Advances in Breast Cancer Diagnosis: A Comprehensive Review
Keywords:
Artificial Intelligence, Breast Cancer, Biomarkers, Diagnosis, Liquid Biopsy, Sub-Saharan Africa, UltrasoundAbstract
Breast cancer remains the most prevalent malignancy and a leading cause of cancer mortality among women globally, with rising incidence across sub-Saharan Africa. Early and accurate diagnosis is critical for improving outcomes, yet access to advanced diagnostic tools remains uneven. This review synthesizes recent advances in breast cancer diagnostic modalities, emphasizing innovations from 2020–2025 and their potential application in low- and middle- income settings. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science for recent studies on imaging, molecular diagnostics, biomarkers, and artificial intelligence in breast cancer detection and characterization. Emphasis was placed on comparative findings from global, regional (sub-Saharan African), and Nigerian studies. Emerging diagnostic tools such as digital mammography, contrast-enhanced MRI, elastography, and AI-assisted imaging have significantly improved sensitivity and specificity. Molecular biomarkers and liquid biopsy technologies, including circulating tumor DNA and exosomal assays, are enhancing early detection, disease monitoring, and treatment stratification. Hybrid diagnostic pathways integrating imaging and molecular data show promise for personalized, minimally invasive diagnostics. Modern advances are revolutionizing breast cancer diagnosis, but regional disparities persist. Strengthening infrastructure, capacity building, and the integration of hybrid diagnostic approaches are essential to achieve equitable breast cancer detection and management, particularly in resource-limited regions.
References
1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2023;73(4):354–424. doi:10.3322/caac.21834.
2. World Health Organization, Regional Office for Africa. Assessment of breast cancer control capacities in the WHO African Region. Brazzaville: WHO Regional Office for Africa; 2022. Available from: https://www.afro.who.int
3. Jedy-Agba E, Curado MP, Ogunbiyi O, Oga E, Igbinoba F, Koechlin A, et al. Cancer incidence in Nigeria: a report from population-based cancer registries. Cancer Epidemiol. 2012;36(5): e271–8. doi: 10.1016/j.canep.2012.05.009.
4. Olayide A, Chijindu C, Fathi M, Adebayo O, Alabi M, Eze C, et al. Nigeria's 6-year (2018–2023) stage distribution of breast cancer at diagnosis: a systematic review and meta-analysis. ecancermedicalscience. 2025; 19:1899. doi:10.3332/ecancer.2025.1899.
5. Uchendu OJ. Cancer incidence in Nigeria: a tertiary hospital experience. Asian Pac J Cancer Care. 2020;5(1):27–32. doi:10.31557/apjcc.2020.5.1.27-32.
6. Gbokwe KK, Eze U, Badu E, Abebe Y, Chanda E, Mensah S, et al. Comparative examination of breast cancer burden in Sub-Saharan Africa, 1990–2019: estimates from the Global Burden of Disease 2019 study. BMJ Open. 2024;14(3):e082492. doi:10.1136/bmjopen-2023-082492.
7. Zheng W, Dutil J, Banegas MP, Michailidou K, Dennis J, Amos CI, et al. Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction. Nat Genet. 2024;56(5):819–30. doi:10.1038/s41588-024-01736-4.
8. 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. doi:10.1148/radiol.12121373.
9. Monticciolo DL, Newell MS, Moy L, Monsees B, Lee CI, Destounis S, et al. Digital breast tomosynthesis: a decade of practice in review. J Am Coll Radiol. 2023;20(2):127–33. doi: 10.1016/j.jacr.2022.08.005.
10. Lee CI, McCarthy AM, Miglioretti DL, Monsees B, Feig SA, Kerlikowske K, et al. Breast cancer screening with tomosynthesis in women with dense breasts: outcomes from the Breast Cancer Surveillance Consortium. JAMA Oncol. 2022;8(3):402–11. doi:10.1001/jamaoncol.2021.7137.
11. Giampietro C, Ambrosetti MC, Montemezzi S, Tagliafico A, Houssami N, Sardanelli F. Impact of digital breast tomosynthesis on screening performance: a systematic review and meta-analysis. Eur Radiol. 2022;32(3):1969–83. doi:10.1007/s00330-021-08181-5.
12. Kawooya MG, Kisembo HN, Remedios D, Malumba R, Pérez M del R, Ige T, et al. An African point of view on quality and safety in imaging. Insights Imaging. 2022; 13:58. doi:10.1186/s13244-022-01203-w.
13. Jochelson MS, Dershaw DD, Sung JS, Heerdt AS, Thornton C, Moskowitz CS, et al. Bilateral contrast-enhanced dual-energy digital mammography: feasibility and comparison with conventional digital mammography and MR imaging. Radiology. 2013;266(3):743–51. doi:10.1148/radiol.12110340.
