Abstract
Breast cancer is one of the most common types of cancer among women worldwide and is still a major cause of death. More than 2 million women are diagnosed with breast cancer annually. Artificial intelligence has great potential in breast cancer diagnosis, treatment, and management. In addition to traditional imaging techniques, the analysis of genetic data is used by artificial intelligence in the process of breast cancer diagnosis. In addition, artificial intelligence plays an important role in treatment planning and follow-up of patients. Data analytics and extensive data integration also contribute to developments in this field. However, the audit and ethical responsibilities of artificial intelligence applications should be considered.
References
- 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.
- 2. Aresta G, Araújo T, Kwok S, Chennamsetty SS, Safwan M, Alex V, et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal. 2019;56:122-39.
- 3. La Porta CA, Zapperi S. Artificial intelligence in breast cancer diagnostics. Cell Rep Med. 2022;3(12):100851.
- 4. Gouri A, Benarba B, Dekaken A, Aoures H, Benharkat S. Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers. Curr Drug Targets. 2020;21(10):1008-25.
- 5. Wu Q, Li J, Zhu S, Wu J, Chen C, Liu Q, et al. Breast cancer subtypes predict the preferential site of distant metastases: a SEER based study. Oncotarget. 2017;8(17):27990-6.
- 6. Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine. 2021;69:103442.
- 7. Tian L, Zhang D, Bao S, Nie P, Hao D, Liu Y, et al. Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas. Clin Radiol. 2021;76(2):158.e19-.e25.
- 8. Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun K, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11(1):4308.
- 9. Högberg C, Larsson S, Lång K. Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists. BMJ Health Care Inform. 2023;30(1).
- 10. Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303-12.
- 11. Vedantham S, Shazeeb MS, Chiang A, Vijayaraghavan GR. Artificial Intelligence in Breast X-Ray Imaging. Semin Ultrasound CT MR. 2023;44(1):2-7.
- 12. Dang LA, Chazard E, Poncelet E, Serb T, Rusu A, Pauwels X, et al. Impact of artificial intelligence in breast cancer screening with mammography. Breast Cancer. 2022;29(6):967-77.
- 13. Liu Y, Han D, Parwani AV, Li Z. Applications of Artificial Intelligence in Breast Pathology. Arch Pathol Lab Med. 2023;147(9):1003-13.
- 14. Chan RC, To CKC, Cheng KCT, Yoshikazu T, Yan LLA, Tse GM. Artificial intelligence in breast cancer histopathology. Histopathology. 2023;82(1):198-210.
Abstract
Meme kanseri, dünya genelinde kadınlar arasında en yaygın kanser türlerinden biridir ve hala ölüm nedenleri arasında önemli bir yer tutmaktadır. Yılda 2 milyondan fazla kadına meme kanseri teşhisi konmaktadır. Yapay zekâ, meme kanseri teşhisi, tedavisi ve yönetimi konularında büyük bir potansiyele sahiptir. Geleneksel görüntüleme tekniklerinin yanı sıra genetik verilerin analizi, yapay zekâ tarafından meme kanseri teşhisi sürecinde kullanılmaktadır. Ayrıca yapay zekâ, tedavi planlaması ve hastaların takibi süreçlerinde de önemli bir rol oynamaktadır. Veri analitiği ve büyük veri entegrasyonu da bu alandaki gelişmelere katkı sağlamaktadır. Ancak yapay zekâ uygulamalarının denetim ve etik sorumlulukları göz önünde bulundurulmalıdır.
Keywords
References
- 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.
- 2. Aresta G, Araújo T, Kwok S, Chennamsetty SS, Safwan M, Alex V, et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal. 2019;56:122-39.
- 3. La Porta CA, Zapperi S. Artificial intelligence in breast cancer diagnostics. Cell Rep Med. 2022;3(12):100851.
- 4. Gouri A, Benarba B, Dekaken A, Aoures H, Benharkat S. Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers. Curr Drug Targets. 2020;21(10):1008-25.
- 5. Wu Q, Li J, Zhu S, Wu J, Chen C, Liu Q, et al. Breast cancer subtypes predict the preferential site of distant metastases: a SEER based study. Oncotarget. 2017;8(17):27990-6.
- 6. Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine. 2021;69:103442.
- 7. Tian L, Zhang D, Bao S, Nie P, Hao D, Liu Y, et al. Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas. Clin Radiol. 2021;76(2):158.e19-.e25.
- 8. Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun K, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11(1):4308.
- 9. Högberg C, Larsson S, Lång K. Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists. BMJ Health Care Inform. 2023;30(1).
- 10. Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303-12.
- 11. Vedantham S, Shazeeb MS, Chiang A, Vijayaraghavan GR. Artificial Intelligence in Breast X-Ray Imaging. Semin Ultrasound CT MR. 2023;44(1):2-7.
- 12. Dang LA, Chazard E, Poncelet E, Serb T, Rusu A, Pauwels X, et al. Impact of artificial intelligence in breast cancer screening with mammography. Breast Cancer. 2022;29(6):967-77.
- 13. Liu Y, Han D, Parwani AV, Li Z. Applications of Artificial Intelligence in Breast Pathology. Arch Pathol Lab Med. 2023;147(9):1003-13.
- 14. Chan RC, To CKC, Cheng KCT, Yoshikazu T, Yan LLA, Tse GM. Artificial intelligence in breast cancer histopathology. Histopathology. 2023;82(1):198-210.
Details
| Primary Language | English |
|---|---|
| Subjects | General Surgery |
| Journal Section | DERLEME |
| Authors | |
| Publication Date | January 16, 2024 |
| Submission Date | October 19, 2023 |
| Acceptance Date | October 27, 2023 |
| Published in Issue | Year 2024 Volume: 46 Issue: 1 |