title: Ultrasound RF time series for classification of breast lesions.
abstract: This work reports the use of ultrasound radio frequency (RF) time series analysis as a method for ultrasound-based classification of malignant breast lesions. The RF time series method is versatile and requires only a few seconds of raw ultrasound data with no need for additional instrumentation. Using the RF time series features, and a machine learning framework, we have generated malignancy maps, from the estimated cancer likelihood, for decision support in biopsy recommendation. These maps depict the likelihood of malignancy for regions of size 1 mm(2) within the suspicious lesions. We report an area under receiver operating characteristics curve of 0.86 (95% confidence interval [CI]: 0.84%-0.90%) using support vector machines and 0.81 (95% CI: 0.78-0.85) using Random Forests classification algorithms, on 22 subjects with leave-one-subject-out cross-validation. Changing the classification method yielded consistent results which indicates the robustness of this tissue typing method. The findings of this report suggest that ultrasound RF time series, along with the developed machine learning framework, can help in differentiating malignant from benign breast lesions, subsequently reducing the number of unnecessary biopsies after mammography screening.
authors.forename: Nishant
authors.initials: N
authors.lastname: Uniyal
authors.forename: Hani
authors.initials: H
authors.lastname: Eskandari
authors.forename: Purang
authors.initials: P
authors.lastname: Abolmaesumi
authors.forename: Samira
authors.initials: S
authors.lastname: Sojoudi
authors.forename: Paula
authors.initials: P
authors.lastname: Gordon
authors.forename: Linda
authors.initials: L
authors.lastname: Warren
authors.forename: Robert N
authors.initials: RN
authors.lastname: Rohling
authors.forename: Septimiu E
authors.initials: SE
authors.lastname: Salcudean
authors.forename: Mehdi
authors.initials: M
authors.lastname: Moradi
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D011846:Radio Waves; D060388:Support Vector Machine; D016217:Ultrasonography, Mammary / Q000379:methods*