Year Published: 2007
Missing Values: Yes
|BI_RADS_assessment||integer||Definitely benign(1) to Highly suggestive of malignancy (5)|
|age||integer||patient's age in years|
|shape||integer||mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal)|
|margin||integer||mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal)|
|density||integer||mass density high=1 iso=2 low=3 fat-containing=4 (ordinal)|
|severity||integer||Predictor Class: benign=0 or malignant=1|
|BI RADS assessment||age||shape||margin||density||severity|
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
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.
UCI Machine Learning
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