scholarly journals Digital breast tomosynthesis (DBT) value in breast mass detection

2021 ◽  
Vol 130 (1) ◽  
pp. 1-4
Author(s):  
Angelika Kuczyńska ◽  
Łukasz Kwietniewski ◽  
Wiktor Kupisz ◽  
Joanna Kruk-Bachonko ◽  
Witold Krupski

AbstractEpidemiologically, breast cancer is the most common cancer in middle-aged women and it is one of the leading causes of cancer-related deaths. Middle-aged patients are covered by screening tests – digital mammography, often supplemented with ultrasound (US) breast examination. Other radiological tests in the diagnosis of breast cancer include such techniques as tomosynthesis, spectral mammography and magnetic resonance imaging (MRI). Many research groups around the world have demonstrated superiority of tomosynthesis in detecting focal lesions in breasts when compared to conventional mammography. Tomosynthesis usage was proposed for screening studies as a test of choice and for radiologically-guided tissue biopsies of suspicious tissue lesions.

2019 ◽  
Vol 1 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Tisha Singer ◽  
Ana P Lourenco ◽  
Grayson L Baird ◽  
Martha B Mainiero

Abstract Objective To evaluate radiologists’ supplemental screening recommendations for women with dense breasts, at average, intermediate, or high risk of breast cancer, and to determine if there are differences between their recommendations for their patients, their friends and family, and themselves. Methods This is an anonymous survey of Society of Breast Imaging (SBI) members. Demographics, knowledge of breast density as a risk factor, and recommendations for screening with digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI) in women with dense breasts, at average, intermediate, or high- risk of breast cancer were assessed. The likelihood of their recommending the screening test for their patients, their family and friends, and themselves was assessed on a Likert scale from 0 to 4 (0 = “not at all likely” to 4 = “extremely likely”). Results There were 295 responses: 67% were women, and breast imaging comprised 95% of their practice. Among participants, 53% correctly answered the question on relative risk of breast cancer when comparing extremely dense versus fatty breasts, and 57% when comparing heterogeneously dense versus scattered breasts. US is recommended at a relatively low rate (1.0–1.4 on the 0–4 scale), regardless of risk. DBT is recommended at a relatively high rate (2.5–3.0 on the 0–4 scale), regardless of risk status. MR is recommended mainly for those at high risk (3.6 on the 0–4 scale). Radiologists were more likely to recommend additional imaging for themselves than for their patients and their family and friends. Conclusion For women with dense breasts, radiologists are “somewhat likely” to recommend US and “likely” to “very likely” to recommend DBT regardless of risk group. They are “very likely” to recommend MRI for high-risk groups.


2018 ◽  
Vol 7 (2) ◽  
pp. 33
Author(s):  
Francesca Galati ◽  
Flaminia Marzocca ◽  
Andrea Tancredi ◽  
Emmanuel Collalunga ◽  
Carlo Catalano ◽  
...  

Objectives To prospectively evaluate the accuracy in tumor extent and size assessment of Digital Breast Tomosynthesis (DBT) and Magnetic Resonance Imaging (MRI) in women with known breast cancer, with pathological size as the gold standard. Methods From May 2014 to April 2016, 50 patients with known breast cancer were enrolled in our prospective study. All patients underwent MRI on a 3T magnet and DBT projections. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, evaluated in consensus each imaging set unaware of the final histological examination. MR and DBT sensitivity, PPV and accuracy were calculated, using histology as the gold standard. McNemar test was used to compare MR and DBT sensitivity. Correlation and regression analyses were used to evaluate MRI vs Histology, DBT vs Histology and MRI vs DBT lesions tumor size agreement to histological results. Results On histological examination 70 lesions were detected. MRI showed 100% sensitivity, 96% PPV and 96% accuracy; DBT sensitivity was 81%, PPV 92% and accuracy 77%. McNemar test p-value was 0.0003. Lesions size Pearson correlation coefficient was 0.97 for MRI vs Histology, 0.92 for DBT vs Histology, (p-value<0.0001). MRI vs DBT regression coefficient was 0.83. Conclusions MRI confirmed to be the most accurate imaging technique in preoperative staging of breast cancer. However, DBT showed very good accuracy, sensitivity and tumor size assessment and could be a valid tool for preoperative staging when MRI is contraindicated.


Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  

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.


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