Parathyroid hormone-related protein (PTHrP) is a regulatory protein hormone that has been associated with normal fetal growth and differentiation as well as fetal calcium regulation. Parathyroid hormone-related protein has been implicated in a variety of carcinomas as a major factor in the development of humoral hypercalcemia of malignancy and may also play a role as an autocrine growth factor. In a previous immunohistochemical study we found that all prostatic adenocarcinomas (CAP) express PTHrP. In the current study, we evaluated PTHrP in prostate intraepithelial neoplasia (PIN) in radical prostatectomy specimens. A validated mouse monoclonal antibody, 9H7, raised against fragment 109-141 of the carboxy-terminus of PTHrP was used for immunostaining. The results generally showed negative to weak staining of normal and hyperplastic tissue and strong staining in PIN. The staining intensity was further evaluated by computer based image analysis. The relative optical density in PIN (9.24 +/- 9.05) was significantly (P = .008) higher than that in normal gland (.00 +/- 3.6). These findings suggest that PTHrP may be involved in the pathogenesis of prostatic dysplasia, and its immunohistochemical evaluation may have diagnostic use in the evaluation of PIN.

High-resolution image analysis was employed in the analysis of round (discoid) erythrocytes from hematologically normal (AA) individuals, AA individuals with nonspecific anemia, individuals with sickle cell trait (AS), individuals with SC disease (SC), and individuals with sickle cell anemia (SS). The shape feature Form Factor (4 pi Area/Perimeter2) was used to select round cells and to exclude sickle and other abnormal cells. Textural features extracted from round cells of SS and SC patients were found to differ from those derived from cells of normal andanemic AA individuals. Two textural features, Standard Deviation of Run Length Matrix Counts and Rotation Moment of the Cooccurrence Matrix, discriminated between patients mean values from AA samples and those from SS samples. The ability of textural features to separate round cells into classes based on genotype suggests that high resolution image analysis may be an effective tool in the study and monitoring of sickle cell disease.

Sickle cell anemia is a disease for which there is currently no effective treatment. One method of evaluating clinical status is the counting of cell types based on morphology. There is a need for a rapid, reproducible method, superior to human inspection, for classification of these cells. Quantitative digital-image analysis is being applied to this need. Blood from 24 patients with sickle cell anemia (SS) and SC disease and ten hematologically normal volunteers (AA) was stressed by bubbling with nitrogen. One hundred fifty cells were analyzed from each sickle specimen, and 100 were analyzed from each nonsickle specimen. Expert observers classified each cell as normal (N), sickle (S), or other abnormal (A). Cells were analyzed with a custom, high-resolution image-analysis instrument. A total of 42 features including metric, optical density-derived, and textural features were extracted. The metric feature Form Factor (4 pi Area/Perimeter2) was selected by recursive partitioning analysis as the sole feature needed for segregating cells into the classes of N, A, and S. The agreement of automated classification (using cutpoints determined by recursive partitioning analysis) with a human expert for specimens from individuals with sickle cell anemia was 89% for N-, 73% for A-, and 92% for S-classified cells. For specimens from AA individuals, the agreement was 92% for N and 76% for A. For specimens from individuals with sickle cell anemia, rates of agreement between two human experts were compared and found to be 86% for N, 84% for A, and 80% for S. For specimens from AA individuals, the agreement was 90% for N and 87% for A.