Evaluation of clinical and histopathologic prognostic factors for survival in canine osteosarcoma of the extracranial flat and irregular bones.

Osteosarcoma is the most common bone tumor in dogs. However, current literature focuses primarily on appendicular osteosarcoma. This study examined the prognostic value of histological and clinical factors in flat and irregular bone osteosarcomas and hypothesized that clinical factors would have a significant association with survival time while histological factors would not. All osteosarcoma biopsy samples of the vertebra, rib, sternum, scapula, or pelvis were reviewed while survival information and clinical data were obtained from medical records, veterinarians, and owners. Forty-six dogs were included in the analysis of histopathological variables and 27 dogs with complete clinical data were included in the analysis of clinical variables. In the histopathologic cox regression model, there was no significant association between any histologic feature of osteosarcoma, including grade, and survival time. In the clinical cox regression model, there was a significant association between the location of the tumor and survival time as well as between the percent elevation of alkaline phosphatase (ALP) above normal and survival time. Controlling for ALP elevation, dogs with osteosarcoma located in the scapula had a significantly greater hazard for death (2.8) compared to dogs with tumors in other locations. Controlling for tumor location, every 100% increase in ALP from normal increased the hazard for death by 1.7. For canine osteosarcomas of the flat and irregular bones, histopathological features, including grade do not appear to be rigorous predictors of survival. Clinical variables such as increased ALP levels and tumor location in the scapula were associated with decreased survival times.

An expert system for teaching cytopathologic diagnosis of lung cancers.

An expert system for teaching cytopathologic skills is described. It uses Tao's cytologic criteria as graded feature parameters for comparison against a set of expert descriptors of known carcinomas. The system scores each diagnosis according to the degree of agreement along each feature axis. The system also allows the user to compare his/her description with that of the expert for any of the tumor entities. The expert knowledge base is abstracted into a numerical matrix within the system. This matrix is used for parameter comparison and score interpretation. This design is simple and compact, allowing portability and broad compatibility for small personal computers. Although testing is only at the initial stages, preliminary studies have shown that the system provides reproducible, consistent results. It is useful as a teaching tool for students, residents and staff pathologists.