Biomaterial Database

Atypical adenomatous hyperplasia (adenosis) of the prostate: development of a Bayesian belief network for its distinction from well-differentiated adenocarcinoma. 1996

R Montironi, and P H Bartels, and P W Hamilton, and D Thompson

Institute of Pathological Anatomy and Histopathology, University of Ancona, Italy.

The diagnosis of atypical adenomatous hyperplasia (AAH) of the prostate and its distinction from well-differentiated prostatic adenocarcinoma with small acinar pattern (PACsmac; Gleason primary grades 1 or 2) are affected by uncertainties that arise from the fact that the knowledge of AAH histopathology is expressed in descriptive linguistic terms, words, and concepts. A Bayesian belief network (BBN) was used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependencies between elements in the reasoning sequence. A shallow network was designed and developed with an open-tree topology, consisting of a root node containing two diagnostic alternatives (eg, AAH v PACsmac) and 12 first-level descendant nodes for the diagnostic features. Eight of these nodes were based on cell features, three on the type of gland lumen contents and one on the gland shape. The results obtained with prototypes of relative likelihood ratios showed that belief for the diagnostic alternatives is high and that the network can differentiate AAH from PACsmac with certainty. The features that best contributed to the highest belief were those concerning the nucleolar size, frequency, and location. In particular, after the analysis of five nucleolar features (prominent nucleoli, inconspicuous nucleoli, nucleoli with diameter greater than 2.5 micron, nucleolar margination, and nuclei with multiple nucleoli), the belief for AAH was 1.0, being already close to 1.0 when three were evaluated (the value range is 0.0 to 1.0; the closer to 1.0, the greater the belief). The contribution of the three features concerning the gland lumen contents (mucinous material, corpora amylacea, and crystalloids) was such that the final belief did not exceed 0.8. Results with the group of remaining features (eg, basal cell recognition, gland shape variation, cytoplasm appearance, and nuclear size variation) were slightly better. These features allowed a substantial accumulation of belief that was already greater than 0.9 when three were polled. However, the maximum belief value was never obtained. In conclusion, a BBN for AAH diagnosis offers a descriptive classifier that is readily implemented, and allows the use of linguistic, fuzzy variables, and the accumulation of evidence presented by diagnostic clues.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D011336	Probability	The study of chance processes or the relative frequency characterizing a chance process.	Probabilities
D011470	Prostatic Hyperplasia	Increase in constituent cells in the PROSTATE, leading to enlargement of the organ (hypertrophy) and adverse impact on the lower urinary tract function. This can be caused by increased rate of cell proliferation, reduced rate of cell death, or both.	Adenoma, Prostatic,Benign Prostatic Hyperplasia,Prostatic Adenoma,Prostatic Hyperplasia, Benign,Prostatic Hypertrophy,Prostatic Hypertrophy, Benign,Adenomas, Prostatic,Benign Prostatic Hyperplasias,Benign Prostatic Hypertrophy,Hyperplasia, Benign Prostatic,Hyperplasia, Prostatic,Hyperplasias, Benign Prostatic,Hypertrophies, Prostatic,Hypertrophy, Benign Prostatic,Hypertrophy, Prostatic,Prostatic Adenomas,Prostatic Hyperplasias, Benign,Prostatic Hypertrophies
D011471	Prostatic Neoplasms	Tumors or cancer of the PROSTATE.	Cancer of Prostate,Prostate Cancer,Cancer of the Prostate,Neoplasms, Prostate,Neoplasms, Prostatic,Prostate Neoplasms,Prostatic Cancer,Cancer, Prostate,Cancer, Prostatic,Cancers, Prostate,Cancers, Prostatic,Neoplasm, Prostate,Neoplasm, Prostatic,Prostate Cancers,Prostate Neoplasm,Prostatic Cancers,Prostatic Neoplasm
D003663	Decision Trees	A graphic device used in decision analysis, series of decision options are represented as branches (hierarchical).	Decision Tree,Tree, Decision,Trees, Decision
D003936	Diagnosis, Computer-Assisted	Application of computer programs designed to assist the physician in solving a diagnostic problem.	Computer-Assisted Diagnosis,Computer Assisted Diagnosis,Computer-Assisted Diagnoses,Diagnoses, Computer-Assisted,Diagnosis, Computer Assisted
D003937	Diagnosis, Differential	Determination of which one of two or more diseases or conditions a patient is suffering from by systematically comparing and contrasting results of diagnostic measures.	Diagnoses, Differential,Differential Diagnoses,Differential Diagnosis
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000230	Adenocarcinoma	A malignant epithelial tumor with a glandular organization.	Adenocarcinoma, Basal Cell,Adenocarcinoma, Granular Cell,Adenocarcinoma, Oxyphilic,Adenocarcinoma, Tubular,Adenoma, Malignant,Carcinoma, Cribriform,Carcinoma, Granular Cell,Carcinoma, Tubular,Adenocarcinomas,Adenocarcinomas, Basal Cell,Adenocarcinomas, Granular Cell,Adenocarcinomas, Oxyphilic,Adenocarcinomas, Tubular,Adenomas, Malignant,Basal Cell Adenocarcinoma,Basal Cell Adenocarcinomas,Carcinomas, Cribriform,Carcinomas, Granular Cell,Carcinomas, Tubular,Cribriform Carcinoma,Cribriform Carcinomas,Granular Cell Adenocarcinoma,Granular Cell Adenocarcinomas,Granular Cell Carcinoma,Granular Cell Carcinomas,Malignant Adenoma,Malignant Adenomas,Oxyphilic Adenocarcinoma,Oxyphilic Adenocarcinomas,Tubular Adenocarcinoma,Tubular Adenocarcinomas,Tubular Carcinoma,Tubular Carcinomas
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes