Artificial Intelligence as an Assistant in Breast Cancer Screening

HomeRadiologyVol. 302, No. 3 PreviousNext Reviews and CommentaryFree AccessEditorialArtificial Intelligence as an Assistant in Breast Cancer ScreeningMichael H. Fuchsjäger , Gabriel AdelsmayrMichael H. Fuchsjäger , Gabriel AdelsmayrAuthor AffiliationsFrom the Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, Graz 8036, Austria.Address correspondence to M.H.F. (e-mail: [email protected]).Michael H. Fuchsjäger Gabriel AdelsmayrPublished Online:Dec 14 2021https://doi.org/10.1148/radiol.212675MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Romero-Martín et al in this issue.Michael H. Fuchsjäger, MD, is professor of radiology and chairman of the Department of Radiology, Medical University of Graz, Austria. His clinical work and research are dedicated to diagnostic and interventional breast radiology. Professor Fuchsjäger is on the executive board of the European Society of Breast Imaging and the board of directors of the European Society of Radiology, currently serving as its chairman. He was the president of the European Congress of Radiology 2021.Download as PowerPointOpen in Image Viewer Gabriel Adelsmayr, MD, PhD, is a consultant radiologist at the Department of Radiology, Medical University of Graz, Austria. He received his MD and PhD degrees from the Medical University of Vienna and performed his residency at the Medical University of Vienna, Austria, and the Medical University of Graz, Austria. His clinical work and research are focused on breast, cardiac, and musculoskeletal radiology.Download as PowerPointOpen in Image Viewer In the late 1990s, computer-assisted detection systems appeared capable of aiding the detection of breast carcinomas and helping manage the ever-growing workloads of radiologists dedicated to reading mammograms. Over time, however, data proved that computer-assisted detection systems improved neither accuracy nor any other relevant measure in interpreting mammograms (1,2). Now, developments in artificial intelligence (AI) offer fresh hope for increasing radiologists’ speed and accuracy when reading mammograms.AI systems potentially could be used to automatically detect and classify suspicious breast lesions on mammograms, assigning a likelihood of malignancy and thereby aiding radiologists in reading both digital mammography (DM) and digital breast tomosynthesis (DBT) images. Solutions like these could reduce recall rates and workloads in screening programs with high volumes of mammograms. The most comprehensive approach to workload reduction in the setting of mammographic screening would include the stand-alone application of AI not only to detect suspicious breast lesions, but also, and even more importantly, to identify true-negative mammograms and exclude them from the radiologists’ workload. In retrospective studies, the use of AI to exclude true-negative mammograms led to a significant reduction in workload while maintaining a cancer detection performance level comparable to that of radiologists (3,4).As a screening tool, the sensitivity of DM in the detection of breast cancer was highest in older women with low-density breasts (5). However, as breast density increases, the sensitivity of DM in detection of breast cancer decreases because the superimposition of dense breast tissue can mask lesions. The seriousness of this limitation is compounded by the fact that dense breast tissue increases the risk for breast cancer (6). DM findings may be more often classified incorrectly as suspicious, especially in younger women with dense breasts, resulting in higher recall rates than in women with lower-density breast tissue (7).A method to overcome the aforementioned limitation of DM is DBT. With DBT, pseudo three-dimensional images are obtained via low-dose two-dimensional projections with different x-ray tube angles, allowing volumetric reconstruction of the whole breast. This procedure has been shown to add significant diagnostic value when used in conjunction with DM in women with dense breast tissue (8).The main nontechnical limitation of breast cancer screening is the high workload for radiologists, who must read many mammograms—the vast majority of which are reported as true-negative findings. In screening situations, this workload further increases when DBT is used alone or in addition to DM and both studies are double-read independently by two radiologists.As described in this issue of Radiology, Romero-Martín et al (9) retrospectively evaluated the stand-alone performance of an AI system as a reader of screening DM and DBT studies. Their study also assessed the impact of the stand-alone use of this AI system on recall rates.This study comprised 15 999 examinations, including 113 cancers. Five dedicated breast radiologists interpreted the examinations. For each examination, the radiologists performed four independent readings: two readings of DM studies and two readings of DBT studies. The AI system under evaluation used a deep learning algorithm to detect and score suspicious breast lesions seen on DM and DBT studies. Each AI-recalled case with known cancer was reviewed by a panel of four radiologists to assess whether AI classified the lesion correctly as malignant. Sensitivities and recall rates of DM and DBT were compared between AI alone and radiologists’ single or double readings of DM studies and single or double readings of DBT studies. The investigators found that stand-alone AI reading of DM images yielded sensitivity that was noninferior to that of radiologists’ single and double readings, with a lower recall rate. For DBT, on the other hand, stand-alone AI again achieved sensitivity as good as that of single and double readings, but the recall rate was almost four times higher than that for radiologists’ readings.The authors concluded that AI as a stand-alone reader could replace the radiologist in the interpretation of DM images but could not do so for the interpretation of DBT images without markedly increasing the recall rate.Limitations of this study include its retrospective single-center nature, the use of technology from one vendor to perform DM and DBT, and the evaluation of only one AI system. However, a noteworthy merit of the study is that its population reflects a real-world screening scenario.Similar performance levels in breast cancer detection on DM images were found for AI systems in another trial (10). However, data on the performance of a comparable AI-only approach for DBT image interpretation are lacking.Given that tissue density influences breast cancer detection