Comparison between execution and reading time of 3D ABUS versus HHUS

Brunetti, Nicole; Giorgis, Sara De; Zawaideh, Jeries Paolo; Rossi, Federica; Calabrese, Massimo; Tagliafico, Alberto

doi:10.1007/s11547-020-01209-8

Cited by 12 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The robustness of these kinds of systems remains to be improved as variations in the images could lead to wrong removal. In this sense, the advantage of deep-learning-based methods is such that the robustness has been drastically enhanced [30][31][32][38][39][40][41][42][43]. However, deep-learning approach requires a very large number of images to be properly trained.…”

Section: Discussionmentioning

confidence: 99%

Comparison between two packages for pectoral muscle removal on mammographic images

et al. 2022

View full text Add to dashboard Cite

Background Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose To compare performance of the two packages on a single database of FFDM images. Methods Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA’s and OpenBreast’s removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA’s Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P < 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P > 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison between two packages for pectoral muscle removal on mammographic images

et al. 2022

View full text Add to dashboard Cite

show abstract

“…SR decreases the equivocality due to a non-uniform lexicon. Wide application of SR is essential to offer referring physicians the best quality of service and to researchers the best quality information in the setting of big data [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background Radiology is an essential tool in the management of a patient. The aim of this manuscript was to build structured report (SR) Mammography based in Breast Cancer. Methods A working team of 16 experts (group A) was composed to create a SR for Mammography Breast Cancer. A further working group of 4 experts (group B), blinded to the activities of the group A, was composed to assess the quality and clinical usefulness of the SR final draft. Modified Delphi process was used to assess level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency and to measure quality analysis according to the average inter-item correlation. Results The final SR version was built by including n = 2 items in Personal Data, n = 4 items in Setting, n = 2 items in Comparison with previous breast examination, n = 19 items in Anamnesis and clinical context; n = 10 items in Technique; n = 1 item in Radiation dose; n = 5 items Parenchymal pattern; n = 28 items in Description of the finding; n = 12 items in Diagnostic categories and Report and n = 1 item in Conclusions. The overall mean score of the experts and the sum of score for structured report were 4.9 and 807 in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.82 in the second round. About the quality evaluation, the overall mean score of the experts was 3.3. The Cronbach’s alpha (Cα) correlation coefficient was 0.90. Conclusions Structured reporting improves the quality, clarity and reproducibility of reports across departments, cities, countries and internationally and will assist patient management and improve breast health care and facilitate research.

show abstract

“…59.7% of women stated that they would choose the HHUS as a future form of examination, if provided with the opportunity, given that its shorter, offers more comfort and provides results immediately [36]. In addition, Brunetti et al showed that the HUUS has a significantly shorter screening duration (average 5.2 minutes) compared to the ABUS (average 16.7 minutes) [37].…”

Section: Comparison Of Hhus and Abusmentioning

confidence: 99%

Automated Breast Ultrasound

Žaja

Matijaš

2022

Radiol. vjesn. (Online)

View full text Add to dashboard Cite

Due to the growing number of breast cancer patients, an early diagnosis is important in order to reduce the mortality rate of those affected. Methods such as mammography, DBT, MRI, HHUS or ABUS are used in the detection of breast cancer. The aim of this article is to review the literature showing the basic principle of ABUS and to point out its advantages and disadvantages in relation to conventional methods of breast imaging. ABUS is a relatively new ultrasound method that performs well on patients with dense breast tissue. It reduces operator dependence and provides valuable diagnostic information with multiplanar reconstructions. Using evidence from reliable researches, studies have demonstrated that ABUS has a higher diagnostic accuracy compared to mammography, which remains the primary modality for early diagnosis of breast cancer. Applying ABUS as an adjunct to mammography during the screening test has proven effective and further confirmed the importance of their application in clinical practice. The disadvantage of the combination of ABUS and mammography was that in a large number of studies the specificity was lower compared to mammography itself. Compared to DBT, ABUS has demonstrated to have a higher diagnostic performance, with the exception that it lacks the ability to effectively detect calcifications. Although MRI seem to outperform ABUS, ABUS devices offer a cost-effective and easy to use imaging system, making it the best alternative. The HHUS technique, on the other hand, was perceived by many studies as less painful, with a shorter operative time compared to ABUS. However, the sensitivity and specificity of this screening method continues to remain inferior to ABUS. The use of artificial intelligence is becoming widely used today. As a result, the CAD software has been developed to be applied in conjunction with ABUS in order to improve the detection rate of breast cancer as well as its accuracy. The use of CAD significantly reduced image reading time and improved the overall diagnostic accuracy of ABUS. According to all the presented data, the use of ABUS medical devices in clinical practice continues to grow in importance and with the further development of technology and medicine, its full integration into healthcare systems around the world is expected.

show abstract

Comparison between execution and reading time of 3D ABUS versus HHUS

Cited by 12 publications

References 23 publications

Comparison between two packages for pectoral muscle removal on mammographic images

Comparison between two packages for pectoral muscle removal on mammographic images

Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal

Automated Breast Ultrasound

Contact Info

Product

Resources

About