MULTICLASS PATTERN RECOGNITION OF THE GLEASON SCORE OF PROSTATIC CARCINOMAS USING METHODS OF SPATIAL STATISTICS
DOI:
https://doi.org/10.5566/ias.v32.p155-165Keywords:
classification, pattern recognition, prostate cancer, regression, spatial statisticsAbstract
The Gleason score of a prostatic carcinoma is generally considered as one of the most important prognostic parameters of this tumour type. In the present study, it was attempted to study the relation between the Gleason score and objective data of spatial statistics, and to predict this score from such data. For this purpose, 25 T1 incidental prostatic carcinomas, 50 pT2N0, and 28 pT3N0 prostatic adenocarcinomas were characterized by a histological texture analysis based on principles of spatial statistics. On sectional images, progression from low grade to high grade prostatic cancer in terms of the Gleason score is correlated with complex changes of the epithelial cells and their lumina with respect to their area, boundary length and Euler number per unit area. The central finding was a highly significant negative correlation between the Gleason score and the Euler number of the epithelial cell phase per unit area. The Gleason score of all individual cases was predicted from the spatial statistical variables by multivariate linear regression. This approach means to perform a multiclass pattern recognition, as opposed to the usual problem of binary pattern recognition. A prediction was considered as acceptable when its deviation from the human classification was no more than 1 point. This was achieved in 79 of these 103 cases when only the Euler number density was used as predictor variable. The accuracy could be risen slightly to 84 of the 103 cases, when 7 input variables were used for prediction of the Gleason score, which means an accuracy of 81.5%.References
Amin MB, Grignon DJ, Humphrey PA, Srigley, JR (2004)
Gleason grading of prostatic cancer. A contemporary approach. Philadelphia: Willincott.
B"ocking A, Kiehn J, Heinzel-Wach M (1982) Combined
histologic grading of prostatic carcinoma. Cancer 50, 288--294.
Bostwick DG (1994) Grading Prostate Cancer. Am J Clin Pathol 102(Suppl 1), 38--56.
Burges JC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2, 121--167.
Eble JN, Sauter G, Epstein JI, Sesterhenn IA (2004)
Pathology and genetics of tumours of the urinary system and male genital organs. Lyon: IARC Press.
Gleason DF (1966) Classification of prostatic carcinoma. Cancer Che-mother Rep 50, 125--128.
Gleason DF (1992) Histologic grading of prostate cancer: a perspective. Hum Pathol 23, 273--279.
Howard CV, Reed MG (2005) Unbiased Stereology
Three-Dimensional Measurement in Microscopy. 2nd Edition. Bios Scientific Publishers, Oxford.
http://217.8.156.155/norcyt/prostata/PROST.htm: Berner A, Busch C, Halvorsen OJ, Haugen OA, Scott H, Sund S, Svindland A: Web-training set for Gleason grading. The Norwegian study group for Prostate Cancer (NUCG).
Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Chichester: Wiley.
Kohonen T, Hynninen J, Kangas J, Laaksonen J, Torkkola
K (1996) LVQ_PAK: The learning vector quantization program package. Technical Report A30, Helsinki University of Technology, Laboratory of
Computer and Information Science, Otaniemi, Finland.
Mattfeldt T (2003) Classification of binary spatial textures using stochastic geometry, nonlinear deterministic analysis and artificial neural networks. Int J Pattern Recogn Artif Intell 17, 275--300.
Mattfeldt T, Kestler HA, Hautmann R, Gottfried HW (1999)
Prediction of prostatic cancer progression after radical prostatectomy using artificial neural networks: a feasibility study. BJU Int 84, 316--323.
Mattfeldt T, Gottfried H-W, Schmidt V, Kestler HA (2000)
Classification of spatial textures in benign and cancerous glandular tissues by stereology and stochastic geometry using artificial neural networks. J Microsc 198, 143--158.
Mattfeldt T, Kestler HA, Hautmann R, Gottfried H-W (2001) Systematic biopsy-based staging of prostatic carcinoma using artificial neural networks. European Urology 39: 530--537.
Mattfeldt T, Gottfried H-W, Wolter H, Schmidt V, Kestler
HA, Mayer J (2003) Classification of prostatic carcinoma with artificial neural networks using comparative genomic
hybridization and quantitative stereological data. Pathol Res Pract 199, 773--784.
Mattfeldt T, Eckel S, Fleischer F, Schmidt V (2007a)
Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss hard-core processes. J Microsc 228, 272--281.
Mattfeldt T, Meschenmoser D, Pantle U, Schmidt V (2007b) Characterization of mammary gland tissue using joint estimators of Minkowski functionals. Image Anal Stereol 26, 13--22.
Mayer J, Schmidt V, Schweiggert F (2004) A unified simulation framework for spatial stochastic models. Simul. Modelling Pract Theory 12, 307--326.
Mostofi FK (1975) Grading of prostatic carcinoma. Cancer
Chemotherapy Rep 59, 111--117.
Ohser J, Muecklich F (2000) Statistical Analysis of
Microstructures in Materials Science. Chichester: Wiley.
Saunders R, Stitson MO, Weston J, Bottou L, Sch"olkopf B, Smola A (1998) Support vector machine reference manual. Technical Report. Royal Holloway, University of London.
Sobin LH, Gospodarowicz MK, Wittekind C (2009) (Eds.) TNM classification of malignant tumours. Wiley: New York.
Stoyan D, Kendall WS, Mecke J (1995) Stochastic Geometry and Its Applications 2nd Ed. Wiley: Chichester.
Tourassi GD, Floyd CE (1997) The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Med Decis Making 17, 186--192.
Vapnik VN (1998) Statistical Learning Theory. Wiley: New York.
Weibel ER (1979) Stereological Methods. I. Practical Methods for Biological Morphometry. London: Academic Press.
Weibel ER (1980) Stereological Methods. II. Theoretical
Foundations. London: Academic Press.
Wittke C, Mayer J, Schweiggert F (2007) On the classification of prostate carcinoma with methods from spatial statistics. IEEE Trans Inform Technol Biomed 11, 406--414.
