OBJECTIVES: To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database. METHODS: The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database. RESULTS: The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups. CONCLUSION: This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.
OBJECTIVES: To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database. METHODS: The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database. RESULTS: The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups. CONCLUSION: This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.
Authors: Bin Zheng; Joseph K Leader; Gordon S Abrams; Amy H Lu; Luisa P Wallace; Glenn S Maitz; David Gur Journal: Med Phys Date: 2006-09 Impact factor: 4.071
Authors: David Gur; Jennifer S Stalder; Lara A Hardesty; Bin Zheng; Jules H Sumkin; Denise M Chough; Betty E Shindel; Howard E Rockette Journal: Radiology Date: 2004-09-09 Impact factor: 11.105
Authors: Rachel F Brem; Janet Baum; Mary Lechner; Stuart Kaplan; Stuart Souders; L Gill Naul; Jeff Hoffmeister Journal: AJR Am J Roentgenol Date: 2003-09 Impact factor: 3.959
Authors: Morteza Heidari; Seyedehnafiseh Mirniaharikandehei; Wei Liu; Alan B Hollingsworth; Hong Liu; Bin Zheng Journal: IEEE Trans Med Imaging Date: 2019-10-09 Impact factor: 10.048
Authors: Seyedehnafiseh Mirniaharikandehei; Alan B Hollingsworth; Bhavika Patel; Morteza Heidari; Hong Liu; Bin Zheng Journal: Phys Med Biol Date: 2018-05-15 Impact factor: 3.609