International Journal of Medical Laboratory Research (IJMLR)

A COMPREHENSIVE EVALUATION OF BREAST CANCER DETECTION AND DIAGNOSTIC METHODS: TRADITIONAL AND EMERGING TECHNOLOGIES

 

Tania Amko, Tongbram Bidyananda Singh, Keleriano, Kaushik Kr. Bora, Liagi Ampa

​

ABSTRACT: Background: Breast cancer is the most frequently diagnosed malignancy in women worldwide, with over 2.3 million new cases annually. Despite advances in treatment, early detection remains the primary determinant of survival. Objective: To systematically evaluate the diagnostic performance of Digital Mammography (DM), Digital Breast Tomosynthesis (DBT), Ultrasound, MRI, Contrast-Enhanced Mammography (CEM), and AI-assisted imaging for breast cancer detection in women aged 30–70 years. Methods: A PRISMA-compliant systematic review of peer-reviewed literature (2010–2024) was conducted. Studies sourced from PubMed/MEDLINE, Cochrane Library, Embase, and Scopus. QUADAS-2 was applied for quality assessment. Outcome measures: cancer detection rate (CDR), sensitivity, specificity, stage at diagnosis, and recall rate. Results: Of 142 included studies (>4.2 million examinations), DBT demonstrated superior CDR (4.0–6.5/1000; 95% CI: 3.7–6.9) over DM (3.4–5.0/1000; 95% CI: 3.1–5.4) with lower recall rates. MRI achieved the highest sensitivity (90–99%) but lowest specificity (72–89%). CEM and AI-assisted imaging showed clinically promising performance, though evidence remains emerging. Conclusion: No single modality is universally optimal. A risk-stratified, personalised screening approach is recommended, with DBT as preferred standard for average-risk populations and MRI for high-risk individuals. Future prospective trials should address equity, AI validation, and access.

​

Keywords: Breast cancer screening; Digital breast tomosynthesis; Breast MRI; Artificial intelligence; Breast density; Early detection

​

​

REFERENCE: 

1. [1]. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249.

2. [2]. Siu AL; U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2016;164(4):279-296.

3. [3]. Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353(17):1773-1783.

4. [4]. Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014;311(24):2499-2507.

5. [5]. Conant EF, Beaber EF, Sprague BL, Herschorn SD, Weaver DL, Onega T, et al. Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the PROSPR consortium. Breast Cancer Res Treat. 2016;156(1):109-116.

6. [6]. Skaane P, Bandos AI, Gullien R, Eben EB, Ekseth U, Haakenaasen U, et al. Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology. 2013;267(1):47-56.

7. [7]. Lång K, Andersson I, Rosso A, Tingberg A, Timberg P, Zackrisson S. Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial. Eur Radiol. 2016;26(1):184-190.

8. [8]. Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Böhm-Vélez M, et al. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA. 2008;299(18):2151-2163.

9. [9]. Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57(2):75-89.

10. [10]. Leach MO, Boggis CR, Dixon AK, Easton DF, Eeles RA, Evans DG, et al. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet. 2005;365(9473):1769-1778.

11. [11]. Kriege M, Brekelmans CT, Boetes C, Besnard PE, Zonderland HM, Obdeijn IM, et al. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med. 2004;351(5):427-437.

12. [12]. Jochelson MS, Lobbes MBI. Contrast-enhanced mammography: state of the art. Radiology. 2021;299(1):36-48.

13. [13]. Lobbes MBI, Lalji UC, Nelemans PJ, Houben IP, Smidt ML, Heuts E, et al. The quality of tumour size assessment by contrast-enhanced spectral mammography and the benefit of additional breast MRI. J Cancer. 2015;6(2):144-150.

14. [14]. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.

15. [15]. Lång K, Josefsson V, Larsson AM, Larsson S, Högberg C, Sartor H, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised controlled trial. Lancet Oncol. 2023;24(8):936-944.

16. [16]. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk stratification. Radiology. 2019;292(1):60-66.

17. [17]. Sprague BL, Gangnon RE, Burt V, Trentham-Dietz A, Hampton JM, Wellman RD, et al. Prevalence of mammographically dense breasts in the United States. J Natl Cancer Inst. 2014;106(10):dju255.

18. [18]. Wolfe JN. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer. 1976;37(5):2486-2492.

19. [19]. D'Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS Atlas: Breast Imaging Reporting and Data System. 5th ed. Reston (VA): American College of Radiology; 2013.

20. [20]. Lehman CD, Arao RF, Sprague BL, Lee JM, Buist DS, Kerlikowske K, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology. 2017;283(1):49-58.

21. [21]. Houssami N, Ciatto S, Macaskill P, Lord SJ, Warren RM, Dixon JM, et al. Accuracy and surgical impact of magnetic resonance imaging in breast cancer staging: systematic review and meta-analysis in detection of multifocal and multicentric cancer. J Clin Oncol. 2008;26(19):3248-3258.

22. [22]. Wishart GC, Warwick J, Pitsinis V, Duffy S, Britton PD. Measuring performance in clinical breast examination. Br J Surg. 2010;97(8):1246-1252.

23. [23]. Pace LE, Keating NL. A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA. 2014;311(13):1327-1335.

