Thompson D

Analysis of the capillary architecture in the precursors of prostate cancer : Recent findings and new concepts

OBJECTIVE To report on recent findings and new concepts in the remodeling of the capillary architecture in the precursors of prostate cancer. METHODS Immunohistochemical methods have been adopted in prostate cancer and in its precursors (prostatic intra-epithelial neoplasia) to investigate capillary pattern changes-which were mainly analyzed as capillary frequency- and the degree of endothelial cell proliferation. Several features related to the capillary architecture have been considered. Manual, semiautomatic, and automatic (machine vision) types of evaluation have been used to quantify the features. RESULTS The data available indicate that: (1) Going from normal prostate through prostatic intra-epithelial neoplasia up to invasive adenocarcinoma, an increasing proportion of capillaries becomes shorter, with open lumen and undulated external contour and with greater proliferation of the endothelial cells and greater expression of type IV collagenase. The highest proportion of touching capillaries is seen in normal prostate, while the lowest is found in invasive adenocarcinoma, being intermediate in prostatic intra-epithelial neoplasia. (2) When total androgen ablation is induced, there is no proliferation of the endothelium, whereas the capillaries are reduced in frequency and represented by small vessels lined by flat endothelial cells and with an open lumen. (3) Automation in the evaluation of the capillary architecture is feasible with a machine vision system. CONCLUSIONS The progression in prostate carcinogenesis is associated with changes in the capillary architecture. There are some preliminary data indicating that total androgen ablation can inhibit the angiogenesis in precursors of prostate cancer.

Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) I, CIN II, and CIN III. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN I, CIN II, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with kappa statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control.

Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia.

Accurate morphological classification of endometrial hyperplasia is crucial as treatments vary widely between the different categories of hyperplasia and are dependent, in part, on the histological diagnosis. However, previous studies have shown considerable inter‐observer variation in the classification of endometrial hyperplasias. The aim of this study was to develop a decision support system (DSS) for the classification of endometrial hyperplasias. The system used a Bayesian belief network to distinguish proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. These diagnostic outcomes were held in the decision node. Four morphological features were selected as diagnostic clues used routinely in the discrimination of endometrial hyperplasias. These represented the evidence nodes and were linked to the decision node by conditional probability matrices. The system was designed with a computer user interface (CytoInform) where reference images for a given clue were displayed to assist the pathologist in entering evidence into the network. Reproducibility of diagnostic classification was tested on 50 cases chosen by a gynaecological pathologist. These comprised ten cases each of proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. The DSS was tested by two consultant pathologists, two junior pathologists and two medical students. Intra‐ and inter‐observer agreement was calculated following conventional histological examination of the slides on two occasions by the consultants and junior pathologists without the use of the DSS. All six participants then assessed the slides using the expert system on two occasions, enabling inter‐ and intra‐observer agreement to be calculated. Using unaided conventional diagnosis, weighted kappa values for intra‐observer agreement ranged from 0.645 to 0.901. Using the DSS, the results for the four pathologists ranged from 0.650 to 0.845. Both consultant pathologists had slightly worse weighted kappa values using the DSS, while both junior pathologists achieved slightly better values using the system. The grading of morphological features and the cumulative probability curve provided a quantitative record of the decision route for each case. This allowed a more precise comparison of individuals and identified why discordant diagnoses were made. Taking the original diagnoses of the consultant gynaecological pathologist as the ‘gold standard’, there was excellent or moderate to good inter‐observer agreement between the ‘gold standard’ and the results obtained by the four pathologists using the expert system, with weighted kappa values of 0.586–0.872. The two medical students using the expert system achieved weighted kappa values of 0.771 (excellent) and 0.560 (moderate to good) compared to the ‘gold standard’. This study illustrates the potential of expert systems in the classification of endometrial hyperplasias. Copyright