Kajian Literatur Penapisan Kanker Payudara: Mammografi dan Ultrasonografi

Abd. Rahman

doi:10.47134/acsc.v2i3.8

Authors

Abd. Rahman Universitas Islam Negeri Alauddin Makassar

DOI:

https://doi.org/10.47134/acsc.v2i3.8

Keywords:

Mammografi, Ultrasonografi, Kanker Payudara, Kecerdasan Buatan, Penapisan Kanker

Abstract

Penelitian ini bertujuan untuk menganalisis efektivitas dan perkembangan metode penapisan kanker payudara melalui mammografi dan ultrasonografi (USG), serta meninjau kontribusi teknologi modern seperti contrast-enhanced mammography (CEM), Automated Breast Ultrasound (ABUS), dan kecerdasan buatan (AI) dalam meningkatkan akurasi deteksi dini. Penelitian ini menggunakan pendekatan kualitatif dengan metode deskriptif melalui studi pustaka, dengan sumber data berupa artikel ilmiah, laporan penelitian, dan publikasi akademik terkini dari tahun 2015 hingga 2025. Data dianalisis melalui proses identifikasi tema, reduksi data, kategorisasi konsep, dan penarikan kesimpulan induktif untuk memperoleh pemahaman komprehensif terhadap efektivitas dua modalitas utama skrining kanker payudara. Hasil kajian menunjukkan bahwa mammografi masih menjadi standar emas dalam deteksi dini, namun efektivitasnya meningkat signifikan ketika dikombinasikan dengan USG, khususnya pada wanita dengan densitas payudara tinggi. Inovasi berbasis AI dan CEM terbukti mampu menurunkan tingkat false positive dan meningkatkan sensitivitas serta efisiensi interpretasi citra. Secara teoretis, penelitian ini memperkuat konsep personalized screening berbasis risiko individu, sementara secara praktis, temuan ini mendukung integrasi teknologi cerdas dalam sistem skrining nasional untuk meningkatkan pemerataan diagnosis dan penanganan kanker payudara. Kesimpulannya, pendekatan multimodalitas berbasis AI menjadi arah strategis masa depan dalam deteksi kanker payudara yang lebih akurat, cepat, dan adaptif terhadap kebutuhan pasien.

References

Abraham, D. P. P. (2024). A Methodological Framework for Descriptive Phenomenological Research. Western Journal of Nursing Research, 47. https://doi.org/10.1177/01939459241308071

Ahmad, J., Akram, S., Jaffar, A., Ali, Z., Bhatti, S., Ahmad, A., & Rehman, S. (2024). Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLOS ONE, 19. https://doi.org/10.1371/journal.pone.0304757

Baillie, J. (2019). Commentary: An overview of the qualitative descriptive design within nursing research. Journal of Research in Nursing, 25, 458–459. https://doi.org/10.1177/1744987119881056

Bandaranayake, P. (2024). Application of grounded theory methodology in library and information science research: An overview. Sri Lanka Library Review. https://doi.org/10.4038/sllr.v38i2.70

Berg, W., Bandos, A., Mendelson, E., Lehrer, D., Jong, R., & Pisano, E. (2016). Ultrasound as the primary screening test for breast cancer: Analysis from ACRIN 6666. Journal of the National Cancer Institute, 108(4). https://doi.org/10.1093/jnci/djv367

Bingham, A. (2023). From Data Management to Actionable Findings: A Five-Phase Process of Qualitative Data Analysis. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231183620

Canelo-Aybar, C., Ferreira, D., Ballesteros, M., Posso, M., Montero, N., Solá, I., Saz-Parkinson, Z., Lerda, D., Rossi, P., Duffy, S., Follmann, M., Graewingholt, A., & Alonso-Coello, P. (2021). Benefits and harms of breast cancer mammography screening for women at average risk of breast cancer: A systematic review for the European Commission Initiative on Breast Cancer. Journal of Medical Screening, 28, 389–404. https://doi.org/10.1177/0969141321993866

Chou, C., Hong, Y., Lin, Y., & Lin, P. (2025). Screening of breast cancer in higher-risk Taiwanese women using contrast-enhanced mammography. Heliyon, 11. https://doi.org/10.1016/j.heliyon.2025.e41851

Ciobotaru, A., Corches, C., Gota, D., & Miclea, L. (2025). Deep learning and federated learning in breast cancer screening and diagnosis: A systematic review. IEEE Access, 13, 76322–76351. https://doi.org/10.1109/access.2025.3560211

