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A Predictive Model Approach to Distinguish between the Benign and Malignant cases in Breast Cancer

Shivani Bali, Amarnath Jena, S.B. Mehta, D.K. Banwet


The objective of the study is to develop a logistic regression model to distinguish between malignant and benign breast tumors. The model helps in assigning a probability of malignancy for any patient based on several variables. One hundred and fifty seven cases were used for data analysis. 102 patients (65%) had malignant tumors and 55 (35%) had benign tumors. For the analysis the variables included are patient’s (1) age, (2) size of tumor, three pharmacokinetic parameters i.e. (3) Ktrans, (4) Ve, (5) Kep, and (6) diffusion imaging parameter ADC and (7) maximum intensity time ratio (MITR). These seven independent variables and the final histological diagnosis for each patient (the dependent variable) were used for model building. The logistic regression model developed in this paper would help in giving the probability of malignancy of any patient based on the significant variables. By using ROC analysis the study also shows that the accuracy of this model appears to be better than using variables individually.


DCE-MRI, Logistic regression, probability, malignancy, ROC

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