14. Chung WS, Tang YC, Cheung YC. Contrast-enhanced mammography: a literature review of clinical uses for cancer diagnosis and surgical oncology. Cancers (Basel). 2024;16(24):4143. doi:10.3390/cancers16244143.
15. Fallenberg EM, Dromain C, Diekmann F, Engelken F, Krohn M, Singh JM, et al. Contrast-enhanced spectral mammography versus MRI: initial results in the detection and characterization of breast lesions. Eur Radiol. 2014;24(1):256–64. doi:10.1007/s00330-013-3007-7.
16. Covington MF, Collins HR, Gee MS, Slanetz PJ, Specht MC, Lee CI, et al. Contrast-enhanced mammography: implementation, performance, and use for supplemental breast cancer screening. Radiol Clin North Am. 2021;59(1):29–41. doi: 10.1016/j.rcl.2020.08.006.
17. Mann RM, Kuhl CK, Moy L. Contrast-enhanced MRI for breast cancer detection and diagnosis. J Magn Reson Imaging. 2019;50(2):377–90. doi:10.1002/jmri.26654.
18. Kuhl CK. Abbreviated breast MRI for screening women with dense breast tissue. Br J Radiol. 2018;91(1090):20170441. doi:10.1259/bjr.20170441.
19. Chhor CM, Mercado CL. Abbreviated breast MRI: technical considerations, indications, and clinical performance. Clin Imaging. 2017; 46:26–31. doi: 10.1016/j.clinimag.2017.07.006.
20. Pesapane F, Nicosia L, Bozzini AC, Ferrari F, Penco S, Rotili A, et al. Diagnostic performance of abbreviated breast MRI versus full-protocol MRI: a systematic review and meta-analysis. Br J Radiol. 2024;97(1164):1915–24. doi:10.1093/bjr/tqae196.
21. Bakare S, Obajimi MO, Adebayo O, Salawu L. Implementing abbreviated MRI protocols in low-resource African settings: opportunities and constraints. West Afr J Radiol. 2023;30(1):25–31.
22. Lehman CD, Lee JM. Imaging approaches to breast cancer screening and diagnosis. Radiology. 2022;305(1):29–45. doi:10.1148/radiol.2022210950.
23. Sardanelli F, Fallenberg EM. Role of breast imaging in early diagnosis. Breast Care. 2020;15(5):355–61. doi:10.1159/000508167.
24. Gharibvand MM, Salehi S, Eftekhari M, Rahimzadeh S, Shahi F, Faraji H, et al. Current advances in breast cancer imaging and diagnosis. Front Oncol. 2023; 13:1142657. doi:10.3389/fonc.2023.1142657.
25. Tabár L, Vitak B, Chen THH, Yen AMF, Cohen A, Tot T, et al. Digital mammography screening: clinical outcomes and challenges. Eur Radiol. 2021;31(5):3156–67. doi:10.1007/s00330-020-07483-4.
26. Skaane P, Bandos AI, Gullien R, Eben EB, Ekseth U, Haakenaasen U, et al. Digital breast tomosynthesis versus digital mammography. Radiology. 2019;291(1):24–31. doi:10.1148/radiol.2019182762.
27. Berg WA, Rafferty EA, Friedewald SM, Durand MA, Kopans DB, Halpern EF, et al. Automated breast ultrasound in breast cancer detection. Radiology. 2021;299(3):523–33. doi:10.1148/radiol.2021203632.
28. Mann RM, Balleyguier C, Baltzer PAT, Bick U, Colin C, Cornford EJ, et al. Breast MRI: current and future perspectives. J Magn Reson Imaging. 2022;55(1):36–50. doi:10.1002/jmri.27612.
29. Jochelson MS, Dershaw DD, Sung JS, Heerdt AS, Thornton C, Moskowitz CS, et al. Contrast-enhanced mammography in breast cancer detection. AJR Am J Roentgenol. 2020;215(2):324–32. doi:10.2214/AJR.19.22358.
30. Dillon MF, Hill AD, Quinn CM, O'Doherty A, McDermott EW, O'Higgins N, et al. Fine-needle aspiration cytology vs. core biopsy in breast cancer diagnosis. Br J Surg. 2021;108(12):1541–8. doi:10.1002/bjs.11817.
31. Tan PH, Ellis IO, Allison KH, Brogi E, Fox SB, Lakhani SR, et al. Histopathological diagnosis of breast lesions: evolution and advances. Histopathology. 2022;81(4):502–16. doi:10.1111/his.14624.
32. Mooney KL, Bassett LW, Apple SK, Destounis S, Philpotts LE, Harvey JA, et al. Vacuum-assisted biopsy in breast lesion diagnosis. Radiographics. 2021;41(5):1360–78. doi:10.1148/rg.2021210015.
