Pemeriksaan Kasus Chronic Myeloid Leukimia pada Pasien Remaja
Abstract
This study aims to assess patients diagnosed with Chronic Myeloid Leukemia (CML). The research method used is qualitative using a descriptive case study design with participants who are patients diagnosed with CML. The results of the study showed that a 15-year-old girl who came with complaints of weakness since 3 days of SMRS had recurred in the last 5 years. Hematology examination found anemia, leukocytosis with 9% blast cells, and thrombocytosis. The results of the BCR ABL molecular examination were positive for the BCR ABL exon e14a2 gene fusion, so the patient was diagnosed with chronic myeloid leukemia chronic phase. In conclusion, the results of the assessment that had been carried out showed that the patient was diagnosed with chronic myeloid leukemia chronic phase.
Keywords: Chronic Myeloid Leukemia, Chronic Phase, Adolescents
References
Abhishek, A. R. K., Jha, R., Sinha., & Jha, K. (2023). Automated Detection and Classification of Leukemia on a Subject-Independent Test Dataset Using Deep Transfer Learning Supported By Grad-CAM Visualization. Biomed Signal Process Control, 83, 104722. 10.1016/j.bspc.2023.104722
Ahmed, M. N., Afreen, M., Ahmed, M., Sameer., & Ahamed, J. (2023). An Inception V3 Approach for Malware Classification Using Machine Learning and Transfer Learning. International Journal of Intelligent Networks, 4, 11–18. 10.1016/j.ijin.2022.11.005
Anilkumar, V. J., Manoj, M., & Sagi, T. M. (2022). Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning. IRBM, 43(5), 405–413, 10.1016/J.IRBM.2021.05.005
Ariyati, R., Purnamasari, E. R. W., & Istiani, H. G. (2023). Asuhan Keperawatan pada Anak dengan Penyakit Leukemia Limfoblastik Akut dan Masalah Keperawatan Nyeri Akut di Ruang IKA 2 RSPAD Gatot Soebroto. Jurnal Masyarakat Sehat Indonesia, 2(01), 25–35. https://doi.org/10.70304/jmsi.v2i01.31
Chen, G., Kuang, Z., Li F., & Li J. (2023). The Causal Relationship between Gut Microbiota and Leukemia: A Two-Sample Mendelian Randomization Study. Front Microbiol, 22(14), 1293333. 10.3389/fmicb.2023.1293333.
Goktan, H., Kuru, R. D., Yilmaz, S., Alaylioglu, M., Dursun, E., Gezen, D. (2022). Mir210 and BCL2 Expressions in Patients with Acute Myeloid Leukemia/Akut Myeloid Losemi Hastalarinda mikroRNA-210 ve BCL-2 Ekspresyon Seviyeleri. Bezmialem Science, 10(4), 453. https://doi.org/10.14235/bas.galenos.2021.5447
Iezza, M., Cortesi, S., Ottaviani, E., Mancini, M., Venturi, C., Monaldi, C., De Santis, S., Testoni, N., Soverini, S., Rosti, G., Cavo, M., & Castagnetti, F. (2023). Prognosis in Chronic Myeloid Leukemia: Baseline Factors, Dynamic Risk Assessment and Novel Insights. Cells, 12(13), 1–25. https://doi.org/10.3390/cells12131703
Indonesian Pediatric Cancer Registry. (2024). Mengungkap Tantangan dan Peluang dalam Perawatan Kanker Anak di Indonesia: Data IPCAR 2020-2024
Irawan, C., Steven, R., Gunarsa, R. G., & Tenggara, J. B. (2022). Outcome Result of Acute Myeloid Leukemia Undergoing Treatment in Semi-Isolation Chemotherapy Ward. Jurnal Penyakit dalam Indonesia, 9(3), 155-163. https://doi.org/10.7454/jpdi.v9i3.857
Jabbour, E., & Kantarjian, H. (2022). Chronic Myeloid Leukemia: 2022 Update on Diagnosis, Therapy, and Monitoring. American Journal of Hematology, 97(9), 1236–1256. https://doi.org/10.1002/ajh.26642
Kementerian Kesehatan Republik Indonesia. (2023). Pedoman Penemuan Dini Kanker pada Anak
