Decision support systems for morphology‐based diagnosis and prognosis of prostate neoplasms

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. © 2009 American Cancer Society.

[1]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[2]  Isabelle Bichindaritz,et al.  Mémoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine , 2006, Artif. Intell. Medicine.

[3]  Isabelle Bichindaritz,et al.  Case-based reasoning in the health sciences: What's next? , 2006, Artif. Intell. Medicine.

[4]  Robert W Veltri,et al.  Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent , 2008, The Prostate.

[5]  P H Bartels,et al.  Prostatic intraepithelial neoplasia (PIN). Performance of bayesian belief network for diagnosis and grading , 1995, The Journal of pathology.

[6]  P. Bartels,et al.  Evaluation of prostatic intraepithelial neoplasia after treatment with a 5-alpha-reductase inhibitor (finasteride). A methodologic approach. , 1996, Analytical and quantitative cytology and histology.

[7]  P H Bartels,et al.  Case-based prediction of survival in colorectal cancer patients. , 1999, Analytical and quantitative cytology and histology.

[8]  P H Bartels,et al.  Diagnostic and prognostic decision support systems. , 1995, Pathologica.

[9]  P H Bartels,et al.  Automated reasoning system in histopathologic diagnosis and prognosis of prostate cancer and its precursors. , 1996, European urology.

[10]  P H Bartels,et al.  Clinical applications of Bayesian belief networks in pathology. , 1995, Pathologica.

[11]  Luís Bragança,et al.  A methodological approach , 2005 .

[12]  Rodolfo Montironi,et al.  Bayesian belief network for the Gleason patterns in prostatic adenocarcinoma: development of a diagnostic decision support system for educational purposes. , 2008, Analytical and quantitative cytology and histology.

[13]  Deborah B. Thompson,et al.  Inference network‐based analyses of the histopathological effects of androgen deprivation on prostate cancer , 1999, The Journal of pathology.

[14]  P H Bartels,et al.  Atypical adenomatous hyperplasia (adenosis) of the prostate: development of a Bayesian belief network for its distinction from well-differentiated adenocarcinoma. , 1996, Human pathology.

[15]  P H Bartels,et al.  Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions. , 2003, Human pathology.

[16]  R Y Ball,et al.  A study of Gleason score interpretation in different groups of UK pathologists; techniques for improving reproducibility , 2006, Histopathology.

[17]  G. McLennan,et al.  Workshop on imaging science development for cancer prevention and preemption. , 2006, Cancer biomarkers : section A of Disease markers.

[18]  S. Moss,et al.  A UK‐based investigation of inter‐ and intra‐observer reproducibility of Gleason grading of prostatic biopsies , 2006, Histopathology.

[19]  Rainer Schmidt,et al.  Case-Based Reasoning to Explain Medical Model Exceptions , 2008, MIE.

[20]  Stefan V. Pantazi,et al.  Case-based medical informatics , 2004, BMC Medical Informatics Decis. Mak..

[21]  R. Montironi,et al.  Up-date on quantitative grading systems and prognosis. , 1994, Anticancer research.

[22]  R. Mazzucchelli,et al.  Gleason grading of prostate carcinoma in needle biopsies vs. radical prostatectomy specimens. , 2005, Analytical and quantitative cytology and histology.

[23]  P. Bartels,et al.  Prostate cancer prevention: review of target populations, pathological biomarkers, and chemopreventive agents. , 1999, Journal of clinical pathology.

[24]  Antonio Lopez-Beltran,et al.  Current practice of Gleason grading of prostate carcinoma , 2006, Virchows Archiv.

[25]  D. Gleason.  Classification of prostatic carcinomas. , 1966, Cancer chemotherapy reports.

[26]  A. Partin,et al.  Significant variations in nuclear structure occur between and within Gleason grading patterns 3, 4, and 5 determined by digital image analysis , 2007, The Prostate.

[27]  Klaus Kayser,et al.  Texture- and object-related automated information analysis in histological still images of various organs. , 2008, Analytical and quantitative cytology and histology.

[28]  L. Egevad,et al.  Correlation of modified Gleason grading with pT stage of prostatic carcinoma after radical prostatectomy. , 2008, Analytical and quantitative cytology and histology.

[29]  P. Bartels,et al.  How to develop and use a Bayesian Belief Network. , 1996, Journal of clinical pathology.

[30]  P. Bartels,et al.  Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia , 2002, The Journal of pathology.

[31]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[32]  D. Linkens,et al.  Application of artificial intelligence to the management of urological cancer. , 2007, The Journal of urology.

[33]  D. Bostwick,et al.  Practical clinical application of predictive factors in prostate cancer. A review with an emphasis on quantitative methods in tissue specimens. , 1998, Analytical and quantitative cytology and histology.

[34]  P. Bartels,et al.  Androgen-deprived prostate adenocarcinoma: evaluation of treatment-related changes versus no distinctive treatment effect with a Bayesian belief network. A methodological approach. , 1996, European urology.

[35]  Peter Hufnagl,et al.  Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology) , 2008, Diagnostic pathology.

[36]  Rodolfo Montironi,et al.  Improving inter-observer agreement and certainty level in diagnosing and grading papillary urothelial neoplasms: usefulness of a Bayesian belief network. , 2002, European urology.

[37]  P H Bartels,et al.  Diagnostic decision support for prostate lesions. , 1995, Pathology, research and practice.

[38]  P H Bartels,et al.  Karyometry in the early detection and chemoprevention of intraepithelial lesions. , 2005, European journal of cancer.

[39]  Robert W Veltri,et al.  Prediction of Prostate-Specific Antigen Recurrence in Men with Long-term Follow-up Postprostatectomy Using Quantitative Nuclear Morphometry , 2008, Cancer Epidemiology Biomarkers & Prevention.

[40]  Klaus Kayser,et al.  Grid technology in tissue-based diagnosis: fundamentals and potential developments , 2006, Diagnostic pathology.

[41]  Gleason Df.  Classification of prostatic carcinomas. , 1966 .