Source:

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

Prof. Dr. R�diger Schulz-Wendtland
Institute of Radiology, Gynaecological Radiology, University Erlangen-Nuremberg
Universit�tsstra�e 21-23
91054 Erlangen, Germany

Data Set 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.

Class Distribution: benign: 516; malignant: 445

Attribute Information:

6 Attributes in total (1 goal field, 1 non-predictive, 4 predictive attributes)

1. BI-RADS assessment: 1 to 5 (ordinal, non-predictive!)
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, goal field!)


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

Relevant Papers:

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

Citation Request:

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

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