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In order to achieve optimal diagnostic performance in breast tomosynthesis (BT) imaging, the parameters of the imaging chain should be evaluated. For the purpose of such evaluations, a simulation procedure based on the Monte Carlo code system Penelope and the geometry of a Siemens BT system has been developed to generate BT projection images. In this work, the simulation procedure is validated by comparing contrast and sharpness in simulated images with contrast and sharpness in real images acquired with the BT system. The results of the study showed a good agreement of sharpness in real and simulated reconstructed image planes, but the contrast was shown to be higher in the simulated compared with the real projection images. The developed simulation procedure could be used to generate BT images, but it is of interest to further investigate how the procedure could be modified to generate more realistic image noise and contrast. [ABSTRACT FROM AUTHOR]

Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training ( n = 89 285), validation ( n = 2106), and test ( n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.

Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: M.D.H.B. Activities related to the present article: The development of the database and its ongoing management was and is funded by grants from Cancer Research UK to The Royal Surrey NHS Foundation Trust. C30682/A28396. Activities not related to the present article: Royal Surrey NHS Foundation Trust received funding from Google Health UK to support research collaboration in the development and evaluation of Google’s AI system for breast cancer screening (McKinney et al. International evaluation of an AI system for breast cancer screening. Nature. Nature Publishing Group; 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6); Royal Surrey NHS Foundation Trust receives additional income from Cancer Research UK as a share of payments for commercial licensed access to the OPTIMAM Mammography Image Database. Other relationships: disclosed no relevant relationships. L.M.W. Activities related to the present article: The development of the database and its ongoing management was and is funded by grants from Cancer Research UK to The Royal Surrey NHS Foundation Trust. Activities not related to the present article: Royal Surrey NHS Foundation Trust received funding from Google Health UK to support research collaboration in the development and evaluation of Google’s AI system for breast cancer screening (McKinney et al. International evaluation of an AI system for breast cancer screening. Nature. Nature Publishing Group; 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6); author teaches two lectures a year on a MSc course at the University of Surrey; Royal Surrey NHS Foundation Trust receives additional income from Cancer Research UK as a share of payments for commercial licensed access to the OPTIMAM Mammography Image Database. Other relationships: disclosed no relevant relationships. D.W. Activities related to the present article: The development of the database and its ongoing management was and is funded by grants from Cancer Research UK to The Royal Surrey NHS Foundation Trust. Activities not related to the present article: Royal Surrey NHS Foundation Trust received funding from Google Health UK to support research collaboration in the development and evaluation of Google’s AI system for breast cancer screening (McKinney et al. International evaluation of an AI system for breast cancer screening. Nature. Nature Publishing Group; 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6); Royal Surrey NHS Foundation Trust receives additional income from Cancer Research UK as a share of payments for commercial licensed access to the OPTIMAM Mammography Image Database. Other relationships: disclosed no relevant relationships. E.L. Activities related to the present article: The development of the database and its ongoing management was and is funded by grants from Cancer Research UK to The Royal Surrey NHS Foundation Trust. Activities not related to the present article: Royal Surrey NHS Foundation Trust received funding from Google Health UK to support research collaboration in the development and evaluation of Google’s AI system for breast cancer screening (McKinney et al. International evaluation of an AI system for breast cancer screening. Nature. Nature Publishing Group; 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6); Royal Surrey NHS Foundation Trust receives additional income from Cancer Research UK as a share of payments for commercial licensed access to the OPTIMAM Mammography Image Database. Other relationships: disclosed no relevant relationships. A.M. disclosed no relevant relationships. M.G.W. Activities related to the present article: Cancer Research UK program grant partly funded setup of database. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. L.S.W. disclosed no relevant relationships. R.G.W. Activities related to the present article: Cancer Research UK charity grant funding the OPTIMAM project staff undertaking data collection from 2009 to 2019. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. R.M. disclosed no relevant relationships. K.C.Y. Activities related to the present article: The development of the database and its ongoing management was and is funded by grants from Cancer Research UK to The Royal Surrey NHS Foundation Trust. Activities not related to the present article: Royal Surrey NHS Foundation Trust received funding from Google Health UK to support research collaboration in the development and evaluation of Google’s AI system for breast cancer screening (McKinney et al. International evaluation of an AI system for breast cancer screening. Nature. Nature Publishing Group; 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6); Royal Surrey NHS Foundation Trust receives additional income from Cancer Research UK as a share of payments for commercial licensed access to the OPTIMAM Mammography Image Database. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.