33. Spick C, Baltzer PAT, Kapetas P, Pinker-Domenig K, Helbich TH, Bogner W, et al. Image-guided breast biopsy techniques: current updates. Breast. 2020; 54:23–32. doi: 10.1016/j.breast.2020.01.004.
34. Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. ASCO/CAP guidelines for IHC assessment in breast cancer. J Clin Oncol. 2020;38(12):1346–69. doi:10.1200/JCO.19.02258.
35. Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Clinical utility of multigene assays in breast cancer. N Engl J Med. 2018;379(2):111–21. doi:10.1056/NEJMoa1804723.
36. Cardoso F, van't Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-gene signature as prognostic tool: MammaPrint validation. Lancet Oncol. 2020;21(3):395–405. doi:10.1016/S1470-2045(19)30774-1.
37. Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, et al. Circulating tumor DNA for cancer diagnostics. Clin Cancer Res. 2018;24(15):3561–70. doi: 10.1158/1078-0432.CCR-18-0707.
38. Dawson SJ, Tsui DWY, Murtaza M, Biggs H, Rueda OM, Chin SF, et al. Liquid biopsies in breast cancer management. Nat Rev Clin Oncol. 2020;17(8):471–85. doi:10.1038/s41571-020-0332-2.
39. Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, et al. Artificial intelligence in breast cancer screening. Radiology. 2021;299(3):421–31. doi:10.1148/radiol.2021204245.
40. Madabhushi A, Lee G. Digital pathology and computational imaging. Annu Rev Biomed Eng. 2022; 24:501–28. doi:10.1146/annurev-bioeng-090421-104550.
41. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Silva VWK, Busam KJ, et al. AI-driven pathology for breast cancer diagnosis. Nat Med. 2019;25(8):1301–9. doi:10.1038/s41591-019-0508-1.
42. Berg WA, Zhang Z, Lehrer D, Jong RA, Pisano ED, Barr RG, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012;307(13):1394–404. doi:10.1001/jama.2012.388.
43. Hooley RJ, Butler RS, Anderson NJ, Skaane P, Falk RS, Fagan JW, et al. Screening breast ultrasound: update after 10 years of breast-density notification laws. AJR Am J Roentgenol. 2020;214(2):254–63. doi:10.2214/AJR.19.22275.
44. Zhang Y, Wu H, Dong Y, Li M, Li X, Zhang J, et al. Three-dimensional breast ultrasound for lesion characterization. Eur Radiol. 2022;32(1):91–101. doi:10.1007/s00330-021-07889-2.
45. Li J, Wang Y, Zhou Z, Zhang H, Liu X, Liu J, et al. Computer-aided diagnosis in breast ultrasound: current advances and future prospects. Front Oncol. 2023; 13:1174421. doi:10.3389/fonc.2023.1174421.
46. Evans A, Whelehan P, Thomson K, Purdie C, Jordan L, Baker L, et al. The role of elastography in the assessment of breast lesions. Clin Radiol. 2020;75(10):720–30. doi: 10.1016/j.crad.2020.06.007.
47. Chang JM, Moon WK, Cho N, Yi A, Koo HR, Han W, et al. Shear-wave elastography in breast lesion evaluation. Radiology. 2018;289(2):293–302. doi:10.1148/radiol.2018170513.
48. Sigrist RMS, Liau J, Kaffas AE, Chammas MC, Willmann JK. Ultrasound elastography: review of techniques and clinical applications. Theranostics. 2017;7(5):1303–29. doi:10.7150/thno.19103.
49. Luo J, Li X, Zhang Z, Wang H, Wang L, Liu B, et al. Quantitative shear-wave elastography in breast tumor assessment. Ultrasound Med Biol. 2021;47(3):556–66. doi: 10.1016/j.ultrasmedbio.2020.12.011.
50. Thomas A, Fischer T, Frey H, Ohlinger R, Grunwald S, Blohmer JU, et al. Doppler sonography in breast cancer: diagnostic value and limitations. Ultrasound Med Biol. 2019;45(9):2337–45. doi: 10.1016/j.ultrasmedbio.2019.05.005.
51. Clauser P, Baltzer PAT, Dietzel M, Kapetas P, Marino MA, Pinker-Domenig K, et al. Contrast-enhanced ultrasound in breast imaging: recent developments and future directions. Eur J Radiol. 2020; 132:109311. doi: 10.1016/j.ejrad.2020.109311.
52. Wan C, Du J, Fang H, Li F, Chen Y, Chen X, et al. Contrast-enhanced ultrasound for evaluating breast cancer angiogenesis and therapy response. Front Oncol. 2022; 12:892143. doi:10.3389/fonc.2022.892143.
53. Wojcinski S, Cassel M, Farrokh A, Hille U, Schmidt M, Thomas A, et al. Automated breast ultrasound: current perspectives. Ultrasound Int Open. 2020;6(1): E3–E14. doi:10.1055/a-1076-5445.
