Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions

We present case-based reasoning computer software developed from mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The case-based reasoning system is designed to support the decision to perform biopsy in those patients who have suspicious findings on...

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Veröffentlicht in:American journal of roentgenology (1976) Jg. 175; H. 5; S. 1347
Hauptverfasser: Floyd, Jr, C E, Lo, J Y, Tourassi, G D
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.11.2000
Schlagworte:
Adult
Aged
Aged, 80 and over
Algorithms
Area Under Curve
Artificial Intelligence
Biopsy
Breast Diseases - diagnostic imaging
Breast Diseases - pathology
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - pathology
Databases as Topic
Decision Making, Computer-Assisted
Decision Support Techniques
Female
Humans
Mammography
Middle Aged
Neural Networks (Computer)
ROC Curve
Sensitivity and Specificity
Software
ISSN:0361-803X
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Zusammenfassung:We present case-based reasoning computer software developed from mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The case-based reasoning system is designed to support the decision to perform biopsy in those patients who have suspicious findings on diagnostic mammography. Currently, between 66% and 90% of biopsies are performed on benign lesions. Our system is designed to help decrease the number of benign biopsies without missing malignancies. Clinicians interpret the mammograms using a standard reporting lexicon. The case-based reasoning system compares these findings with a database of cases with known outcomes (from biopsy) and returns the fraction of similar cases that were malignant. This malignancy fraction is an intuitive response that the clinician can then consider when making the decision regarding biopsy. The system was evaluated using a round-robin sampling scheme and performed with an area under the receiver operating characteristic curve of 0.83, comparable with the performance of a neural network model. If only the cases returning a malignancy fraction of greater than a threshold of 0.10 are sent to biopsy, no malignancies would be missed, and the number of benign biopsies would be decreased by 25%. At a threshold of 0.21, 98%, of the malignancies would be biopsied, and the number of benign biopsies would be decreased by 41%. This preliminary investigation indicates that the case-based reasoning approach to computer-aided diagnosis has the potential to improve the accuracy of breast cancer diagnosis on mammography.
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ISSN:0361-803X
DOI:10.2214/ajr.175.5.1751347