Diagnostic decision support for prostate lesions

Decision support systems for morphology-based diagnosis and prognosis of prostate neoplasms: a methodological approach

Recent advances in computer and information technologies have allowed the integration of both numeric and non-numeric data, that is, descriptive, linguistic terms. This has led at 1 end of the spectrum of technology development to machine vision based on image understanding and, at the other, to decision support systems. This has had a significant impact on our capability to derive diagnostic and prognostic information from histopathological material with prostate neoplasms. Cancer 2009;115(13 suppl):3068-77. (c) 2009 American Cancer Society.

Digital imaging in pathology: theoretical and practical considerations, and applications

Digital imaging is rapidly replacing photographic prints and Kodachromes for pathology reporting and conference purposes. Advanced systems linked to computers allow greater versatility and speed of turn-around as well as lower costs, allowing the incorporation of macroscopic and microscopic pictures into routine pathology reports and publications. Digital images allow transmission to remote sites via the Internet for primary diagnosis, consultation, quality assurance and educational purposes and can be stored and disseminated in CD-ROMs. Total slide digitisation is now a reality and has the potential to replace glass slides to a large extent. There are extensive applications of digital images in education and research, allowing more objective and automated quantitation of a variety of morphological and immunohistological parameters. Three-dimensional images of gross specimens can be developed and posted on websites for interactive educational programs and preliminary reports indicate that medical vision systems are a reality and can provide for automated computer generated histopathological diagnosis and quality assurance.

Digital imaging applications in anatomic pathology

Digital imaging has progressed at a rapid rate and is likely to eventually replace chemical photography in most areas of professional and amateur digital image acquisition. In pathology, digital microscopy has implications beyond that of taking a photograph. The arguments for adopting this new medium are compelling, and given similar developments in other areas of pathology and radiologic imaging, acceptance of the digital medium should be viewed as a component of the technological evolution of the laboratory. A digital image may be stored, replicated, catalogued, employed for educational purposes, transmitted for further interpretation (telepathology), analyzed for salient features (medical vision/image analysis), or form part of a wider digital healthcare strategy. Despite advances in digital camera technology, good image acquisition still requires good microscope optics and the correct calibration of all system components, something which many neglect. The future of digital imaging in pathology is very promising and new applications in the fields of automated quantification and interpretation are likely to have profound long-term influence on the practice of anatomic pathology. This paper discusses the state of the art of digital imaging in anatomic pathology.

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.

Image processing, diagnostic information extraction and quantitative assessment in pathology

As we enter the information age we hold strong beliefs in the benefits of digital technology applied to pathology: numerical representation offers objectivity. Digital knowledge may indeed lead to significant information discovery, and, processing systems might be designed to allow a true evolution of capabilities. Questions arise whether the methodology underlying quantitative analysis provides the information that we need and whether it is appropriate for some of the problems encountered in diagnostic and prognostic histopathology. While one certainly would not dispute the value of statistical procedures, the clinical needs call for individual patient targeted prognosis.

Diagnostic distance of high grade prostatic intraepithelial neoplasia from normal prostate and adenocarcinoma

OBJECTIVE

To develop a distance measure based methodology to support the morphological evaluation of high grade prostatic intraepithelial neoplasia (PIN), a direct precursor of prostate cancer.

METHODS

Eight morphological and cellular features were analysed in 20 cases of high grade PIN found in radical prostatectomy specimens from patients with adenocarcinoma. The diagnostic distance was evaluated to measure the extent to which the feature outcomes of the individual high grade PIN cases differed from the expected outcome profile of normal prostate, low and high grade PIN, and cribriform and large acinar adenocarcinoma. The belief value for high grade PIN was evaluated with a Bayesian belief network (BBN).

RESULTS

Complete separation existed between the cumulative absolute diagnostic distances of these 20 cases from the prototype feature outcomes of high grade PIN and normal prostate the values for which were < or = 3 (range 0 to 3) and > or = 9 (range 9 to 15), respectively. The distances from low grade PIN (range 3 to 9), cribriform adenocarcinoma (range 2 to 8), and large acinar adenocarcinoma (range 5 to 10) were intermediate and showed overlap in their distribution. When taking into consideration whether the severity of feature changes was increasing or decreasing in comparison with the category prototype outcomes, the cumulative directional diagnostic distances from high grade PIN ranged from -3 to +3. Positive distance values were seen relative to low grade PIN (range +3 to +9) and relative to normal prostate (range +9 to +15). Negative values were found relative to cribriform adenocarcinoma (range -8 to +2). The distance values from large acinar adenocarcinoma ranged from -2 to +4 and partly overlapped with those from the high grade PIN category. A bivariate scattergram derived from both diagnostic distance measures showed excellent separation between the groups' distances. BBN analysis confirmed the morphology based diagnosis. The distance evaluation resulted in 18 cases whose belief value for high grade PIN ranged from 0.60 to 0.87. In the remaining two cases the results of the BBN analysis showed a belief value of 0.50 and 0.57 for low grade PIN and of 0.49 and 0.38 for high grade PIN, respectively.

CONCLUSIONS

Distance measure based methodology represents a useful diagnostic decision support tool for the accurate evaluation of high grade PIN.

Machine vision-based histometry of premalignant and malignant prostatic lesions

The implementation of knowledge-guided control of the processing and segmentation of histopathologic images of prostatic lesions has made automated analysis and interpretation possible. To establish correspondence between histopathologic concepts, terms and diagnostic criteria, and computed histometric entities, "interpretive transforms" are introduced. Scene segmentation is controlled by an expert system following a model-based reasoning process. The expert system is structured as an associative network with frames at each node, which controls a knowledge file and a large library of image processing algorithms.