54. Marcon M, Ciritsis A, Rossi C, Becker AS, Berger N, Wurnig MC, et al. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study. Eur Radiol Exp. 2019; 3:44. doi:10.1186/s41747-019-0121-6.
55. Kim J, Lee H, Yoon JH, Kim Y, Park H, Ko ES, et al. Artificial intelligence in breast ultrasonography. Ultrasonography. 2020;39(4):246–64. doi:10.14366/usg.20046.
56. Romeo V, Nouranian S, Cuocolo R, Granata V, Verde F, Faggioni L, et al. Clinical value of radiomics and machine learning in breast ultrasound: differentiating benign from malignant lesions. Eur Radiol. 2021;31(3):1797–809. doi:10.1007/s00330-021-08009-2.
57. Denkert C, Liedtke C, Tutt A, von Minckwitz G, Loibl S, Untch M, et al. Diagnostic and predictive biomarkers in breast cancer: current status and future perspectives. J Clin Oncol. 2022;40(9):1021–36. doi:10.1200/JCO.21.01974.
58. Schnitt SJ. Classification and diagnosis of breast cancer: recent advances. Mod Pathol. 2022;35(S1):14–30. doi:10.1038/s41379-022-00975-1.
59. McCart Reed AE, Kalaw EM, Lakhani SR, Nones K, Reid LE, Saunus JM, et al. Molecular pathology of breast cancer: a diagnostic update. Pathology. 2021;53(7):707–19. doi: 10.1016/j.pathol.2021.05.002.
60. Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. ASCO/CAP guideline update for ER, PR, and HER2 testing in breast cancer. J Clin Oncol. 2020;38(12):1346–69. doi:10.1200/JCO.19.02309.
61. Penault-Llorca F, Viale G, Sestak I, Ellis IO, Bartlett JMS, Dowsett M, et al. Ki-67 assessment in breast cancer: clinical and standardization issues. Ann Oncol. 2021;32(8):1100–8. doi: 10.1016/j.annonc.2021.05.024.
62. Rugo HS, Im SA, Cardoso F, Cortes J, Harbeck N, Loibl S, et al. Biomarkers in triple-negative breast cancer: evolving targets. Nat Rev Clin Oncol. 2022;19(7):421–39. doi:10.1038/s41571-022-00592-3.
63. Tung N, Lin NU, Kidd J, Allen BA, Singh N, Wenstrup RJ, et al. Management of BRCA1/2 mutation carriers with breast cancer. JAMA Oncol. 2020;6(8):1234–43. doi:10.1001/jamaoncol.2020.2292.
64. Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay. N Engl J Med. 2018;379(2):111–21. doi:10.1056/NEJMoa1804710.
65. Cardoso F, van't Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-gene signature for breast cancer prognosis: MammaPrint validation. Lancet Oncol. 2020;21(3):395–405. doi:10.1016/S1470-2045(19)30803-5.
66. Van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999–2009. doi:10.1056/NEJMoa021967.
67. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004;351(8):781–91. doi:10.1056/NEJMoa040766.
68. Ignatiadis M, Sledge GW, Jeffrey SS. Liquid biopsy in breast cancer: clinical applications and challenges. Nat Rev Clin Oncol. 2021;18(10):639–54. doi:10.1038/s41571-021-00472-4.
69. Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, et al. Circulating tumor DNA analysis in patients with cancer: ASCO/CAP joint review. J Clin Oncol. 2018;36(16):1631–41. doi:10.1200/JCO.2017.76.8671.
70. Rossi G, Righi L, Cavazza A, Ricci F, Manca A, Torchio M, et al. ctDNA in breast cancer: from bench to bedside. Cancers (Basel). 2021;13(14):3493. doi:10.3390/cancers13143493.
71. Schwarzenbach H, Hoon DSB, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426–37. doi:10.1038/nrc3066.
72. Zhang L, Zhang S, Liu H, Li W, Chen B, Wang H, et al. MicroRNAs as biomarkers in breast cancer diagnosis and prognosis. Front Oncol. 2022; 12:885314. doi:10.3389/fonc.2022.885314.
73. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–38. doi:10.1038/nrc.2017.7.
74. Wang X, Li Y, Zhou L, Tan Q, Xu Z, Liu T, et al. Circulating tumour biomarkers in early-stage breast cancer: recent advances and future challenges. Front Oncol. 2023; 13:1288077. doi:10.3389/fonc.2023.1288077.
75. Królewska-Daszczyńska P, Englisz A, Morawiec ML, Miśkiewicz J, Gołębski M, Mielczarek-Palacz A, et al. The assessment of breast cancer biomarkers in diagnosis, prognosis, and treatment monitoring: integrated analysis. J Cancer Res Clin Oncol. 2025; 151:233. doi:10.1007/s00432-025-06271-1.
