Cancer cases from ACRIN digital mammographic imaging screening trial: radiologist analysis with use of a logistic regression model.
OBJECTIVE
To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results.


METHODS
This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images.


RESULTS
Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices.


CONCLUSIONS
The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.
Pisano
Etta D
ED
Dept of Radiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 4030 Bondurant Hall, Campus Box 7000, Chapel Hill, NC 27599-7515, USA. etta_pisano@med.unc.edu
Acharyya
Suddhasatta
S
Cole
Elodia B
EB
Marques
Helga S
HS
Yaffe
Martin J
MJ
Blevins
Meredith
M
Conant
Emily F
EF
Hendrick
R Edward
RE
Baum
Janet K
JK
Fajardo
Laurie L
LL
Jong
Roberta A
RA
Koomen
Marcia A
MA
Kuzmiak
Cherie M
CM
Lee
Yeonhee
Y
Pavic
Dag
D
Yoon
Sora C
SC
Padungchaichote
Wittaya
W
Gatsonis
Constantine
C
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000379:methods*; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D011856:Radiographic Image Enhancement / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
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