Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis.
The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound.
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Santosh S
SS
Venkatesh
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Benjamin J
BJ
Levenback
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Laith R
LR
Sultan
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ghizlane
G
Bouzghar
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address: sehgalc@uphs.upenn.edu.
Chandra M
CM
Sehgal
D019540:Area Under Curve; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008875:Middle Aged; D015588:Observer Variation; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*
Adaptive boosting; Artificial intelligence; Breast cancer; Breast ultrasound; Computer-aided diagnosis