Khalid, R., & Riasat, S. (2023). Molecular Pathogenesis and Treatment Strategies of Chronic Myeloid Leukemia (CML). Sudan Journal of Medical Sciences, 18(4), 525–538. https://doi.org/10.18502/sjms.v18i4.14741
Lan, D., Xiong, J., Zhang, C., & Zhang, X. (2023). Association between Gut Microbiota and Leukemia: A Bidirectional Multivariable Mendelian Randomization Study. Blood Journal American Society of Hematology, 142(1). https://doi.org/10.1182/blood-2023-189071
Looi, W., Zargari, A., Dun, K., Grigoriadis, G., Fedele, P., Gregory, G. P., & Low, M. S. Y. (2022). Concomitant Diagnosis of Chronic Myeloid Leukaemia and Myeloma. Pathology. 54(4), 493-495. 10.1016/j.pathol.2021.08.007
Mulya, R. F., Utami, E., & Ariatmanto, D. (2023). Classification of Acute Lymphoblastic Leukemia Based on White Blood Cell Images Using InceptionV3 Model. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 947 - 952. https://doi.org/10.29207/resti.v7i4.5182
Nagiub, K. F., Hussain, N., Omar, O., & Taher, Q. (2022). ALL-018 Application of Machine Learning in the Diagnosis of Acute Lymphoblastic Leukemia. Clin Lymphoma Myeloma Leuk, 22(S190). 10.1016/S2152-2650(22)01174-0
Pagani, I. S., Poudel, G., & Wardill, H. R. (2022). A Gut Instinct on Leukaemia: A New Mechanistic Hypothesis for Microbiota-Immune Crosstalk in Disease Progression and Relapse. Microorganisms. 10(4), 713. 10.3390/microorganisms10040713
Rinaldi, I., & Winston, K. (2023). Chronic Myeloid Leukemia, from Pathophysiology to Treatment-Free Remission: A Narrative Literature Review. Journal of Blood Medicine, 14(April), 261–277. https://doi.org/10.2147/JBM.S382090
Sarkar, A. T., Das, G., Das., & Ghosh, Z. (2023). MicroRNA Mediated Gene Regulatory Circuits Leads to Machine Learning Based Preliminary Detection of Acute Myeloid Leukemia. Comput Biol Chem, 104, 107859. 10.1016/j.compbiolchem.2023.107859
Setiya, D. F. J., Kusnan, D. A. P., & Handayani, E. S. (2024). Peran Microbiome Gut Axis dalam Perkembangan dan Kekambuhan Chronic Myeloid Leukemia: Systematic Review. Jurnal Media Akademik, 2(7), 1-12. https://doi.org/10.62281/v2i7.674
Urgessa, F., Lengiso, B., Tsegaye, A., Gebremedhin, A., Abdella, F., Tadesse, F., Radich, J., Nigussie, H., & Kuru Gerbaba, T. (2024). Hematological, Clinical, cytogenetic and Molecular Profiles of Confirmed Chronic Myeloid Leukemia Patients At Presentation At A Tertiary Care Teaching Hospital In Addis Ababa, Ethiopia: A Cross-Sectional Study. BMC Cancer, 24(1), 1–10. https://doi.org/10.1186/s12885-024-12282-x
Xu, J. H., Jia, H. H., Jeong, J., & Zhao, Z. (2023). Deep Learning for Detecting and Elucidating Human T-Cell Leukemia Virus Type 1 Integration in The Human Genome. Patterns, 4(2), 100674, 10.1016/j.patter.2022.100674
Ahmed, M. N., Afreen, M., Ahmed, M., Sameer., & Ahamed, J. (2023). An Inception V3 Approach for Malware Classification Using Machine Learning and Transfer Learning. International Journal of Intelligent Networks, 4, 11–18. 10.1016/j.ijin.2022.11.005
Anilkumar, V. J., Manoj, M., & Sagi, T. M. (2022). Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning. IRBM, 43(5), 405–413, 10.1016/J.IRBM.2021.05.005
Ariyati, R., Purnamasari, E. R. W., & Istiani, H. G. (2023). Asuhan Keperawatan pada Anak dengan Penyakit Leukemia Limfoblastik Akut dan Masalah Keperawatan Nyeri Akut di Ruang IKA 2 RSPAD Gatot Soebroto. Jurnal Masyarakat Sehat Indonesia, 2(01), 25–35. https://doi.org/10.70304/jmsi.v2i01.31
Chen, G., Kuang, Z., Li F., & Li J. (2023). The Causal Relationship between Gut Microbiota and Leukemia: A Two-Sample Mendelian Randomization Study. Front Microbiol, 22(14), 1293333. 10.3389/fmicb.2023.1293333.
