Refereed full papers (journals, book chapters, international conferences)

2004

@article{HGV2004,
	vgclass =	{refpap},
	author =	{Karla Horsch and Maryellen L. Giger and Carl J. Vyborny
	and Luz A. Venta},
	title =	{Performance of computer-aided diagnosis in the
	interpretation of lesions on breast sonograph},
	journal =	{Academic Radiology},
	volume =	{11},
	number =	{3},
	pages =	{272--280},
	month =	{March},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/S1076-6332(03)00719-0},
	abstract =	{Rationale and objectives

	To investigate the potential usefulness of computer-aided diagnosis as
	a tool for radiologists in the characterization and classification of
	mass lesions on ultrasound.

	Materials and methods

	Previously, a computerized method for the automatic classification of
	breast lesions on ultrasound was developed. The computerized method
	includes automatic segmentation of the lesion from the ultrasound image
	background and automatic extraction of four features related to lesion
	shape, margin, texture, and posterior acoustic behavior. In this study,
	the effectiveness of the computer output as an aid to radiologists in
	their ability to distinguish between malignant and benign lesions, and
	in their patient management decisions in terms of biopsy recommendation
	are evaluated. Six expert mammographers and six radiologists in private
	practice at an institution accredited by the American Ultrasound
	Institute of Medicine participated in the study. Each observer first
	interpreted 25 training cases with feedback of biopsy results, and then
	interpreted 110 additional ultrasound cases without feedback.
	Simulating an actual clinical setting, the 110 cases were unknown to
	both the observers and the computer. During interpretation, observers
	gave their confidence that the lesion was malignant and also their
	patient management recommendation (biopsy or follow-up). The computer
	output was then displayed, and observers again gave their confidence
	that the lesion was malignant and their patient management
	recommendation. Statistical analyses included receiver operator
	characteristic analysis and Student t-test.

	Results

	For the expert mammographers and for the community radiologists, the Az
	(area under the receiver operator characteristic curve) increased from
	0.83 to 0.87 (P = .02) and from 0.80 to 0.84 (P = .04), respectively,
	when the computer aid was used in the interpretation of the ultrasound
	images. Also, the Az values for the community radiologists with aid and
	for the expert mammographers without aid are similar to the Az value
	for the computer alone (Az = 0.83).

	Conclusion

	Computer analysis of ultrasound images of breast lesions has been shown
	to improve the diagnostic accuracy of radiologists in the task of
	distinguishing between malignant and benign breast lesions and in
	recommending cases for biopsy.},
}