Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care
Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Bre...
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2024-11-01
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| author | Petar Uchikov Usman Khalid Granit Harris Dedaj-Salad Dibya Ghale Harney Rajadurai Maria Kraeva Krasimir Kraev Bozhidar Hristov Mladen Doykov Vanya Mitova Maria Bozhkova Stoyan Markov Pavel Stanchev |
| author_facet | Petar Uchikov Usman Khalid Granit Harris Dedaj-Salad Dibya Ghale Harney Rajadurai Maria Kraeva Krasimir Kraev Bozhidar Hristov Mladen Doykov Vanya Mitova Maria Bozhkova Stoyan Markov Pavel Stanchev |
| author_sort | Petar Uchikov |
| collection | DOAJ |
| description | Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the <i>BRCA1</i> and 45% with the <i>BRCA2</i> gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving <i>HER2</i> and <i>Ki67</i> assessments. Personalised medicine benefits from AI’s predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes. |
| format | Article |
| id | doaj-art-4b08230c3fdb4ed4a1464cdc461f36d1 |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-11-01 |
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| series | Life |
| spelling | doaj-art-4b08230c3fdb4ed4a1464cdc461f36d12024-11-26T18:10:28ZengMDPI AGLife2075-17292024-11-011411145110.3390/life14111451Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized CarePetar Uchikov0Usman Khalid1Granit Harris Dedaj-Salad2Dibya Ghale3Harney Rajadurai4Maria Kraeva5Krasimir Kraev6Bozhidar Hristov7Mladen Doykov8Vanya Mitova9Maria Bozhkova10Stoyan Markov11Pavel Stanchev12Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaFaculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, BulgariaFaculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, BulgariaFaculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, BulgariaFaculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSecond Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Urology and General Medicine, Medical Faculty, Medical University of Plovdiv, 4001 Plovdiv, BulgariaUniversity Specialized Hospital for Active Oncology Treatment “Prof. Ivan Chernozemsky”, 1756 Sofia, BulgariaMedical College, Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaClinic of Endocrinology and Metabolic Diseases, St George University Hospital, Medical University of Plovdiv, 4002 Plovdiv, BulgariaBreast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the <i>BRCA1</i> and 45% with the <i>BRCA2</i> gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving <i>HER2</i> and <i>Ki67</i> assessments. Personalised medicine benefits from AI’s predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.https://www.mdpi.com/2075-1729/14/11/1451breast cancerartificial intelligencepathologyimaging |
| spellingShingle | Petar Uchikov Usman Khalid Granit Harris Dedaj-Salad Dibya Ghale Harney Rajadurai Maria Kraeva Krasimir Kraev Bozhidar Hristov Mladen Doykov Vanya Mitova Maria Bozhkova Stoyan Markov Pavel Stanchev Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care Life breast cancer artificial intelligence pathology imaging |
| title | Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care |
| title_full | Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care |
| title_fullStr | Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care |
| title_full_unstemmed | Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care |
| title_short | Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care |
| title_sort | artificial intelligence in breast cancer diagnosis and treatment advances in imaging pathology and personalized care |
| topic | breast cancer artificial intelligence pathology imaging |
| url | https://www.mdpi.com/2075-1729/14/11/1451 |
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