Breakthroughs in Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Radiomics and Pathology

Yuxuan Ji

doi:10.54097/15jcd219

Authors

Yuxuan Ji

DOI:

https://doi.org/10.54097/15jcd219

Keywords:

Breast cancer, AI, Deep learning, Pathology, Imaging.

Abstract

As the most common cancer after lung cancer presently, breast cancer needs to gain extensive awareness of both general public and healthcare professionals. With continuous developments of technology researchers on medical science began to attach great significance to the integration of artificial intelligence (AI) and clinical diagnostics. With the purpose of enhancing early diagnosis and prognosis and implementing more effective and timely treatment for breast cancer, increased research efforts are currently aimed at applying AI to breast cancer detection and classification, opening up new avenues for improving patient care. The paper initially discusses the progress of AI in the diagnosis and prognosis of breast cancer and presents an overview of the existing literature of research in this area. It describes the main methods and procedures employed in AI-assisted diagnosis and response prediction with particular emphasis on radiological as well as pathological approaches. Comparative analysis is also provided comparing differences in sensitivity and specificity between clinicians and AI systems when interpreting images. Furthermore, the paper discusses existing challenges in the introduction of AI in clinical practice to assist clinicians’ diagnosis and carry out treatment. The major challenges include requirements for data volumes, standardization and ethical problems. Then, it predicted the future progress of AI technologies in diagnostics and prognosis of breast cancer, providing practical suggestions on promoting applications of AI in diagnosis, therapy and general use.

Downloads

Download data is not yet available.

References

[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: a cancer journal for clinicians, 2024, 74(3): 229-263.

[2] Warwas K M, Meyer M, Gonçalves M, et al. Co-stimulatory bispecific antibodies induce enhanced T cell activation and tumor cell killing in breast cancer models[J]. Frontiers in Immunology, 2021, 12: 719116.

[3] Cong S, Zhou Y. A review of convolutional neural network architectures and their optimizations[J]. Artificial Intelligence Review, 2023, 56(3): 1905-1969.

[4] Masud M, Eldin Rashed A E, Hossain M S. Convolutional neural network-based models for diagnosis of breast cancer[J]. Neural Computing and Applications, 2022, 34(14): 11383-11394.

[5] Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)[J]. Clinical eHealth, 2021, 4: 1-11.

[6] Chaudhary P, Gibbs L D, Maji S, et al. Serum exosomal-annexin A2 is associated with African-American triple-negative breast cancer and promotes angiogenesis[J]. Breast Cancer Research, 2020, 22(1): 11.

[7] Yala A, Mikhael P G, Strand F, et al. Toward robust mammography-based models for breast cancer risk[J]. Science Translational Medicine, 2021, 13(578): eaba4373.

[8] Damiani C, Kalliatakis G, Sreenivas M, et al. Evaluation of an AI model to assess future breast cancer risk[J]. Radiology, 2023, 307(5): e222679.

[9] Dustler M. Evaluating AI in breast cancer screening: a complex task[J]. The Lancet Digital Health, 2020, 2(3): e106-e107.

[10] Philpotts L E, Grewal J K, Horvath L J, et al. Breast cancers detected during a decade of screening with digital breast tomosynthesis: comparison with digital mammography[J]. Radiology, 2024, 312(3): e232841.

[11] Romero-Martín S, Elías-Cabot E, Raya-Povedano J L, et al. Stand-alone use of artificial intelligence for digital mammography and digital breast tomosynthesis screening: a retrospective evaluation[J]. Radiology, 2022, 302(3): 535-542.

[12] Zhou L Q, Wu X L, Huang S Y, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning[J]. Radiology, 2020, 294(1): 19-28.

[13] Jiang Y, Edwards A V, Newstead G M. Artificial intelligence applied to breast MRI for improved diagnosis[J]. Radiology, 2021, 298(1): 38-46.

[14] Gullo R L, Marcus E, Huayanay J, et al. Artificial intelligence-enhanced breast MRI: applications in breast cancer primary treatment response assessment and prediction[J]. Investigative Radiology, 2024, 59(3): 230-242.

[15] Mantrala S, Ginter P S, Mitkari A, et al. Concordance in breast cancer grading by artificial intelligence on whole slide images compares with a multi-institutional cohort of breast pathologists[J]. Archives of pathology & laboratory medicine, 2022, 146(11): 1369-1377.

[16] Aswolinskiy W, Munari E, Horlings H M, et al. PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning[J]. Breast Cancer Research, 2023, 25(1): 142.

[17] Challa B, Tahir M, Hu Y, et al. Artificial intelligence–aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow[J]. Modern Pathology, 2023, 36(8): 100216.

[18] Jarkman S, Karlberg M, Pocevičiūtė M, et al. Generalization of deep learning in digital pathology: experience in breast cancer metastasis detection[J]. Cancers, 2022, 14(21): 5424.

[19] Balkenende L, Teuwen J, Mann R M. Application of deep learning in breast cancer imaging[C]//Seminars in Nuclear Medicine. WB Saunders, 2022, 52(5): 584-596.

[20] You C, Shen Y, Sun S, et al. Artificial intelligence in breast imaging: Current situation and clinical challenges[C]//Exploration. 2023, 3(5): 20230007.

[21] Chang K, Balachandar N, Lam C, et al. Distributed deep learning networks among institutions for medical imaging[J]. Journal of the American Medical Informatics Association, 2018, 25(8): 945-954.

[22] Ueda D, Kakinuma T, Fujita S, et al. Fairness of artificial intelligence in healthcare: review and recommendations[J]. Japanese journal of radiology, 2024, 42(1): 3-15.

[23] Savulescu J, Giubilini A, Vandersluis R, et al. Ethics of artificial intelligence in medicine[J]. Singapore Medical Journal, 2024, 65(3): 150-158.