Search

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.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.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.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.

Computer-based image analysis (IA) is a technology gaining importance in diagnostic pathology. Applications of IA in pathology include DNA ploidy analysis, quantitative immunohistochemistry, three-dimensional reconstruction of tissue sections, motility studies, and chromosomal analysis. Morphometry, the quantitative measurement of size, shape, and textural features of cells and tissues, is another rapidly developing area of IA in pathology. Morphometric IA allows the objective evaluation of subtle histologic and cytologic features to yield useful diagnostic and prognostic information. Research is currently underway to develop diagnostically useful applications of morphometric IA. Several image analysis workstations designed for the pathology laboratory are currently available. However, the high cost and software inflexibility of these instruments are prohibitive to many potential users limiting the practicality of IA and hindering research. We present a relatively inexpensive pathology IA workstation assembled from commercially available hardware and software components. System features and basic image processing methods are described. A variety of practical applications for the surgical pathology laboratory are illustrated, including spatial measurements of tumors, nerve and muscle biopsy evaluation, and nuclear morphometry for classification of lymphoid effusions and hepatocellular carcinoma.