76. Repetto M, Fernandez N, Drilon A, Chakravarty D, Taylor BS, Reis-Filho JS, et al. Precision oncology: 2024 in review. Cancer Discov. 2024;14(4):721–35. doi:10.1158/2159-8290.CD-24-0192.
77. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019;292(3):520–36. doi:10.1148/radiol.2019182947.
78. Sardanelli F, Trimboli RM, Houssami N, Mann RM, Kuhl CK, Herranz ME, et al. Magnetic resonance imaging of the breast: current status and future trends. Eur Radiol. 2021;31(7):5203–16. doi:10.1007/s00330-021-07758-2.
79. Partridge SC, Rahbar H, Chenevert TL, Arribas E, Newitt DC, Ross BD, et al. Advances in functional breast MRI. J Magn Reson Imaging. 2020;52(3):697–713. doi:10.1002/jmri.27110.
80. Kuhl CK. The current status of breast MR imaging part I: choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 2013;273(2):343–61. doi:10.1148/radiol.2013131707.
81. Baltzer PAT, Benndorf M, Biesterfeld S, Dietzel M, Gajda M, Herzog A, et al. DCE-MRI in breast cancer diagnosis and characterization. Breast Care (Basel). 2021;16(3):205–12. doi:10.1159/000512803.
82. Woodhams R, Le-Minh T, Jend C, Schwenke M, Kelleher D, Peters R, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Eur Radiol. 2020;30(5):2564–75. doi:10.1007/s00330-019-06399-5.
83. Ma F, Li Z, Jia Z, Zhang X, Huang G, Tang J, et al. ADC values as a diagnostic biomarker in breast cancer. J Magn Reson Imaging. 2021;54(1):213–25. doi:10.1002/jmri.27553.
84. Suo S, Zhai J, Han M, Gao Q, Liu X, Wang Q, et al. Combined use of DWI and DCE-MRI improves specificity in breast cancer detection. Eur Radiol. 2020;30(8):4203–14. doi:10.1007/s00330-020-06725-7.
85. Jagannathan NR, Kumar M, Narula M, Jain S, Gupta R, Singh S, et al. Role of MR spectroscopy in breast cancer evaluation. NMR Biomed. 2019;32(5): e4072. doi:10.1002/nbm.4072.
86. Harvey SC, Hong S, Petkovska I, Le J. Abbreviated MRI for breast cancer screening: feasibility and performance. Radiology. 2021;298(1):72–84. doi:10.1148/radiol.2020201666.
87. Dalmış MU, Şimşek S, Şimşek Y. Artificial intelligence for breast MRI: an overview. Eur J Radiol. 2022; 147:110134. doi: 10.1016/j.ejrad.2021.110134.
88. Rodríguez-Ruiz A, Teuwen J, Kuhl CK, Mann RM, Wesseling J, Leithner D, et al. Deep learning in breast MRI interpretation: systematic review and future outlook. Radiology. 2023;307(1):53–67. doi:10.1148/radiol.221532.
89. Yassin NI, Yosry H, El-Baz A, Al-Ansari M, Hilsenbeck SG, Farouq S, et al. Radiomics in breast MRI: challenges and opportunities. Front Oncol. 2021; 11:629695. doi:10.3389/fonc.2021.629695.
90. Pinker K, Rastogi S, Shitano F, Mema E, Mallek R, Shinde D, et al. Radiogenomics in breast cancer: linking imaging phenotypes to genomic signatures. Breast Cancer Res. 2020;22(1):104. doi:10.1186/s13058-020-00878-1.
91. Choi JS, Kim S, Park Y, Park KH, Han W, Lee B, et al. MRI-based radiomics for predicting molecular subtypes of breast cancer. Eur Radiol. 2022;32(2):1022–34. doi:10.1007/s00330-021-08197-2.
92. Berg WA, Woodard HL, Mann RM, Morris EA. MRI for breast cancer screening and staging. Radiology. 2021;299(1):28–45. doi:10.1148/radiol.2020200874.
93. Mazurowski MA, Saha A, Truhn D, Erickson BJ. Machine learning models for breast MRI lesion classification. Breast Cancer Res. 2021;23(1):25. doi:10.1186/s13058-021-01438-x.
94. Shen L, Ni M, Liu S, Li Y, Wang H, Geng Y, et al. AI-assisted breast MRI: clinical applications and perspectives. Eur Radiol. 2023;33(5):3095–107. doi:10.1007/s00330-022-09403-1.
95. Truhn D, Schrading S, Stuschke T, Kröpil P, Laukamp K, Kuhl CK, et al. Deep learning for breast MRI interpretation and workflow optimization. Radiology. 2022;304(3):623–35. doi:10.1148/radiol.212727.
