OBJECTIVE: The purpose of this article is to compare the ability of digital breast tomosynthesis and full field digital mammography (FFDM) to detect and characterize calcifications. MATERIALS AND METHODS: One hundred paired examinations were performed utilizing FFDM and digital breast tomosynthesis. Twenty biopsy-proven cancers, 40 biopsy-proven benign calcifications, and 40 randomly selected negative screening studies were retrospectively reviewed by five radiologists in a crossed multireader multimodal observer performance study. Data collected included the presence of calcifications and forced BI-RADS scores. Receiver operator curve analysis using BI-RADS was performed. RESULTS: Overall calcification detection sensitivity was higher for FFDM (84% [95% CI, 79-88%]) than for digital breast tomosynthesis (75% [95% CI, 70-80%]). [corrected] In the cancer cohort, 75 (76%) of 99 interpretations identified calcification in both modes. Of those, a BI-RADS score less than or equal to 2 was rendered in three (4%) and nine (12%) cases with FFDM and digital breast tomosynthesis, respectively. In the benign cohort, 123 (62%) of 200 interpretations identified calcifications in both modes. Of those, a BI-RADS score greater than or equal to 3 was assigned in 105 (85%) and 93 (76%) cases with FFDM and digital breast tomosynthesis, respectively. There was no significant difference in the nonparametric computed area under the receiver operating characteristic curves (AUC) using the BI-RADS scores (FFDM, AUC = 0.76 and SD = 0.03; digital breast tomosynthesis, AUC = 0.72 and SD = 0.04 [p = 0.1277]). CONCLUSION: In this small data set, FFDM appears to be slightly more sensitive than digital breast tomosynthesis for the detection of calcification. However, diagnostic performance as measured by area under the curve using BI-RADS was not significantly different. With improvements in processing algorithms and display, digital breast tomosynthesis could potentially be improved for this purpose.
OBJECTIVE: The purpose of this article is to compare the ability of digital breast tomosynthesis and full field digital mammography (FFDM) to detect and characterize calcifications. MATERIALS AND METHODS: One hundred paired examinations were performed utilizing FFDM and digital breast tomosynthesis. Twenty biopsy-proven cancers, 40 biopsy-proven benign calcifications, and 40 randomly selected negative screening studies were retrospectively reviewed by five radiologists in a crossed multireader multimodal observer performance study. Data collected included the presence of calcifications and forced BI-RADS scores. Receiver operator curve analysis using BI-RADS was performed. RESULTS: Overall calcification detection sensitivity was higher for FFDM (84% [95% CI, 79-88%]) than for digital breast tomosynthesis (75% [95% CI, 70-80%]). [corrected] In the cancer cohort, 75 (76%) of 99 interpretations identified calcification in both modes. Of those, a BI-RADS score less than or equal to 2 was rendered in three (4%) and nine (12%) cases with FFDM and digital breast tomosynthesis, respectively. In the benign cohort, 123 (62%) of 200 interpretations identified calcifications in both modes. Of those, a BI-RADS score greater than or equal to 3 was assigned in 105 (85%) and 93 (76%) cases with FFDM and digital breast tomosynthesis, respectively. There was no significant difference in the nonparametric computed area under the receiver operating characteristic curves (AUC) using the BI-RADS scores (FFDM, AUC = 0.76 and SD = 0.03; digital breast tomosynthesis, AUC = 0.72 and SD = 0.04 [p = 0.1277]). CONCLUSION: In this small data set, FFDM appears to be slightly more sensitive than digital breast tomosynthesis for the detection of calcification. However, diagnostic performance as measured by area under the curve using BI-RADS was not significantly different. With improvements in processing algorithms and display, digital breast tomosynthesis could potentially be improved for this purpose.
Authors: Ravi K Samala; Heang-Ping Chan; Yao Lu; Lubomir M Hadjiiski; Jun Wei; Mark A Helvie Journal: Phys Med Biol Date: 2014-11-13 Impact factor: 3.609
Authors: Berkman Sahiner; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Jun Wei; Chuan Zhou; Yao Lu Journal: Med Phys Date: 2012-01 Impact factor: 4.071
Authors: Jun Wei; Heang-Ping Chan; Mark A Helvie; Marilyn A Roubidoux; Colleen H Neal; Yao Lu; Lubomir M Hadjiiski; Chuan Zhou Journal: Phys Med Biol Date: 2019-02-11 Impact factor: 3.609
Authors: Richard A Baheza; E Brian Welch; Daniel F Gochberg; Melinda Sanders; Sara Harvey; John C Gore; Thomas E Yankeelov Journal: Med Phys Date: 2015-03 Impact factor: 4.071
Authors: Lars J Grimm; David Y Johnson; Karen S Johnson; Jay A Baker; Mary Scott Soo; E Shelley Hwang; Sujata V Ghate Journal: Eur Radiol Date: 2016-10-17 Impact factor: 5.315