24. [24]. Comstock CE, Gatsonis C, Newstead GM, Snyder BS, Gareen IF, Bergin JT, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA. 2020;323(8):746-756.

25. [25]. Mango VL, Morris EA, Dershaw DD, Abramson A, Fry C, Moskowitz CS, et al. Abbreviated protocol for breast MRI: are multiple sequences needed for cancer detection? Eur J Radiol. 2015;84(1):65-70.

26. [26]. Geisel J, Raghu M, Hooley R. The role of ultrasound in breast cancer screening: the case for and against ultrasound. Semin Ultrasound CT MR. 2018;39(1):25-34.

27. [27]. Melnikow J, Fenton JJ, Whitlock EP, Miglioretti DL, Weyrich MS, Thompson JH, et al. Supplemental screening for breast cancer in women with dense breasts: a systematic review for the U.S. Preventive Services Task Force. Ann Intern Med. 2016;164(4):268-278.

28. [28]. Hooley RJ, Greenberg KL, Stackhouse RM, Geisel JL, Butler RS, Philpotts LE. Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09-41. Radiology. 2012;265(1):59-69.

29. [29]. Brem RF, Lenihan MJ, Lieberman J, Torrente J. Screening breast ultrasound: past, present, and future. AJR Am J Roentgenol. 2015;204(2):234-240.

30. [30]. Kerlikowske K, Hubbard RA, Miglioretti DL, Geller BM, Yankaskas BC, Lehman CD, et al. Comparative effectiveness of digital versus film-screen mammography in community practice in the United States: a cohort study. Ann Intern Med. 2011;155(8):493-502.

31. [31]. Partridge SC, McDonald ES. Diffusion weighted magnetic resonance imaging of the breast: protocol optimisation, guidelines for interpretation, and potential clinical applications. Magn Reson Imaging Clin N Am. 2013;21(3):601-624.

32. [32]. U.S. Preventive Services Task Force. Breast cancer: screening. Recommendation Statement. 2024. Available from: https://www.uspreventiveservicestaskforce.org

33. [33]. Mainiero MB, Moy L, Baron P, Didwania AD, diFlorio RM, Green ED, et al. ACR Appropriateness Criteria breast cancer screening. J Am Coll Radiol. 2017;14(11 Suppl):S383-S390.

34. [34]. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019;292(3):520-536.

35. [35]. 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.

36. [36]. Ohuchi N, Suzuki A, Sobue T, Kawai M, Yamamoto S, Zheng YF, et al. Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-Cancer Randomized Trial (J-START): a randomised controlled trial. Lancet. 2016;387(10016):341-348.

37. [37]. Tabar L, Yen AM, Wu WY, Chen SL, Chiu SY, Fann JC, et al. Insights from the breast cancer screening trials: how screening affects the natural history of breast cancer and implications for evaluating service screening programs. Breast J. 2015;21(1):13-20.

38. [38]. Lynge E, Sand Ravn C, Hjertholm P, Andersen JH, Christiansen P, Vejborg I. Breast cancer screening in Denmark: a cohort study of tumour characteristics, overdiagnosis, and survival. J Natl Cancer Inst. 2021;113(7):847-856.

39. [39]. Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review. Lancet. 2012;380(9855):1778-1786.

40. [40]. Lehman CD, Lee JM, DeMartini WB, Hippe DS, Rendi MH, Kalish G, et al. Screening MRI in women with a personal history of breast cancer. J Natl Cancer Inst. 2016;108(3):djv349.

41. [41]. Hovda T, Holen ÅS, Chen Y, Lund-Hanssen H, Lømo J, Soumekh Tehrani L, et al. Interval and consecutive round breast cancer in a randomized controlled trial comparing digital breast tomosynthesis and digital mammography screening (TOSYMA). Eur Radiol. 2022;32(5):3455-3463.

42. [42]. Pattacini P, Nitrosi A, Giorgi Rossi P, Iotti V, Ravaioli S, Melotti MG, et al. Digital mammography versus digital mammography plus tomosynthesis for breast cancer screening: the Reggio Emilia Tomosynthesis Randomized Trial. Radiology. 2022;302(2):299-307.

43. [43]. Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology. 2023;307(2):e220426.

44. [44]. Richman IB, Hoag JR, Xu X, Ross JS, Pelletier SR, Chagpar AB, et al. Adoption of digital breast tomosynthesis in clinical practice. JAMA Intern Med. 2023;183(9):1010-1017.

45. [45]. Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, et al. Evaluating AI systems for breast cancer detection in mammography: the International Cancer Imaging Archive (TCIA) Challenge. Nat Med. 2023;29(4):985-994.

46. [46]. Sung JS, Lebron L, Keating D, D'Alessio D, Comstock CE, Lee CH, et al. Performance of dual-energy contrast-enhanced digital mammography for screening women at increased risk of breast cancer. Radiology. 2023;307(5):e220422.

47. [47]. Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, et al. External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms (PRAIM Study). JAMA Oncol. 2023;9(11):1514-1520.

48. [48]. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, Lynge E, Vejborg I, Mann RM, et al. An artificial intelligence-based mammography screening protocol for breast cancer: outcome and radiologist workload (STREAM Trial). Radiology. 2024;310(1):e230970.