Hepatocellular carcinoma is often difficult to diagnose in cytologic material and biopsy specimens. To demonstrate the utility of image analysis in discriminating benign and malignant hepatocytes, 42 malignant cell groups were compared with 26 benign cell groups with a wide range of nuclear morphology in hematoxylin and eosin-stained histologic sections from 42 patients with hepatocellular carcinoma. Nuclear measurements were performed with a relatively inexpensive microcomputer-based image analysis system using a highly flexible imaging software package. Twenty-two nuclear morphometric and densitometric parameters were evaluated. The best single discriminator of benign and malignant cells was the nuclear major axis. Classification of the test samples using optimized linear discriminant functions achieved the following positive predictive values (PV+) and negative predictive values (PV-) for hepatocellular carcinoma: 95.0% PV+ and 85.7% PV- for the major axis; 90.5% PV+ and 84.6% PV- for five densitometric parameters; 100% PV+ and 86.7% PV- for three morphometric parameters; and 95.5% PV+ and 100% PV- for nine combined morphometric/densitometric parameters. These results demonstrate that multivariate linear discriminant functions of nuclear features measured by image analysis can be used to classify benign and malignant hepatocytes accurately.

DNA “ploidy” histogram interpretation is one of the most important sources of variation in DNA image cytometry and is influenced by multiple technical factors such as scaling, selection of peaks, and variable classification criteria. A rule‐based expert system was developed to automate and eliminate subjectivity from this interpretative process. Ninety‐eight Feulgen stained histologic sections from patients with breast, colon, and lung cancer were measured with the CAS 200 image analysis system (Becton Dickinson, Santa Clara, CA); they included diploid (n = 42), aneuploid (n = 46), tetraploid (n = 7), and multiploid (n = 3) examples. The data was converted from listmode format into ASCII with the aid of CELLSHEET software (JVC Imaging, Elmhurst, IL). Individual microphotometric nuclear measurements were sorted to one of 64 bins based on DNA index. The 64 bins were then divided into 5 semi‐arbitrarily defined ranges: hypodiploid, diploid, aneuploid, tetraploid, and hypertetraploid. The nuclear percentages in each range were calculated with EXCEL 4.0 (Microsoft, Redmond, WA). The histograms were divided into 2 equal sets: training and testing. The data from the training set were used to develop 16 IF‐THEN rules to classify the histograms into diploid, aneuploid, or tetraploid. A macro was programmed in EXCEL to automate all these operations. The rule‐based expert system classified correctly 45/50 histograms of the training set. Two tetraploid histograms were classified as aneuploid. Three multiploid histograms were classified as tetraploid. All histograms in the testing set were correctly classified by the expert system. The potential role of rule‐based expert system technology for the objective classification of DNA “ploidy” histograms measured by image cytometry is discussed. Cytometry 30:39–46, 1997. © 1997 Wiley‐Liss, Inc.