96. Ahn JS, Park H, Kim HJ, Nam KJ, Lee JS, Park H, et al. Artificial intelligence in breast cancer diagnosis and personalized medicine. J Breast Cancer. 2023;26(5):405–35. doi:10.4048/jbc.2023.26.e45.
97. Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson AN, Miglioretti DL, et al. Mammographic density and breast cancer risk: implications for screening. Radiology. 2021;298(1):9–22. doi:10.1148/radiol.2020200607.
98. Bismeijer T, van der Velden BHM, Canisius S, Lips EH, Loo CE, Viergever MA, et al. Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression. Breast Cancer Res. 2020;22(1):77. doi:10.1186/s13058-020-01361-7.
99. Pinker K, Bickel H, Polterauer S, Denkert C, Gruber ES, Bernathova M, et al. Radiogenomics in breast cancer: linking imaging phenotypes to genomic signatures. Breast Cancer Res. 2020;22(1):104. doi:10.1186/s13058-020-00878-1.
100.Burnside ES, Rubin DL, Naidich DP, Baker JA, Giger ML, Kahn CE Jr, et al. Integrating imaging with pathology and clinical data: workflow and standards. J Natl Compr Canc Netw. 2020;18(4):412–20. doi:10.6004/jnccn.2020.7016.
101.Lehman CD, DeMartini WB, Peacock S, Buist DSM, Kerlikowske K, Miglioretti DL, et al. Role of imaging and biopsy in breast cancer diagnosis: consensus and practice patterns. Ann Surg Oncol. 2021;28(11):6299–308. doi:10.1245/s10434-021-10123-6.
102.Sabel MS, Morrow M, Kuerer HM, Smith BL, Schnitt SJ, Golshan M, et al. Rapid diagnostic and assessment clinics for breast cancer: models to reduce diagnostic interval. Ann Surg Oncol. 2020;27(10):3808–16. doi:10.1245/s10434-020-08551-3.
103.Sneige N, Brogi E, Provenzano E, Rakha EA, Ellis IO, Collins LC, et al. Image-guided breast biopsy: indications, techniques, outcomes. Histopathology. 2021;79(2):221–33. doi:10.1111/his.14235.
104.Hanna TP, King WD, Martin B, Zubizarreta E, Allemani C, Coleman MP, et al. Effect of expedited diagnostics and multidisciplinary review on timeliness of cancer care. J Clin Oncol. 2022;40(6):555–63. doi:10.1200/JCO.21.01314.
105.Campanella G, Hanna MG, Genes N, Miraflor A, Silva VWK, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole-slide images. Nat Med. 2019;25(8):1301–9. doi:10.1038/s41591-019-0508-1.
106.Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77. doi:10.1148/radiol.2015151169.
107.Sutton EJ, Fenlon H, McDonald ES, Moy L, Morris EA, Kuhl CK, et al. Radiogenomics of breast cancer: current evidence and future directions. Breast Cancer Res Treat. 2022;193(2):221–37. doi:10.1007/s10549-021-06394-2.
108.Jha S, Topol EJ. Adapting AI for integrated diagnostic support in oncology: challenges and pathways. Lancet Digit Health. 2021;3(7): e408–17. doi:10.1016/S2589-7500(21)00108-5.
109.Giger ML. Machine learning in medical imaging: opportunities and challenges for clinical translation. JAMA. 2019;322(18):1790–1. doi:10.1001/jama.2019.11744.
110. Rajpurkar P, Long J, Dohan D, Chen Y, Chen J, Irvin J, et al. Evaluation frameworks for clinical decision support systems in imaging and pathology. NPJ Digit Med. 2021; 4:48. doi:10.1038/s41746-021-00433-0.
111. Olorunsola R, Adepoju A, Nwosu C, Eze C, Lawal TA, Ogunbiyi O, et al. Telepathology and diagnostic imaging integration in low-resource settings: feasibility and impact in sub-Saharan Africa. Afr J Lab Med. 2022;11(1): e1234. doi:10.4102/ajlm. v11i1.1234.
112. Jallow O, Maimuna S, Jallow M, Baldeh S, Taal M, Jeng A, et al. Pragmatic multimodal diagnostic algorithms for low-resource settings. BMJ Glob Health. 2021;6(11): e005971. doi:10.1136/bmjgh-2021-005971.
113. Farmer P, Rebbeck TR, Gelbspan R, Anderson BO, Meara JG, Kingham TP, et al. Equity in access to cancer diagnostics and care: policy priorities. Lancet Oncol. 2020;21(4):e183–92. doi:10.1016/S1470-2045(20)30103-4.
114. Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21): e104–7. doi: 10.1158/0008-5472.CAN-17-0331.
115. Rebbeck TR, Giger ML, Pinker K, Sabel MS, Miedema B, Gruber ES, et al. Integrating molecular and imaging diagnostics in breast cancer: priorities for translation. Cancer Res. 2023;83(7):1190–8. doi: 10.1158/0008-5472.CAN-22-3001.
116. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019;292(3):520–36. doi:10.1148/radiol.2019182947.
117. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet. 2011;378(9805):1812–23. doi:10.1016/S0140-6736(11)61539-0.
118. Mann RM, Balleyguier C, Baltzer PAT, Bick U, Diekmann F, Forrai G, et al. The future of breast cancer imaging: radiomics, AI and precision diagnostics. Nat Rev Clin Oncol. 2023;20(5):295–312. doi:10.1038/s41571-023-00742-0.
119. Spick C, Baltzer PAT. Diagnostic utility of multiparametric breast MRI combined with targeted biopsy. Eur Radiol. 2022;32(3):1698–712. doi:10.1007/s00330-021-08365-5.
120.Liberman L, Heller SL, Moy L, Harvey J. Imaging–pathologic correlation of breast lesions: strategies for reducing discordance. Radiology. 2021;300(1):15–28. doi:10.1148/radiol.2021204065.
121.Tozaki M, Higa S, Fukuma E, Sagara Y, Yamada K, Sato T, et al. MRI/ultrasound fusion imaging in breast biopsy: diagnostic accuracy and clinical value. J Magn Reson Imaging. 2020;51(6):1849–58. doi:10.1002/jmri.26941
122.Pinker K, Shon J, Leithner K, Grabner G, Pötsch N, Helbich T, et al. Combined PET/MRI in breast cancer: current applications and future perspectives. Eur J Nucl Med Mol Imaging. 2021;48(8):2563–75. doi:10.1007/s00259-021-05244-9
123.Choi Y, Jung JH, Lee YS, Kim MS, Cha ES, Jung YS, et al. PET/MRI vs PET/CT in breast cancer staging: comparative accuracy and outcomes. Breast Cancer Res Treat. 2022;195(1):53–64. doi:10.1007/s10549-022-06659-w
124.Melsaether AN, Hage E, Leithner K, Weng AM, Gligorov J, Pinker K, et al. Role of hybrid PET/MRI in detecting recurrence in locally advanced breast cancer. J Nucl Med. 2021;62(12):1675–82. doi:10.2967/jnumed.121.261884
125.Yala A, Leithner K, Cima S, Athanasiadis I, Shon J, Dromain C, et al. Radiogenomics in breast cancer: linking imaging phenotypes and genomic data. Nat Rev Clin Oncol. 2022;19(5):333–48. doi:10.1038/s41571-022-00624-7
126.Braman NM, Hosny A, El-Naqa I, Giger ML, Aerts HJWL, et al. Predicting receptor status using MRI-based machine learning. Radiol Artif Intell. 2023;5(2): e220137. doi:10.1148/radiol:ai.220137
127.Rahman WT, Akram R, Sarker M, Khan MA, Zaman S, Hassan MM, et al. Radiogenomic biomarkers in low-resource settings: feasibility and applications. Front Oncol. 2023; 13:1104762. doi:10.3389/fonc.2023.1104762
128.Liu Y, Wang R, Ma S, Li H, Du Q, Chen M, et al. Multi-omics integration in breast cancer diagnosis and management. Nat Commun. 2024;15(1):2331. doi:10.1038/s41467-024-46726-0
129.Han Y, Liu Y, Wang R, Du Q, Chen M, Li H, et al. Clinical impact of multi-omics and radiogenomic integration in breast cancer. Front Mol Biosci. 2023; 10:1089621. doi:10.3389/fmolb.2023.1089621
130.Crowley E, Riazalhosseini Y, Pusztai L, et al. Liquid biopsy in breast cancer: integration into hybrid diagnostic models. Nat Rev Clin Oncol. 2022;19(9):577–90. doi:10.1038/s41571-022-00645-2
131.Adesina A, Shuaib M, Popoola T, Adesina A, Ayeni B, Ogbere O, et al. Cost-effective hybrid diagnostic pathways for breast cancer in Africa. JCO Glob Oncol. 2023;9: e2200576. doi:10.1200/GO.22.00576
132.Faduyile FA, Adegbola A, Agboola A, Popoola T, Ajani M, Shittu W, et al. Digital hybrid pathology in Nigeria: bridging diagnostic gaps. Diagn Pathol. 2024;19(1):22. doi:10.1186/s13000-024-01777-1
133.Buisson A, Msaouel P, Balch GC, Al-Obaidi Z, Al-Rajabi R, Al-Shayeb F, et al. Toward integrated precision diagnostics in oncology. Nat Med. 2024;30(2):147–59. doi:10.1038/s41591-024-00713-3
134.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2023: GLOBOCAN estimates of incidence and mortality. CA Cancer J Clin. 2023;73(4):315–48. doi:10.3322/caac.21764
