Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

• Published in: Medical physics (Lancaster) 2014-01, Vol.41 (1), p.012901-n/a
• Main Authors: Drukker, Karen, Sennett, Charlene A., Giger, Maryellen L.
• Format: Article
• Language: English
• Subjects: 3D automated breast ultrasound; Aged; Biological material, e.g. blood, urine; Haemocytometers; biomedical ultrasonics; Breast - pathology; breast cancer; breast density; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Cancer; COMPUTER CODES; computer‐aided detection; DATASETS; Diagnosis using ultrasonic, sonic or infrasonic waves; Diagnosis, Computer-Assisted - methods; Digital computing or data processing equipment or methods, specially adapted for specific applications; False Positive Reactions; Female; Humans; Image analysis; Image data processing or generation, in general; Image Interpretation, Computer-Assisted; IMAGE PROCESSING; Imaging, Three-Dimensional; MAMMARY GLANDS; mammography; Medical diagnosis with acoustics; medical image processing; Medical X‐ray imaging; Middle Aged; NEOPLASMS; PATIENTS; Radiologists; RADIOLOGY AND NUCLEAR MEDICINE; SENSITIVITY; Three dimensional image processing; Three dimensional sensing; Transducers; tumours; ULTRASONOGRAPHY; Ultrasonography, Mammary - methods; Ultrasound Physics; WOMEN
• ISSN: 0094-2405; 2473-4209; 0094-2405
• DOI: 10.1118/1.4837196

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V® ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.