Results: 4

1.
Fig. 2

Fig. 2. From: A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis.

Graph shows ROC curves constructed from the output probabilities of Model-1 and Model-2, and Radiologist’s BI-RADS assessment categories. AUC = Area under the curve.

Jagpreet Chhatwal, et al. AJR Am J Roentgenol. ;192(4):1117-1127.
2.
Appendix 2 Fig. 2

Appendix 2 Fig. 2. From: A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis.

Graph shows PR curves constructed from the output probabilities of Model-1, Model-2 and Model-3, and Radiologist’s BI-RADS assessment categories.
AUC = Area under the curve.

Jagpreet Chhatwal, et al. AJR Am J Roentgenol. ;192(4):1117-1127.
3.
Appendix 2 Fig. 1

Appendix 2 Fig. 1. From: A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis.

Graph shows ROC curves constructed from the output probabilities of Model-1, Model-2 and Model-3, and Radiologist’s BI-RADS assessment categories.
AUC = Area under the curve.

Jagpreet Chhatwal, et al. AJR Am J Roentgenol. ;192(4):1117-1127.
4.
Fig. 1

Fig. 1. From: A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis.

Descriptors of National Mammography Database entered to build a logistic regression model for breast cancer prediction
*Binary variable with categories – “Present” or “Not Present”
**class1: predominantly fatty, class 2: scattered fibroglandular, class 3: heterogeneously dense, and class 4: extremely dense tissue.

Jagpreet Chhatwal, et al. AJR Am J Roentgenol. ;192(4):1117-1127.

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