Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study

• Published in: Radiology Vol. 212; no. 3; p. 817
• Main Authors: Chan, H P, Sahiner, B, Helvie, M A, Petrick, N, Roubidoux, M A, Wilson, T E, Adler, D D, Paramagul, C, Newman, J S, Sanjay-Gopal, S
• Format: Journal Article
• Language: English
• Published: United States 01.09.1999
• Subjects: Breast - pathology; Breast Diseases - diagnosis; Breast Neoplasms - diagnosis; Confidence Intervals; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Humans; Image Processing, Computer-Assisted; Mammography; Observer Variation; ROC Curve; Sensitivity and Specificity
• ISSN: 0033-8419
• DOI: 10.1148/radiology.212.3.r99au47817

To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammograms. The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings. Six radiologists assessed biopsy-proved masses with and without CAD. Two experiments, one with a single view and the other with two views, were conducted. The classification accuracy was quantified by using the area under the ROC curve, Az. For the reading of 238 images, the Az value for the computer classifier was 0.92. The radiologists' Az values ranged from 0.79 to 0.92 without CAD and improved to 0.87-0.96 with CAD. For the reading of a subset of 76 paired views, the radiologists' Az values ranged from 0.88 to 0.95 without CAD and improved to 0.93-0.97 with CAD. Improvements in the reading of the two sets of images were statistically significant (P = .022 and .007, respectively). An improved positive predictive value as a function of the false-negative fraction was predicted from the improved ROC curves. CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies.