ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis. - Gefen, S.
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 Az and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an Az of 0.96 with a confidence interval of [0.93, 0.99] 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.
Keywords: ROC analysis; Acoustic features; Benign breast lesions; Biopsies; Bootstrap resampling; Breast cancer diagnosis; Clinical findings; Combined diagnostic feature; Confidence interval; Confidence intervals; Feature fusion method; Ordinal dominance theory; Paired data analyses; Partial diagnostic data; Patient age; Patient record; Prebiopsy radiological findings; Radiological findings; Receiver operating characteristic analysis; Significance level; Simple data analyses; Statistically significant improvement; Ultrasound tissue characterization classifiers