scholarly journals Comparison of two-view versus single-view digital breast tomosynthesis and 2D-mammography in breast cancer surveillance imaging

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256514
Author(s):  
Andria Hadjipanteli ◽  
Petros Polyviou ◽  
Ilias Kyriakopoulos ◽  
Marios Genagritis ◽  
Natasa Kotziamani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Performance ◽  
Area Under The Curve ◽  
Digital Breast Tomosynthesis ◽  
Cancer Surveillance ◽  
Operating Characteristics ◽  
Real Patient ◽  
Breast Tomosynthesis ◽  
Surveillance Imaging ◽  
Sensitivity Specificity

Purpose Limited work has been performed for the implementation of digital breast tomosynthesis (DBT) in breast cancer surveillance imaging. The aim of this study was to investigate the differences between two different DBT implementations in breast cancer surveillance imaging, for patients with a personal history of breast cancer. Method The DBT implementations investigated were: (1) 2-view 2D digital mammography and 2-view DBT (2vDM&2vDBT) (2) 1-view (cranial-caudal) DM and 1-view (mediolateral-oblique) DBT (1vDM&1vDBT). Clinical performance of these two implementations was assessed retrospectively using observer studies with 118 sets of real patient images, from a single imaging centre, and six observers. Sensitivity, specificity and area under the curve (AUC) using the Jack-knife alternative free-response receiver operating characteristics (JAFROC) analysis were evaluated. Results Results suggest that the two DBT implementations are not significantly different in terms of sensitivity, specificity and AUC. When looking at the two main different lesion types, non-calcifications and calcifications, and two different density levels, no difference in the performance of the two DBT implementations was found. Conclusions Since 1vDM&1vDBT exposes the patient to half the dose of 2vDM&2vDBT, it might be worth considering 1vDM&1vDBT in breast cancer surveillance imaging. However, larger studies are required to conclude on this matter.

scholarly journals Performance evaluation of digital mammography, digital breast tomosynthesis and ultrasound in the detection of breast cancer using pathology as gold standard: an institutional experience

2022 ◽  
Vol 53 (1) ◽  
Author(s):  
Pranjali Joshi ◽  
Neha Singh ◽  
Gaurav Raj ◽  
Ragini Singh ◽  
Kiran Preet Malhotra ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Neoplasms ◽  
Diagnostic Accuracy ◽  
Digital Mammography ◽  
Gold Standard ◽  
Imaging Modality ◽  
Digital Breast Tomosynthesis ◽  
Routine Practice ◽  
Breast Tomosynthesis ◽  
Sensitivity Specificity

Abstract Background Mammography is the primary imaging modality for diagnosing breast cancer in women more than 40 years of age. Digital breast tomosynthesis (DBT), when supplemented with digital mammography (DM), is useful for increasing the sensitivity and improving BIRADS characterization by removing the overlapping effect. Ultrasonography (US), when combined with the above combination, further increases the sensitivity and diagnostic confidence. Since most of the research regarding tomosynthesis has been in screening settings, we wanted to quantify its role in diagnostic mammography. The purpose of this study was to assess the performance of DM alone vs. DM combined with DBT vs. DM plus DBT and ultrasound in diagnosing malignant breast neoplasms with the gold standard being histopathology or cytology. Results A prospective study of 1228 breasts undergoing diagnostic or screening mammograms was undertaken at our institute. Patients underwent 2 views DM, single view DBT and US. BIRADS category was updated after each step. Final categorization was made with all three modalities combined and pathological correlation was done for those cases in which suspicious findings were detected, i.e. 256 cases. Diagnosis based on pathology was done for 256 cases out of which 193 (75.4%) were malignant and the rest 63 (24.6%) were benign. The diagnostic accuracy of DM alone was 81.1%. Sensitivity, Specificity, PPV and NPV were 87.8%, 60%, 81.3% and 61.1%, respectively. With DM + DBT the diagnostic accuracy was 84.8%. Sensitivity, Specificity, PPV and NPV were 92%, 56.5%, 89% and 65%, respectively. The diagnostic accuracy of DM + DBT + US was found to be 85.1% and Sensitivity, Specificity, PPV and NPV were 96.3%, 50.7%, 85.7% and 82%, respectively. Conclusion The combination of DBT to DM led to higher diagnostic accuracy, sensitivity and PPV. The addition of US to DM and DBT further increased the sensitivity and diagnostic accuracy and significantly increased the NPV even in diagnostic mammograms and should be introduced in routine practice for characterizing breast neoplasms.

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.

scholarly journals New screening approach to detecting congenital syphilis in China: a retrospective cohort study

2020 ◽  
pp. archdischild-2020-320549
Author(s):  
Fang Hu ◽  
Shuai-Jun Guo ◽  
Jian-Jun Lu ◽  
Ning-Xuan Hua ◽  
Yan-Yan Song ◽  
...  
Keyword(s):  
Predictive Value ◽  
Congenital Syphilis ◽  
Area Under The Curve ◽  
Diagnostic Tools ◽  
Operating Characteristics ◽  
Loss To Follow Up ◽  
Sensitivity Specificity ◽  
Mother To Child ◽  
Rule Out

BackgroundDiagnosis of congenital syphilis (CS) is not straightforward and can be challenging. This study aimed to evaluate the validity of an algorithm using timing of maternal antisyphilis treatment and titres of non-treponemal antibody as predictors of CS.MethodsConfirmed CS cases and those where CS was excluded were obtained from the Guangzhou Prevention of Mother-to-Child Transmission of syphilis programme between 2011 and 2019. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using receiver operating characteristics (ROC) in two situations: (1) receiving antisyphilis treatment or no-treatment during pregnancy and (2) initiating treatment before 28 gestational weeks (GWs), initiating after 28 GWs or receiving no treatment for syphilis seropositive women.ResultsAmong 1558 syphilis-exposed children, 39 had confirmed CS. Area under the curve, sensitivity and specificity of maternal non-treponemal titres before treatment and treatment during pregnancy were 0.80, 76.9%, 78.7% and 0.79, 69.2%, 88.7%, respectively, for children with CS. For the algorithm, ROC results showed that PPV and NPV for predicting CS were 37.3% and 96.4% (non-treponemal titres cut-off value 1:8 and no antisyphilis treatment), 9.4% and 100% (non-treponemal titres cut-off value 1:16 and treatment after 28 GWs), 4.2% and 99.5% (non-treponemal titres cut-off value 1:32 and treatment before 28 GWs), respectively.ConclusionsAn algorithm using maternal non-treponemal titres and timing of treatment during pregnancy could be an effective strategy to diagnose or rule out CS, especially when the rate of loss to follow-up is high or there are no straightforward diagnostic tools.

Sign in / Sign up

Export Citation Format

Share Document