RATIONALE AND OBJECTIVES: The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis. MATERIALS AND METHODS: The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations. RESULTS: The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%). CONCLUSION: CAD can be used to improve radiologists' performance in breast cancer diagnosis.
RATIONALE AND OBJECTIVES: The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis. MATERIALS AND METHODS: The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations. RESULTS: The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%). CONCLUSION: CAD can be used to improve radiologists' performance in breast cancer diagnosis.
Authors: Heang-Ping Chan; Yi-Ta Wu; Berkman Sahiner; Jun Wei; Mark A Helvie; Yiheng Zhang; Richard H Moore; Daniel B Kopans; Lubomir Hadjiiski; Ted Way Journal: Med Phys Date: 2010-07 Impact factor: 4.071
Authors: Brandon D Gallas; Heang-Ping Chan; Carl J D'Orsi; Lori E Dodd; Maryellen L Giger; David Gur; Elizabeth A Krupinski; Charles E Metz; Kyle J Myers; Nancy A Obuchowski; Berkman Sahiner; Alicia Y Toledano; Margarita L Zuley Journal: Acad Radiol Date: 2012-02-03 Impact factor: 3.173