Literature DB >> 10872034

Grading of ductal breast carcinoma by cytomorphology and image morphometry with histologic correlation.

A Tahlan1, R Nijhawan, K Joshi.   

Abstract

OBJECTIVE: To investigate the relevance of image analysis for grading breast carcinoma. STUDY
DESIGN: Twenty-five ductal breast carcinoma cases were chosen randomly from routine fine needle aspiration clinics. The results of cytomorphologic grading and image morphometry were correlated with those of histologic grading. The five image morphometric parameters studied were nuclear diameter, nuclear area, nuclear roundness, nuclear perimeter and grey level to compare with chromatin texture.
RESULTS: Cytologic grading alone had a high correlation with histologic grading. The lowest correlation was found in grade 2 tumors. When cytologic grading was supplemented with image morphometric parameters, the correlation was higher than that of cytologic grading alone.
CONCLUSION: Cytologic grading has a high correlation with histologic grading. The correlation improves further on supplementation with image morphometric parameters.

Entities:  

Mesh:

Year:  2000        PMID: 10872034

Source DB:  PubMed          Journal:  Anal Quant Cytol Histol        ISSN: 0884-6812            Impact factor:   0.302


  4 in total

1.  Correlation of Nuclear Morphometry with Clinicopathologic Parameters in Malignant Breast Aspirates.

Authors:  Shivani Kalhan; Shilpa Garg; Rahul N Satarkar; Puja Sharma; Sonia Hasija; Sonia Sharma
Journal:  South Asian J Cancer       Date:  2021-12-31

2.  Significance of nuclear morphometry in cytological aspirates of breast masses.

Authors:  Shivani Kalhan; Suparna Dubey; Sonia Sharma; Sharmila Dudani; Monika Dixit
Journal:  J Cytol       Date:  2010-01       Impact factor: 1.000

3.  Study of nuclear morphometry on cytology specimens of benign and malignant breast lesions: A study of 122 cases.

Authors:  Anamika Kashyap; Manjula Jain; Shailaja Shukla; Manoj Andley
Journal:  J Cytol       Date:  2017 Jan-Mar       Impact factor: 1.000

4.  Computational pathology to discriminate benign from malignant intraductal proliferations of the breast.

Authors:  Fei Dong; Humayun Irshad; Eun-Yeong Oh; Melinda F Lerwill; Elena F Brachtel; Nicholas C Jones; Nicholas W Knoblauch; Laleh Montaser-Kouhsari; Nicole B Johnson; Luigi K F Rao; Beverly Faulkner-Jones; David C Wilbur; Stuart J Schnitt; Andrew H Beck
Journal:  PLoS One       Date:  2014-12-09       Impact factor: 3.240

  4 in total

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