Ciurescu, S., Cerbu, S., Dima, C., Borozan, F., Pârvănescu, R., Ilaș, D., Cîtu, C., Vernic, C., & Sas, I. (2025). AI in 2D mammography: Improving breast cancer screening accuracy. Medicina, 61. https://doi.org/10.3390/medicina61050809

Coffey, K., & Jochelson, M. (2022). Contrast-enhanced mammography in breast cancer screening. European Journal of Radiology, 156. https://doi.org/10.1016/j.ejrad.2022.110513

Dan, Q., Zheng, T., Liu, L., Sun, D., & Chen, Y. (2023). Ultrasound for breast cancer screening in resource-limited settings: Current practice and future directions. Cancers, 15. https://doi.org/10.3390/cancers15072112

Doyle, L. M. C., Keogh, B., Brady, A. & McCann, M. (2019). An overview of the qualitative descriptive design within nursing research. Journal of Research in Nursing, 25. https://doi.org/10.1177/1744987119880234

Fife, S. G. J. (2024). Deductive Qualitative Analysis: Evaluating, Expanding, and Refining Theory. International Journal of Qualitative Methods, 23. https://doi.org/10.1177/16094069241244856

Granikov, V. H. Q., Crist, E. & Pluye, P. (2020). Mixed methods research in library and information science: A methodological review. Library & Information Science Research, 42(2). https://doi.org/10.1016/j.lisr.2020.101003

Guo, R., Lu, G., Qin, B., & Fei, B. (2018). Ultrasound imaging technologies for breast cancer detection and management: A review. Ultrasound in Medicine & Biology, 44(1), 37–70. https://doi.org/10.1016/j.ultrasmedbio.2017.09.012

Haerunisa, L., & Noviartha, D. (2025). The analysis study of diagnostic performance and accuracy of mammography as screening and diagnostic of breast cancer: A comprehensive systematic review. The International Journal of Medical Science and Health Research. https://doi.org/10.70070/8a3ben43

He, J., Liu, N., & Zhao, L. (2025). New progress in imaging diagnosis and immunotherapy of breast cancer. Frontiers in Immunology, 16. https://doi.org/10.3389/fimmu.2025.1560257

Kalpokaite, N. R., I. (2018). Demystifying Qualitative Data Analysis for Novice Qualitative Researchers. The Qualitative Report. https://doi.org/10.46743/2160-3715/2019.4120

Mahmood, S., Anjum, M., Farooq, F., Gilani, S., Fatima, M., Andlib, S., & Ramzan, H. (2021). Comparison of mammography and ultrasonography for early detection of breast cancer. Pakistan Journal of Medical and Health Sciences. https://doi.org/10.53350/pjmhs211571450

Mandrik, O., Zielonke, N., Meheus, F., Severens, J., Guha, N., Acosta, R., & Murillo, R. (2019). Systematic reviews as a ‘lens of evidence’: Determinants of benefits and harms of breast cancer screening. International Journal of Cancer, 145, 994–1006. https://doi.org/10.1002/ijc.32211

Ohuchi, N., Suzuki, A., Sobue, T., Kawai, M., Yamamoto, S., Zheng, Y., Shiono, Y., Saito, H., Kuriyama, S., Tohno, E., Endo, T., Fukao, A., Tsuji, I., Yamaguchi, T., Ohashi, Y., Fukuda, M., & Ishida, T. (2016). 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. The Lancet, 387, 341–348. https://doi.org/10.1016/s0140-6736(15)00774-6

Pratt, M. (2025). On the Evolution of Qualitative Methods in Organizational Research. Annual Review of Organizational Psychology and Organizational Behavior. https://doi.org/10.1146/annurev-orgpsych-111722-032953

Qi, X., Yi, F., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Guo, J., Wang, J., Guo, Q., Li, J., Chen, Y., Lv, Q., & Yi, Z. (2021). Computer-aided diagnosis of breast cancer in ultrasonography images by deep learning. Neurocomputing, 472, 152–165. https://doi.org/10.1016/j.neucom.2021.11.047

