http://mlearn.ics.uci.edu/databases/mammographic-masses/

1. Title: Mammographic Mass Data

2. Sources:

   (a) Original owners of database:
        Prof. Dr. Rüdiger Schulz-Wendtland
        Institute of Radiology, Gynaecological Radiology, University Erlangen-Nuremberg
        Universitätsstraße 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

    Abstract:
        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 several 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. 
    We present two novel CAD approaches that both emphasize an intelligible decision 
    process to predict breast biopsy outcomes from BI-RADSTM findings. An intelligible 
    reasoning process is an important requirement for the acceptance of CAD systems by 
    physicians. The first approach induces a global model based on decison-tree learning. 
    The second approach is based on case-based reasoning and applies an entropic similarity 
    measure. We have evaluated the performance of both CAD approaches on two large publicly 
    available mammography reference databases using receiver operating characteristic (ROC) 
    analysis, bootstrap sampling, and the ANOVA statistical significance test. Both 
    approaches outperform the diagnosis decisions of the physicians. Hence, both systems 
    have the potential to reduce the number of unnecessary breast biopsies in clinical 
    practice. A comparison of the performance of the proposed decision tree and CBR 
    approaches with a state of the art approach based on artificial neural networks (ANN) 
    shows that the CBR approach performs slightly better than the ANN approach, which in 
    turn results in slightly better performance than the decision-tree approach. The diff-
    erences are statistically significant (p value <0.001). On 2100 masses extracted from 
    the DDSM database, the CRB approach for example resulted in an area under the ROC curve 
    of A(z)=0.89±0.01, the decision-tree approach in A(z)=0.87±0.01, and the ANN approach 
    in A(z)=0.88±0.01. ©2007 American Association of Physicists in Medicine

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 (definitely benign) to 5 (highly suggestive of malignancy) (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