Assessment of breast positioning criteria in mammographic screening: Agreement between artificial intelligence software and radiographers

2021 ◽  
pp. 096914132199871
Author(s):  
Gunvor G Waade ◽  
Anders Skyrud Danielsen ◽  
Åsne S Holen ◽  
Marthe Larsen ◽  
Berit Hanestad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intraclass Correlation ◽  
Digital Breast Tomosynthesis ◽  
Pectoral Muscle ◽  
Mammographic Screening ◽  
Kappa Coefficient ◽  
Evidence Based ◽  
Oblique View ◽  
Perfect Agreement ◽  
Breast Tomosynthesis

Objectives To determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and digital breast tomosynthesis. Methods Assessment of breast positioning was performed by AI and by four radiographers in pairs of two on 156 examinations of women screened in Bergen, April to September 2019, as part of BreastScreen Norway. Ten criteria were used; three for craniocaudal and seven for mediolateral-oblique view. The criteria evaluated the appearance of the nipple, breast rotation, pectoral muscle, inframammary fold and pectoral nipple line. Intraclass correlation and Cohen’s kappa coefficient (κ) were used to investigate the correlation and agreement between the radiographer’s assessments and AI. Results The intraclass correlation for the pectoral nipple line between the radiographers and AI was >0.92. A substantial to almost perfect agreement (κ > 0.69) was observed between the radiographers and AI on the nipple in profile criterion. We observed a slight to moderate agreement for the other criteria (κ = 0.06–0.52) and generally a higher agreement between the two pairs of radiographers (mean κ = 0.70) than between the radiographers and AI (mean κ = 0.41). Conclusions AI has great potential in evaluating breast position criteria in mammography by reducing subjectivity. However, varying agreement between radiographers and AI was observed. Standardized and evidence-based criteria for definitions, understandings and assessment methods are needed to reach optimal image quality in mammography.

scholarly journals Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

2021 ◽  
Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Support System ◽  
Reading Time ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Screening Programs ◽  
The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

Visualization of the Nipple in Profile: Does It Really Affect Selected Outcomes in Organized Mammographic Screening?

2021 ◽  
Author(s):  
Åsne S Holen ◽  
Marthe Larsen ◽  
Nataliia Moshina ◽  
Gunvor G Wåade ◽  
Ioannis Sechopoulos ◽  
...  
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Mammographic Screening ◽  
Descriptive Statistics ◽  
Breast Volume ◽  
Compression Force ◽  
Compression Pressure ◽  
Breast Tomosynthesis ◽  
Screening Technique ◽  
Study Results ◽  
Screening Mammograms

Abstract Objective To investigate whether having the nipple imaged in profile was associated with breast characteristics or compression parameters, and whether it affected selected outcomes in screening with standard digital mammography or digital breast tomosynthesis. Methods In this IRB-approved retrospective study, results from 87 450 examinations (174 900 breasts) performed as part of BreastScreen Norway, 2016–2019, were compared by nipple in profile status and screening technique using descriptive statistics and generalized estimating equations. Unadjusted and adjusted odds ratios with 95% confidence intervals (95% CIs) were estimated for outcomes of interest, including age, breast volume, volumetric breast density, and compression force as covariates. Results Achieving the nipple in profile versus not in profile was associated with lower breast volume (845.1 cm3 versus 1059.9 cm3, P &lt; 0.01) and higher mammographic density (5.6% versus 4.4%, P &lt; 0.01). Lower compression force and higher compression pressure were applied to breasts with the nipple in profile (106.6 N and 11.5 kPa) compared to the nipple not in profile (110.8 N and 10.5 kPa, P &lt; 0.01 for both). The adjusted odds ratio was 0.95 (95% CI: 0.88–1.02; P = 0.15) for recall and 0.92 (95% CI: 0.77–1.10; P = 0.36) for screen-detected cancer for nipple in profile versus not in profile. Conclusion Breast characteristics and compression parameters might hamper imaging of the nipple in profile. However, whether the nipple was in profile or not on the screening mammograms did not influence the odds of recall or screen-detected cancer, regardless of screening technique.

scholarly journals Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis

2019 ◽  
Vol 1 (4) ◽  
pp. e180096 ◽  
Author(s):  
Emily F. Conant ◽  
Alicia Y. Toledano ◽  
Senthil Periaswamy ◽  
Sergei V. Fotin ◽  
Jonathan Go ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Concurrent Use ◽  
Improving Accuracy

scholarly journals Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Radiology ◽  
2021 ◽  
Author(s):  
Sara Romero-Martín ◽  
Esperanza Elías-Cabot ◽  
José Luis Raya-Povedano ◽  
Albert Gubern-Mérida ◽  
Alejandro Rodríguez-Ruiz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Retrospective Evaluation ◽  
Breast Tomosynthesis
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