The Use of a Diagnostic Database in Clinical Oncogenetics

In addition to a relatively small number of well known hereditary cancer syndromes, hundreds of presumed or proven hereditary disorders have been observed to manifest cancer as a characteristic feature or as a possible complication. The recognition of these disorders may be of great importance for the medical management of the families involved. Specialized databases, like the Familial Cancer Database (FaCD, http://www.facd.info), may be helpful in the making of differential diagnoses and offer advantages compared with traditional textbooks and on-line literature searches. Based on our own experience and interviews with the other Dutch family cancer clinics, we expect that in similar clinics, computer-assisted differential diagnosis will be primarily used in helping to decide whether or not cancer patients and families should be referred to family cancer clinics for further study and counseling. FaCD has been developed as a tool for experts. As general practitioners and other health professionals with non-expert knowledge of cancer genetics are under increasing pressure to advise on genetic risks, it should be encouraged that other software is developed to support them in interpreting family histories of cancer

Equivalence of pathologists' and rule-based parser's annotations of Dutch pathology reports

Introduction: In the Netherlands, pathology reports are annotated using a nationwide pathology network (PALGA) thesaurus. Annotations must address topography, procedure, and diagnosis. The Pathology Report Annotation Module (PRAM) can be used to annotate the report conclusion with PALGA-compliant code series. The equivalence of these generated annotations to manual annotations is unknown. We assess the equivalence of annotations by authoring pathologists, pathologists participating in this study, and PRAM. Methods: New annotations were created for one thousand histopathology reports by the PRAM and a pathologist panel. We calculated dissimilarity of annotations using a semantic distance measure, Minimal Transition Cost (MTC). In absence of a gold standard, we compared dissimilarity scores having one common annotator. The resulting comparisons yielded a measure for the coding dissimilarity between PRAM, the pathologist panel and the authoring pathologist. To compare the comprehensiveness of the coding methods, we assessed number and length of the annotations. Results: Eight of the twelve comparisons of dissimilarity scores were significantly equivalent. Non-equivalent score pairs involved dissimilarity between the code series by the original pathologist and the panel pathologists. Coding dissimilarity was lowest for procedures, highest for diagnoses: MTC overall = 0.30, topographies = 0.22, procedures = 0.13, diagnoses = 0.33. Both number and length of annotations per report increased with report conclusion length, mostly in PRAM-annotated conclusions: conclusion length ranging from 2 to 373 words, number of annotations ranged from 1 to 10 for pathologists, 1–19 for PRAM, annotation length ranged from 3 to 43 codes for pathologists, 4–123 for PRAM. Conclusions: We measured annotation similarity among PRAM, authoring pathologists and panel pathologists. Annotating by PRAM, the panel pathologists and to a lesser extent by the authoring pathologist was equivalent. Therefore, the use of annotations by PRAM in a practical setting is justified. PRAM annotations are equivalent to study-setting annotations, and more comprehensive than routine coding. Further research on annotation quality is needed