1. Title: Mammographic Mass Data

2. Sources:

   (a) Original owners of database:
        Prof. Dr. R�diger Schulz-Wendtland
        Institute of Radiology, Gynaecological Radiology, 
        University Erlangen-Nuremberg
        Universit�tsstra�e 21-23
        91054 Erlangen, Germany
        
   (b) Donor of database:
        Matthias Elter
        Fraunhofer Institute for Integrated Circuits (IIS)
        Image Processing and Medical Engineering Department (BMT) 
        Am Wolfsmantel 33
        91058 Erlangen, Germany
        matthias.elter@iis.fraunhofer.de
        (49) 9131-7767327 
        
   (c) Date received: October 2007
 
3. Past Usage:
    M. Elter, R. Schulz-Wendtland and T. Wittenberg (2007)
    The prediction of breast cancer biopsy outcomes using two CAD approaches
    that both emphasize an intelligible decision process.
    Medical Physics 34(11), pp. 4164-4172

4. Relevant Information:
    Mammography is the most effective method for breast cancer screening
    available today. However, the low positive predictive value of breast
    biopsy resulting from mammogram interpretation leads to approximately
    70% unnecessary biopsies with benign outcomes. To reduce the high
    number of unnecessary breast biopsies, several computer-aided diagnosis
    (CAD) systems have been proposed in the last years.These systems
    help physicians in their decision to perform a breast biopsy on a
    suspicious lesion seen in a mammogram or to perform a short term follow-up
    examination instead.
    This data set can be used to predict the severity (benign or malignant)
    of a mammographic mass lesion from BI-RADS attributes and the patient's
    age.
    It contains a BI-RADS assessment, the patient's age and three BI-RADS 
    attributes together with the ground truth (the severity field) for 516 
    benign and 445 malignant masses that have been identified on full field 
    digital mammograms collected at the Institute of Radiology of the
    University Erlangen-Nuremberg between 2003 and 2006.
    Each instance has an associated BI-RADS assessment ranging from 1
    (definitely benign) to 5 (highly suggestive of malignancy) assigned in a 
    double-review process by physicians. Assuming that all cases with BI-RADS 
    assessments greater or equal a given value (varying from 1 to 5), 
    are malignant and the other cases benign, sensitivities and associated 
    specificities can be calculated. These can be an indication of how well a
    CAD system performs compared to the radiologists.

5. Number of Instances: 961

6. Number of Attributes: 6 (1 goal field, 1 non-predictive, 4 predictive attributes)

7. Attribute Information:
   1. BI-RADS assessment: 1 to 5 (ordinal)  
   2. Age: patient's age in years (integer)
   3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal)
   4. Margin: mass margin: circumscribed=1 
                           microlobulated=2 
                           obscured=3 
                           ill-defined=4 
                           spiculated=5 (nominal)
   5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal)
   6. Severity: benign=0 or malignant=1 (binominal)

8. Missing Attribute Values: Yes
    - BI-RADS assessment:    2
    - Age:                   5
    - Shape:                31
    - Margin:               48
    - Density:              76
    - Severity:              0

9. Class Distribution: benign: 516; malignant: 445