title: Utility of adaptive control processing for the interpretation of digital mammograms.
abstract: Background Adaptive control processing for mammography (ACM) is a novel program that automatically sets up appropriate image-processing parameters for individual mammograms (MMGs) by analyzing the focal and whole breast histogram. Purpose To investigate whether ACM improves the image contrast of digital MMGs and whether it improves radiologists' diagnostic performance in reading of MMGs. Material and Methods One hundred normal cases for image quality assessment and another 100 cases (50 normal and 50 cancers) for observer performance assessment were enrolled. All mammograms were examined with and without ACM. Five radiologists assessed the intra- and extra-mammary contrast of 100 normal MMGs, and the mean scores of the intra- and extra-mammary contrast were compared between MMGs with and without ACM in both the dense and non-dense group. They classified 100 MMGs into BI-RADS categories 1-5, and were asked to rate the images on a scale of 0 to 100 for the likelihood of the presence of category 3-5 lesions in each breast. Detectability of breast cancer, reading time, and frequency of window adjustment were compared between MMGs with and without ACM. Results ACM improved the intra-mammary contrast in both the dense and non-dense group but degraded extra-mammary contrast in the dense group. There was no significant difference in detectability of breast cancer between MMGs with and without ACM. Frequency of window adjustment without ACM was significantly higher than that with ACM. Reading time without ACM was significantly longer than that with ACM. Conclusion ACM improves the image contrast of MMGs and shortens reading time.
authors.affiliation: 1 Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Mikako
authors.initials: M
authors.lastname: Jinnouchi
authors.affiliation: 2 Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Hidetake
authors.initials: H
authors.lastname: Yabuuchi
authors.affiliation: 3 Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Kubo
authors.affiliation: 4 Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Eriko
authors.initials: E
authors.lastname: Tokunaga
authors.affiliation: 5 Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Hidetaka
authors.initials: H
authors.lastname: Yamamoto
authors.affiliation: 1 Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Honda
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Digital mammography; extra-mammary contrast; image processing; intra-mammary contrast; observer performance