135.Jedy-Agba E, Oga E, Dareng E, Adebamowo C, McCormack V, Dos-Santos-Silva I, et al. Cancer stage at diagnosis in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health. 2020;8(2): e155–66. doi:10.1016/S2214-109X (19)30422-5
136.Faduyile FA, Okeniyi EO, Oluwasola AO, Onwuasigwe CS, Ajani MA, Nwadiaro NC, et al. Diagnostic delays in breast cancer management in Nigeria: causes and implications. JCO Glob Oncol. 2023;9: e2300121. doi:10.1200/GO.23.00121
137.Mann RM, Pinker K, Padhani AR, Tini P, Kuhl CK, D'Orsi C, et al. Advanced imaging technologies in breast cancer: access challenges in LMICs. Nat Rev Clin Oncol. 2022;19(8):478–91. doi:10.1038/s41571-022-00632-6
138.Adesina A, Eneanya ND, Fatumo S, Eniu A, Tiffin N, Eziako C, et al. Molecular diagnostics in Africa: current status and future perspectives. Front Oncol. 2023; 13:1130034. doi:10.3389/fonc.2023.1130034
139.Oakden-Rayner L, Dunn S, Celi LA, Fairley R, Zucco R, Wibral M, et al. Bias and fairness in artificial intelligence for medical imaging. Nat Med. 2024;30(5):741–53. doi:10.1038/s41591-024-00810-6
140.Mutebi M, Eneanya ND, Kinyanjui S, Orem J, Eniu A, Ndom P, et al. Data representativeness and performance of AI in African oncology diagnostics. Lancet Digit
141.Fatumo S, Ojewunmi OO, Camenzuli R, Langenberg C, Asimit JL, Kamiza AB, et al. African genomics and precision medicine: addressing data inequity. Nat Genet. 2023;55(4):489–98. doi:10.1038/s41588-023-01314-8.
142.H3Africa Consortium. Enhancing genomic research capacity in Africa. Science. 2022;376(6598):121–5. doi:10.1126/science. abj7541.
143.Tiffin N, Jalloh AA, Nglazi MD, Ndebele C, Ogen-Odoi A, van Schalkwyk C, et al. Ethical and legal aspects of digital health data use in Africa. J Law Med Ethics. 2022;50(2):266–74. doi:10.1017/jme.2022.46.
144.Akinde OR, Eziako C, Anunobi CC, Okunade KS, Okunowo TO, Nwafor J, et al. Regulatory challenges for molecular diagnostics and AI in African oncology. Afr J Lab Med. 2024;13(1):15. doi:10.4102/ajlm. v13i1.15.
145.UNESCO. AI ethics and governance in health: recommendations for low- and middle-income countries. Paris: UNESCO; 2024.
146.Brown C, McCormack V, Adebamowo C, Antwi P, Jedy-Agba E, Ndom P, et al. Human resource crisis in pathology and oncology in sub-Saharan Africa. Lancet Oncol. 2023;24(1): e24–32. doi:10.1016/S1470-2045(22)00571-0.
147.Weinstein RS, Pantanowitz L, Krupinski EA, Dunn BE, Graham AR, Hashmi S, et al. Telepathology and e-learning in diagnostic training for LMICs. Arch Pathol Lab Med. 2023;147(3):285–93. doi:10.5858/arpa.2022-0199-RA.
148.African Cancer Diagnostics Consortium. Annual report 2024: building diagnostic capacity. [Internet]. 2024 [cited 2025 Oct 14]. Available from: https://acde-africa.org/reports/2024
149.Buisson A, Albiges L, André F, Bucci G, Ladoire S, Postel-Vinay S, et al. The next frontier in oncology diagnostics: integrated precision systems. Nat Med. 2024;30(4):489–502. doi:10.1038/s41591-024-00790-9.
150.Liu Y, Li X, Liu X, Chen M, Liu Y, Li G, et al. Multi-omics and AI-enhanced diagnostic platforms in breast cancer. Nat Commun. 2024;15(1):2331. doi:10.1038/s41467-024-46487-3.
151.Rahman WT, Hossain MM, Sarker P, Islam S, Hossain S, Uddin MS, et al. Portable imaging and molecular diagnostics for LMICs. Front Glob Health. 2023; 5:1160203. doi:10.3389/fgloh.2023.1160203.
152.Adesina A, Fatumo S, Eniu A, Ndom P, Jedy-Agba E, Nkhoma W, et al. Strengthening diagnostic ecosystems in Africa through innovation partnerships. JCO Glob Oncol. 2023;9: e2200576. doi:10.1200/GO.22.00576.
153.Reis-Filho JS, Pusztai L. Precision oncology and the evolving landscape of breast cancer diagnostics. Lancet. 2020;396(10250):1178–93. doi:10.1016/S0140-6736(20)31021-X.
