Unbiased stereological estimation of the number and volume of nuclei and nuclear size variability in invasive ductal breast carcinomas.
The application of design-based stereological methods for estimating nuclear features quantitatively in invasive ductal breast cancer is described. Nuclear number, size and size variability are explored in relation to the tumour grade and patient prognosis. The study includes an examination of the efficiency in estimating different nuclear volumes, and two different estimators of the nuclear size variability are contrasted. Forty-two invasive ductal breast carcinomas diagnosed and graded by two pathologists were used. Both 5-microns and 25-microns-thick sections were obtained from paraffin blocks for stereological study. More undifferentiated tumours show significantly larger nuclei than low-grade tumours. The estimates based on the disector method demonstrate a decrease in the number of tumour cell nuclei per unit volume of tissue from grades 1 to 2 and especially from grades 2 to 3. The univariate survival analysis shows a high prognostic value of the nuclear volume estimates. The study shows that an efficient sampling procedure was performed, particularly when estimating volume-weighted mean nuclear volume using the point-sampled intercepts method. This method is more efficient than estimation of the number-weighted mean nuclear volume using the selector method; however, the latter provides paired estimates of volume- and number-weighted mean nuclear volume, as well as an estimate of the coefficient of variation of nuclear volume in the number distribution of the same cells.
Artacho-Pérula
E
E
Department of Morphological Sciences (Section of Histology), School of Medicine, University of Córdoba, Spain. cm1arpee@sun630.uco.es
Roldán-Villalobos
R
R
D001943:Breast Neoplasms / Q000473:pathology* / Q000648:ultrastructure; D018270:Carcinoma, Ductal, Breast / Q000473:pathology* / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods*; D010336:Pathology / Q000379:methods; D010780:Photogrammetry / Q000379:methods*; D011379:Prognosis; D016019:Survival Analysis