carcinoma, Liver,Cell Carcinomas, Liver,Hepatocellular Carcinomas,Hepatomas,Liver Cancers, Adult,Liver Cell Carcinomas
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015999 Multivariate Analysis A set of techniques used when variation in several variables are studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables. Analysis, Multivariate,Multivariate Analyses
D016002 Discriminant Analysis A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis. Analyses, Discriminant,Analysis, Discriminant,Discriminant Analyses
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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