Breast carcinoma, intratumour heterogeneity and histological grading, using geostatistics.
Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff-Bloom and Richardson) grades were obtained and paraffin sections stained by MIB-1 (Ki-67) and peroxidase staining. Whole two-dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB-1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies.
Sharifi-Salamatian
V
V
Laboratoire d'Analyse d'Images en Pathologie, Cellulaire, Institut Universitaire d'Hematologie, Paris, France.
de Roquancourt
A
A
Rigaut
J P
JP
D034961:Antigens, Nuclear; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000473:pathology*; D002277:Carcinoma / Q000473:pathology*; D002467:Cell Nucleus / Q000378:metabolism; D016002:Discriminant Analysis; D004847:Epithelial Cells / Q000378:metabolism; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007150:Immunohistochemistry; D019394:Ki-67 Antigen; D009687:Nuclear Proteins / Q000032:analysis; D013269:Stochastic Processes
D034961:Antigens, Nuclear; D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D009687:Nuclear Proteins