ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis.
Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A(z) and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A(z) of 0.96 with a confidence interval of at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.
Gefen
Smadar
S
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.
Tretiak
Oleh J
OJ
Piccoli
Catherine W
CW
Donohue
Kevin D
KD
Petropulu
Athina P
AP
Shankar
P Mohana
PM
Dumane
Vishruta A
VA
Huang
Lexun
L
Kutay
M Alper
MA
Genis
Vladimir
V
Forsberg
Flemming
F
Reid
John M
JM
Goldberg
Barry B
BB
D000367:Age Factors; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011786:Quality Control; D012372:ROC Curve; D015203:Reproducibility of Results; D016217:Ultrasonography, Mammary / Q000379:methods*