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Volume 8 Supplement 1

Symposium Mammographicum 2006

Interpretation of mammographic breast microcalcification: interobserver variability between radiologists and mammographers and analysis of best mammographic predictors of histopathological outcomes


To evaluate the interobserver variability among readers, thereby assessing the performance of radiologists versus radiographers in differentiating mammographic breast microcalcification. We also analyse the best mammographic predictors of histopathological outcome.

Materials and methods

One hundred patients were randomly selected with microcalcification (MC) on their screening mammograms who underwent stereotactic core biopsy at our institution between August 2002 and August 2004. All the mammograms were retrospectively read by five readers independently. Each observer noted the various features and final analysis category for all MCs. Interobserver variabilities were calculated using Cohen's kappa statistics, Kilem Gwet's agreement coefficient 1 and the interclass agreement coefficient. The performance of radiologists and mammographers were determined using a logistic regression model. Overall best predictors of histopathology outcomes were also determined.


Interobserver agreement was moderate to good for distribution, moderate for the shape, moderate for final analysis category, poor for morphology, poor for variation in density of MC and poor for category on more MC on magnification. There is a significant difference in determining the benign nature of MC and the overall differentiation of MC between radiologists and mammographers favouring radiologists. There is no significant difference between them in determining malignant MC. The best predictors of histopathology were the morphology (P < 0.0001), distribution (P < 0.0042) and number of MCs (P < 0.013).


There is moderate interobserver variability in assessment of the final analysis category (benign vs malignant). The radiologists are significantly better than mammographers in determining the benign nature and overall assessment but not significantly better at determining definite malignancy. The morphology, distribution and number of MCs are the best predictors of histopathological outcome.

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  • Logistic Regression
  • Regression Model
  • Cancer Research
  • Logistic Regression Model
  • Good Predictor