Comparison of full-field digital mammography and screen-film mammography for detection and characterization of simulated small masses

Fully convolutional network for automated detection and diagnosis of mammographic masses

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets- INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.

Use of Diagnostic Imaging Modalities in Modern Screening, Diagnostics and Management of Breast Tumours 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

Breast radiologists and nuclear medicine specialists updated their previous recommendation/guidance at the 4th Hungarian Breast Cancer Consensus Conference in Kecskemét. A recommendation is hereby made that breast tumours should be screened, diagnosed and treated according to these guidelines. These professional guidelines include the latest technical developments and research findings, including the role of imaging methods in therapy and follow-up. It includes details on domestic development proposals and also addresses related areas (forensic medicine, media, regulations, reimbursement). The entire material has been agreed with the related medical disciplines.

Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme

OBJECTIVES

To investigate if direct radiography (DR) performs better than screen-film mammography (SF) and computed radiography (CR) in dense breasts in a decentralized organised Breast Cancer Screening Programme. To this end, screen-detected versus interval cancers were studied in different BI-RADS density classes for these imaging modalities.

METHODS

The study cohort consisted of 351,532 women who participated in the Flemish Breast Cancer Screening Programme in 2009 and 2010. Information on screen-detected and interval cancers, breast density scores of radiologist second readers, and imaging modality was obtained by linkage of the databases of the Centre of Cancer Detection and the Belgian Cancer Registry.

RESULTS

Overall, 67% of occurring breast cancers are screen detected and 33% are interval cancers, with DR performing better than SF and CR. The interval cancer rate increases gradually with breast density, regardless of modality. In the high-density class, the interval cancer rate exceeds the cancer detection rate for SF and CR, but not for DR.

CONCLUSIONS

DR is superior to SF and CR with respect to cancer detection rates for high-density breasts. To reduce the high interval cancer rate in dense breasts, use of an additional imaging technique in screening can be taken into consideration.

KEY POINTS

• Interval cancer rate increases gradually with breast density, regardless of modality. • Cancer detection rate in high-density breasts is superior in DR. • IC rate exceeds CDR for SF and CR in high-density breasts. • DR performs better in high-density breasts for third readings and false-positives.

What effect does mammographic breast density have on lesion detection in digital mammography?

Effective detection of breast cancer using mammography is an important public health issue worldwide. Breasts that contain higher levels of fibroglandular compared with fatty tissue increase breast radio-opacity making it more difficult to differentiate between normal and abnormal findings. The higher prevalence of breast cancer amongst women with denser breasts demands the origination of effective solutions to manage this common radiographic appearance. This brief review considers the impact of higher levels of density on cancer detection and the importance of digital technology in possibly reducing the negative effects of increased density.

A comparison study of image features between FFDM and film mammogram images

PURPOSE

This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system.

METHODS

The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both).

RESULTS

For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference.

CONCLUSIONS

The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of training CAD algorithms, and a single CAD algorithm may be applied to either type of images.

Breast cancer: missed interval and screening-detected cancer at full-field digital mammography and screen-film mammography-- results from a retrospective review

PURPOSE

To compare the percentages and mammographic features of cancers missed at full-field digital mammography (FFDM) and screen-film mammography (SFM) in women who participated in the Norwegian Breast Cancer Screening Program in 2002-2008.

MATERIALS AND METHODS

Social Science Data Services approval was obtained; the requirement for informed consent was waived. Cases were all the interval and screening-detected cancers from 35 127 FFDM and 52 444 SFM examinations in two Norwegian counties. Prior and diagnostic FFDM examinations of 49 interval and 86 screening-detected breast cancers were reviewed by four breast radiologists and compared with a review of SFM examinations of 81 interval and 123 screening-detected cancers. Cancers were classified as missed or true, mammographic features were described, percentages were compared by using the χ(2) or Fisher exact test, and 95% confidence intervals (CIs) were calculated.

RESULTS

The percentages of interval and screening-detected cancers missed at FFDM and SFM did not differ significantly. (interval cancers missed: 33% [16 of 49] at FFDM vs 30% [24 of 81] at SFM [P = .868]; screening-detected cancers missed: 20% [17 of 86] at FFDM vs 21% [26 of 123] at SFM [P = .946]). Asymmetry was present in 27% (95% CI: 13.3%, 45.5%) of prior mammograms of cancers missed at FFDM and 10% (95% CI: 3.3%, 21.8%) of those missed at SFM (P = .070). Calcifications were observed in 18% (95% CI: 7.0%, 35.5%) of the cancers missed at FFDM and 34% (95% CI: 21.2%, 48.8%) of those missed at SFM (P = .185). Average mammographic tumor size of missed cancers manifesting as masses was 10.4 mm at FFDM and 13.6 mm at SFM (P = .036).

CONCLUSION

The use of FFDM has not reduced the challenge of missed cancers. Cancers missed at FFDM tend to have different mammographic features than those missed at SFM.

Diagnostic performance of detecting breast cancer on computed radiographic (CR) mammograms: comparison of hard copy film, 3-megapixel liquid-crystal-display (LCD) monitor and 5-megapixel LCD monitor

The purpose was to compare observer performance in the detection of breast cancer using hard-copy film, and 3-megapixel (3-MP) and 5-megapixel (5-MP) liquid crystal display (LCD) monitors in a simulated screening setting. We amassed 100 sample sets, including 32 patients with surgically proven breast cancer (masses present, N = 12; microcalcifications, N = 10; other types, N = 10) and 68 normal controls. All the mammograms were obtained using computed radiography (CR; sampling pitch of 50 mum). Twelve mammographers independently assessed CR mammograms presented in random order for hard-copy and soft-copy reading at minimal 4-week intervals. Observers rated the images on seven-point (1 to 7) and continuous (0 to 100) malignancy scales. Receiver-operating-characteristics analysis was performed, and the average area under the curve (AUC) was calculated for each modality. The jackknife method with the Bonferroni correction was applied to multireader/multicase analysis. The average AUC values for the 3-MP LCD, 5-MP LCD, and hard-copy film were 0.954, 0.947, and 0.956 on the seven-point scale and 0.943, 0.923, and 0.944 on the continuous scale, respectively. There were no significant differences among the three modalities on either scale. Soft-copy reading using 3-MP and 5-MP LCDs is comparable to hard-copy reading for detecting breast cancer.