Breast cancer detection: Comparison of digital mammography and digital breast tomosynthesis across non-dense and dense breasts

Temporal Evolution and Associated Factors of Adherence to Mammography Screening among Women in Spain: Results from Two National Health Surveys (2017-2020)

Ensuring equity in cancer screening is recommended by the European Commission. Despite the fact that breast cancer screening is a free population-based program in Spain, there remains considerable variation in the adherence to screening rate among population groups. This study was designed to describe the adherence of breast cancer screening in women in Spain, to evaluate the evolution in the period from 2017 to 2020, and to determine the variables that influence choosing to undergo breast cancer screening. A nationwide cross-sectional study with 7220 females aged 50-69 years from the 2017 Spanish National Health Survey and the 2020 European Health Survey for Spain was performed. We investigated mammography uptake rates, with socio-demographic factors, lifestyle habits, and health-related characteristics as independent variables. Multivariable logistic regression was used to identify the associated factors of mammography adherence. Of the women, 78% had undergone mammography in the previous two years, and there was a significant decrease in the uptake rate for breast cancer screening from 2017 (81.23%) to 2020 (74.68%) (p < 0.001). Educational level, marital status, residential location, nationality, smoking status, alcohol consumption, and leisure-time physical activity were all associated factors of mammography uptake.

Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection

Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.

Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

Background: To compare the diagnostic efficacy of digital breast tomosynthesis (DBT) and ultrasound across breast densities in women recalled for assessment. Methods: A total of 482 women recalled for assessment from January 2017 to December 2019 were selected for the study. Women met the inclusion criteria if they had undergone DBT, ultrasound and had confirmed biopsy results. We calculated sensitivity, specificity, PPV, and AUC for DBT and ultrasound. Results: In dense breasts, DBT showed significantly higher sensitivity than ultrasound (98.2% vs. 80%; p < 0.001), but lower specificity (15.4% vs. 55%; p < 0.001), PPV (61.3% vs. 71%; p = 0.04) and AUC (0.568 vs. 0.671; p = 0.001). In non-dense breasts, DBT showed significantly higher sensitivity than ultrasound (99.2% vs. 84%; p < 0.001), but no differences in specificity (22% vs. 33%; p = 0.14), PPV (69.2% vs. 68.8%; p = 0.93) or AUC (0.606 vs. 0.583; p = 0.57). Around 73% (74% dense and 71% non-dense) and 77% (81% dense and 72% non-dense) of lesions assigned a RANZCR 3 by DBT and ultrasound, respectively, were benign. Conclusion: DBT has higher sensitivity, but lower specificity and PPV than ultrasound in women with dense breasts recalled for assessment. Most lesions rated RANZCR 3 on DBT and ultrasound are benign and may benefit from short interval follow-up rather than biopsy.

Comparison of Diagnostic Test Accuracy of Cone-Beam Breast Computed Tomography and Digital Breast Tomosynthesis for Breast Cancer: A Systematic Review and Meta-Analysis Approach

BACKGROUND

Cone-beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) remain the main 3D modalities for X-ray breast imaging. This study aimed to systematically evaluate and meta-analyze the comparison of diagnostic accuracy of CBBCT and DBT to characterize breast cancers.

METHODS

Two independent reviewers identified screening on diagnostic studies from 1 January 2015 to 30 December 2021, with at least reported sensitivity and specificity for both CBBCT and DBT. A univariate pooled meta-analysis was performed using the random-effects model to estimate the sensitivity and specificity while other diagnostic parameters like the area under the ROC curve (AUC), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were estimated using the bivariate model.

RESULTS

The pooled sensitivity specificity, LR+ and LR- and AUC at 95% confidence interval are 86.7% (80.3-91.2), 87.0% (79.9-91.8), 6.28 (4.40-8.96), 0.17 (0.12-0.25) and 0.925 for the 17 included studies in DBT arm, respectively, while, 83.7% (54.6-95.7), 71.3% (47.5-87.2), 2.71 (1.39-5.29), 0.20 (0.04-1.05), and 0.831 are the pooled sensitivity specificity, LR+ and LR- and AUC for the five studies in the CBBCT arm, respectively.

CONCLUSIONS

Our study demonstrates that DBT shows improved diagnostic performance over CBBCT regarding all estimated diagnostic parameters; with the statistical improvement in the AUC of DBT over CBBCT. The CBBCT might be a useful modality for breast cancer detection, thus we recommend more prospective studies on CBBCT application.