Mammographic Mass

Donated on 10/28/2007

Discrimination of benign and malignant mammographic masses based on BI-RADS attributes and the patient's age.

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Integer

# Instances

961

# Features

5

Dataset Information

Additional 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

Has Missing Values?

Yes

Introductory Paper

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process.

By M. Elter, R. Schulz-Wendtland, T. Wittenberg. 2007

Published in Medical Physics (Lancaster)

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
BI-RADSFeatureIntegeryes
AgeFeatureIntegerAgeyes
ShapeFeatureIntegeryes
MarginFeatureIntegeryes
DensityFeatureIntegeryes
SeverityTargetBinaryno

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Additional Variable 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

Papers Citing this Dataset

Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling

By Minghao Gu, Shiliang Sun. 2019

Published in ArXiv.

Falling Rule Lists

By Fulton Wang, Cynthia Rudin. 2014

Published in ArXiv.

Reliable Probabilistic Prediction for Medical Decision Support

By Harris Papadopoulos. 2011

Published in EANN/AIAI.

0 to 4 of 4

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4 citations
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Creators

Matthias Elter

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