Qureshi, S., Rehman, A., Hussain, L., Sadiq, T., Shah, S., Mir, A., Nadim, M., Williams, D., Duong, T., Chaudhary, Q., Habib, N., Ahmad, A., & Shah, S. (2025). Breast cancer detection using mammography: Image processing to deep learning. IEEE Access, 13, 60776–60801. https://doi.org/10.1109/access.2024.3523745

Rebolj, M., Assi, V., Brentnall, A., Parmar, D., & Duffy, S. (2018). Addition of ultrasound to mammography in the case of dense breast tissue: Systematic review and meta-analysis. British Journal of Cancer, 118, 1559–1570. https://doi.org/10.1038/s41416-018-0080-3

Rella, R., Belli, P., Giuliani, M., Bufi, E., Carlino, G., Rinaldi, P., & Manfredi, R. (2018). Automated breast ultrasonography (ABUS) in the screening and diagnostic setting: Indications and practical use. Academic Radiology, 25(11), 1457–1470. https://doi.org/10.1016/j.acra.2018.02.014

Ren, W., Chen, M., Qiao, Y., & Zhao, F. (2022). Global guidelines for breast cancer screening: A systematic review. The Breast, 64, 85–99. https://doi.org/10.1016/j.breast.2022.04.003

Sahu, A., Das, P., & Meher, S. (2023). Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Physica Medica, 114, 103138. https://doi.org/10.1016/j.ejmp.2023.103138

Shen, X. H. S., Cui, M., Zhao, Q., Guo, Y., Huang, Y., Zhang, W., Chen, S., Zhang, Y., Chen, S., Chen, K., Cheng, W., Zuo, C., Tan, L., Ding, D., Dong, Q., Jeromin, A., Yen, T., Yu, J. (2023). Plasma Glial Fibrillary Acidic Protein in the Alzheimer Disease Continuum: Relationship to Other Biomarkers, Differential Diagnosis, and Prediction of Clinical Progression. Clinical Chemistry. https://doi.org/10.1093/clinchem/hvad018

Shi, J., Li, J., Gao, Y., Chen, W., Zhao, L., Li, N., Tian, J., & Li, Z. (2025). The screening value of mammography for breast cancer: An overview of 28 systematic reviews with evidence mapping. Journal of Cancer Research and Clinical Oncology, 151. https://doi.org/10.1007/s00432-025-06122-z

Sood, R., Rositch, A., Shakoor, D., Ambinder, E., Pool, K., Pollack, E., Mollura, D., Mullen, L., & Harvey, S. (2019). Ultrasound for breast cancer detection globally: A systematic review and meta-analysis. Journal of Global Oncology, 5. https://doi.org/10.1200/jgo.19.00127

Tadesse, G., Tegaw, E., & Abdisa, E. (2023). Diagnostic performance of mammography and ultrasound in breast cancer: A systematic review and meta-analysis. Journal of Ultrasound, 1–13. https://doi.org/10.1007/s40477-022-00755-3

Tsarouchi, M., Hoxhaj, A., & Mann, R. (2023). New approaches and recommendations for risk-adapted breast cancer screening. Journal of Magnetic Resonance Imaging, 58. https://doi.org/10.1002/jmri.28731

Vila-Henninger, L., Dupuy, C., Van Ingelgom, V., Caprioli, M., Teuber, F., Pennetreau, D., Bussi, M., & Gall, C. (2022). Abductive coding: Theory building and qualitative (re)analysis. Sociological Methods & Research, 53, 968–1001. https://doi.org/10.1177/00491241211067508

Yan, F., Huang, H., Pedrycz, W., & Hirota, K. (2023). Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms. Expert Systems with Applications, 227, 120282. https://doi.org/10.1016/j.eswa.2023.120282

Yang, L., Wang, S., Zhang, L., Sheng, C., Song, F., Wang, P., & Huang, Y. (2019). Performance of ultrasonography screening for breast cancer: A systematic review and meta-analysis. BMC Cancer, 20. https://doi.org/10.1186/s12885-020-06992-1

Yuan, W., Hsu, H., Chen, Y., & Wu, C. (2020). Supplemental breast cancer-screening ultrasonography in women with dense breasts: A systematic review and meta-analysis. British Journal of Cancer, 123, 673–688. https://doi.org/10.1038/s41416-020-0928-1

Zhou, J., Zhang, Y., & Shi, S. (2025). Ultrasound elastography: Advances and challenges in early detection of breast cancer. Frontiers in Oncology, 15. https://doi.org/10.3389/fonc.2025.1589142