at mammography, the relative abilities of deep-learning AI and dedicated breast radiologists to detect suspicious lesions in dense breast tissue could decisively influence the applicability of AI in breast cancer screening. In dense and extremely dense breast tissue, breast US is an alternative to DBT that has been shown to improve the detection of malignant lesions with the burdens of a higher workload and examiner dependence. Therefore, in the screening of women with dense and very dense breasts (ie, American College of Radiology Breast Imaging Reporting and Data System breast density categories III and IV), assistance from AI could yield a considerable workload reduction if it could be shown to reliably identify negative mammograms and make US unnecessary. Future AI systems used to detect breast cancer will therefore require superior performance, particularly in dense and extremely dense breasts, to increase sensitivity and reduce radiologists’ workloads. However, sensitivity for breast cancer detection must be prudently balanced against the recall rate to prevent overdiagnosis, overtreatment, and, ultimately, increased workloads.Besides the performance of AI systems in breast cancer detection, what other concerns might be raised before such systems find their way into routine breast cancer screening?Legal issues are one area of concern that will need to be addressed. Like humans, AI to date performs far from flawlessly, and errors in image interpretation and diagnosis must be expected when using AI-based or AI-aided systems. It follows that a clear approach to handling cases of misdiagnosis and mismanagement based on AI-guided decisions must be established before an AI system is applied in routine practice.Because AI systems use large quantities of data, data protection should also be a key objective prior to any implementation of AI. Anonymized patient-related data are often loaded into a cloud-based data management system, where the data are analyzed by an algorithm. Anonymization of patient-related data without any opportunity for back-tracing, as well as data security to prevent any unwarranted third-party access, are mandatory for the implementation of AI-based diagnostic tools.The threat of radiologists’ imminent replacement by AI systems is off the table. However, the role of the radiologist in diagnosing diseases may indeed change over the coming years, with AI systems massively supporting humans in some specific areas of image interpretation. Despite these concerns, we as radiologists should be aware of our obligation not only to supply the best available medical techniques, but also to ensure that the right examination is performed at the right time for the right patient. While radiologic diagnoses may depend increasingly on machines in the future, radiologists’ expertise will continue to be needed to communicate AI-based findings, help resolve discrepancies or ambiguities in AI-based results, and provide consultations and follow-up recommendations to both referring physicians and patients.Disclosures of conflicts of interest: M.H.F. disclosed no relevant relationships. G.A. disclosed no relevant relationships.AcknowledgmentThe authors are grateful to Ada Muellner, MS, for editing the manuscript.References1. Fenton JJ, Taplin SH, Carney PA, et al. Influence of computer-aided detection on performance of screening mammography. N Engl J Med 2007;356(14):1399–1409. Crossref, Medline, Google Scholar2. Lehman CD, Wellman RD, Buist DS, et al. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. JAMA Intern Med 2015;175(11):1828–1837. Crossref, Medline, Google Scholar3. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 2019;29(9):4825–4832. Crossref, Medline, Google Scholar4. Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology 2019;293(1):38–46. Link, Google Scholar5. Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 2002;225(1):165–175. Link, Google Scholar6. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356(3):227–236. Crossref, Medline, Google Scholar7. Lehman CD, White E, Peacock S, Drucker MJ, Urban N. Effect of age and breast density on screening mammograms with false-positive findings. AJR Am J Roentgenol 1999;173(6):1651–1655. Crossref, Medline, Google Scholar8. Østerås BH, Martinsen ACT, Gullien R, Skaane P. Digital Mammography versus Breast Tomosynthesis: Impact of Breast Density on Diagnostic Performance in Population-based Screening. Radiology 2019;293(1):60–68. Link, Google Scholar9. Romero-Martín S, Elías-Cabot E, Raya-Povedano JL, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. Stand-alone use of artificial intelligence for digital mammography and digital breast tomosynthesis: a retrospective evaluation. Radiology 2022;302(3):535–542. Abstract, Google Scholar10. Salim M, Wåhlin E, Dembrower K, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncol 2020;6(10):1581–1588. Crossref, Medline, Google ScholarArticle HistoryReceived: Oct 22 2021Revision requested: Nov 8 2021Revision received: Nov 10 2021Accepted: Nov 11 2021Published online: Dec 14 2021Published in print: Mar 2022 FiguresReferencesRelatedDetailsCited ByThe Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental ScreeningPaula B.Gordon2022 | Current Oncology, Vol. 29, No. 5Accompanying This ArticleStand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective EvaluationDec 14 2021RadiologyRecommended Articles Reducing False-Positive Screening MRI Rates in Women with Extremely Dense BreastsRadiology2021Volume: 301Issue: 2pp. 293-294Can Digital Breast Tomosynthesis Solve the Challenge of Dense Breast Parenchyma?Radiology2019Volume: 293Issue: 1pp. 69-71MRI Screening Reduces Interval Breast Cancer in Women with Dense BreastsRadiology: Imaging Cancer2020Volume: 2Issue: 1Digital Mammography and Breast Tomosynthesis Performance in Women with a Personal History of Breast Cancer, 2007–2016Radiology2021Volume: 300Issue: 2pp. 290-300A Deep Learning Model to Triage Screening Mammograms: A Simulation StudyRadiology2019Volume: 293Issue: 1pp. 38-46See More RSNA Education Exhibits Breast Cancer in Younger Women: Screening and Diagnostic Imaging ExaminationsDigital Posters2018Contrast-Enhanced Mammography: Current Indications and Future Directions Â Digital Posters2019Non-Contrast-Enhanced Breast MR Screening for Women with Dense BreastsDigital Posters2019 RSNA Case Collection Multifocal breast cancerRSNA Case Collection2020Invasive ductal carcinoma as developing asymmetryRSNA Case Collection2021Hyperplastic Residual Breast TissueRSNA Case Collection2022 Vol. 302, No. 3 Metrics Altmetric Score PDF download