OBJECTIVE: The objective of our study was to test whether the use of computer-assisted detection (CAD) improves sensitivity at no cost to specificity for the detection of breast cancer and enables more accurate assessment of fatty breast tissue compared with dense breast tissue. MATERIALS AND METHODS: We created a stratified random sample of screening mammograms weighted with difficult cases split evenly among women with fatty breast tissue and those with dense breast tissue: 114 patients were cancer-free, 114 had cancer 1 year after screening, and 113 had cancer 13-24 months after screening. In test settings 6 months apart, 19 community radiologists interpreted 341 bilateral screening mammograms with and without CAD. We compared the sensitivity and specificity using regression models adjusting for repeated measures. RESULTS: CAD assistance did not affect overall sensitivity (cancer by 1 year: 63.2% without CAD and 62.0% with CAD; cancer in 13-24 months: 33.5% without CAD and 32.3% with CAD), but its effect differed for visible masses that were marked by CAD compared with those that were not marked by CAD (hereafter referred to as "unmarked"). CAD was associated with improved sensitivity for marked visible cancers and decreased sensitivity for unmarked visible masses; the sensitivities without and with CAD, respectively, were as follows: marked cancer by 1 year, 82.7% versus 83.1%; marked cancer in 13-24 months, 44.2% versus 57.9%; unmarked cancer by 1 year, 37.4% versus 30.1%; unmarked cancer in 13-24 months, 29.7% versus 23.0% (p < 0.03 for both interactions between assistance and CAD marking for cancer by 1 year and cancer in 13-24 months). CAD marked 77% (70/91) of the visible cancers by 1 year and 67.3% (37/55) of the visible cancers in 13-24 months. CAD marked more visible calcified lesions (86%) than masses and asymmetric densities (67%) (p < 0.05). Overall specificity was 72% without and 75% with CAD (p < 0.02). CAD had a greater effect on both specificity (p < 0.02) and sensitivity (p < 0.03) among radiologists who interpret more than 50 mammograms per week. The results were the same for fatty breast tissue and dense breast tissue. CONCLUSION: In this experiment, CAD increased interpretive specificity but did not affect sensitivity because visible noncalcified lesions that went unmarked by CAD were less likely to be assessed as abnormal by radiologists. Breast density did not affect CAD's performance.
OBJECTIVE: The objective of our study was to test whether the use of computer-assisted detection (CAD) improves sensitivity at no cost to specificity for the detection of breast cancer and enables more accurate assessment of fatty breast tissue compared with dense breast tissue. MATERIALS AND METHODS: We created a stratified random sample of screening mammograms weighted with difficult cases split evenly among women with fatty breast tissue and those with dense breast tissue: 114 patients were cancer-free, 114 had cancer 1 year after screening, and 113 had cancer 13-24 months after screening. In test settings 6 months apart, 19 community radiologists interpreted 341 bilateral screening mammograms with and without CAD. We compared the sensitivity and specificity using regression models adjusting for repeated measures. RESULTS: CAD assistance did not affect overall sensitivity (cancer by 1 year: 63.2% without CAD and 62.0% with CAD; cancer in 13-24 months: 33.5% without CAD and 32.3% with CAD), but its effect differed for visible masses that were marked by CAD compared with those that were not marked by CAD (hereafter referred to as "unmarked"). CAD was associated with improved sensitivity for marked visible cancers and decreased sensitivity for unmarked visible masses; the sensitivities without and with CAD, respectively, were as follows: marked cancer by 1 year, 82.7% versus 83.1%; marked cancer in 13-24 months, 44.2% versus 57.9%; unmarked cancer by 1 year, 37.4% versus 30.1%; unmarked cancer in 13-24 months, 29.7% versus 23.0% (p < 0.03 for both interactions between assistance and CAD marking for cancer by 1 year and cancer in 13-24 months). CAD marked 77% (70/91) of the visible cancers by 1 year and 67.3% (37/55) of the visible cancers in 13-24 months. CAD marked more visible calcified lesions (86%) than masses and asymmetric densities (67%) (p < 0.05). Overall specificity was 72% without and 75% with CAD (p < 0.02). CAD had a greater effect on both specificity (p < 0.02) and sensitivity (p < 0.03) among radiologists who interpret more than 50 mammograms per week. The results were the same for fatty breast tissue and dense breast tissue. CONCLUSION: In this experiment, CAD increased interpretive specificity but did not affect sensitivity because visible noncalcified lesions that went unmarked by CAD were less likely to be assessed as abnormal by radiologists. Breast density did not affect CAD's performance.
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
Authors: Bin Zheng; Claudia Mello-Thoms; Xiao-Hui Wang; Gordon S Abrams; Jules H Sumkin; Denise M Chough; Marie A Ganott; Amy Lu; David Gur Journal: Acad Radiol Date: 2007-08 Impact factor: 3.173
Authors: Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan Journal: Med Phys Date: 2013-08 Impact factor: 4.071
Authors: Joshua J Fenton; Stephen H Taplin; Patricia A Carney; Linn Abraham; Edward A Sickles; Carl D'Orsi; Eric A Berns; Gary Cutter; R Edward Hendrick; William E Barlow; Joann G Elmore Journal: N Engl J Med Date: 2007-04-05 Impact factor: 91.245
Authors: Charlotte C Gard; Erin J Aiello Bowles; Diana L Miglioretti; Stephen H Taplin; Carolyn M Rutter Journal: Breast J Date: 2015-07-01 Impact factor: 2.431
Authors: Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin Journal: Ann Intern Med Date: 2013-04-16 Impact factor: 25.391