Goktan, H., Kuru, R. D., Yilmaz, S., Alaylioglu, M., Dursun, E., Gezen, D. (2022). Mir210 and BCL2 Expressions in Patients with Acute Myeloid Leukemia/Akut Myeloid Losemi Hastalarinda mikroRNA-210 ve BCL-2 Ekspresyon Seviyeleri. Bezmialem Science, 10(4), 453. https://doi.org/10.14235/bas.galenos.2021.5447
Iezza, M., Cortesi, S., Ottaviani, E., Mancini, M., Venturi, C., Monaldi, C., De Santis, S., Testoni, N., Soverini, S., Rosti, G., Cavo, M., & Castagnetti, F. (2023). Prognosis in Chronic Myeloid Leukemia: Baseline Factors, Dynamic Risk Assessment and Novel Insights. Cells, 12(13), 1–25. https://doi.org/10.3390/cells12131703
Indonesian Pediatric Cancer Registry. (2024). Mengungkap Tantangan dan Peluang dalam Perawatan Kanker Anak di Indonesia: Data IPCAR 2020-2024
Irawan, C., Steven, R., Gunarsa, R. G., & Tenggara, J. B. (2022). Outcome Result of Acute Myeloid Leukemia Undergoing Treatment in Semi-Isolation Chemotherapy Ward. Jurnal Penyakit dalam Indonesia, 9(3), 155-163. https://doi.org/10.7454/jpdi.v9i3.857
Jabbour, E., & Kantarjian, H. (2022). Chronic Myeloid Leukemia: 2022 Update on Diagnosis, Therapy, and Monitoring. American Journal of Hematology, 97(9), 1236–1256. https://doi.org/10.1002/ajh.26642
Kementerian Kesehatan Republik Indonesia. (2023). Pedoman Penemuan Dini Kanker pada Anak
Khalid, R., & Riasat, S. (2023). Molecular Pathogenesis and Treatment Strategies of Chronic Myeloid Leukemia (CML). Sudan Journal of Medical Sciences, 18(4), 525–538. https://doi.org/10.18502/sjms.v18i4.14741
Lan, D., Xiong, J., Zhang, C., & Zhang, X. (2023). Association between Gut Microbiota and Leukemia: A Bidirectional Multivariable Mendelian Randomization Study. Blood Journal American Society of Hematology, 142(1). https://doi.org/10.1182/blood-2023-189071
Looi, W., Zargari, A., Dun, K., Grigoriadis, G., Fedele, P., Gregory, G. P., & Low, M. S. Y. (2022). Concomitant Diagnosis of Chronic Myeloid Leukaemia and Myeloma. Pathology. 54(4), 493-495. 10.1016/j.pathol.2021.08.007
Mulya, R. F., Utami, E., & Ariatmanto, D. (2023). Classification of Acute Lymphoblastic Leukemia Based on White Blood Cell Images Using InceptionV3 Model. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 947 - 952. https://doi.org/10.29207/resti.v7i4.5182
Nagiub, K. F., Hussain, N., Omar, O., & Taher, Q. (2022). ALL-018 Application of Machine Learning in the Diagnosis of Acute Lymphoblastic Leukemia. Clin Lymphoma Myeloma Leuk, 22(S190). 10.1016/S2152-2650(22)01174-0
Pagani, I. S., Poudel, G., & Wardill, H. R. (2022). A Gut Instinct on Leukaemia: A New Mechanistic Hypothesis for Microbiota-Immune Crosstalk in Disease Progression and Relapse. Microorganisms. 10(4), 713. 10.3390/microorganisms10040713
Rinaldi, I., & Winston, K. (2023). Chronic Myeloid Leukemia, from Pathophysiology to Treatment-Free Remission: A Narrative Literature Review. Journal of Blood Medicine, 14(April), 261–277. https://doi.org/10.2147/JBM.S382090
Sarkar, A. T., Das, G., Das., & Ghosh, Z. (2023). MicroRNA Mediated Gene Regulatory Circuits Leads to Machine Learning Based Preliminary Detection of Acute Myeloid Leukemia. Comput Biol Chem, 104, 107859. 10.1016/j.compbiolchem.2023.107859
Setiya, D. F. J., Kusnan, D. A. P., & Handayani, E. S. (2024). Peran Microbiome Gut Axis dalam Perkembangan dan Kekambuhan Chronic Myeloid Leukemia: Systematic Review. Jurnal Media Akademik, 2(7), 1-12. https://doi.org/10.62281/v2i7.674
Urgessa, F., Lengiso, B., Tsegaye, A., Gebremedhin, A., Abdella, F., Tadesse, F., Radich, J., Nigussie, H., & Kuru Gerbaba, T. (2024). Hematological, Clinical, cytogenetic and Molecular Profiles of Confirmed Chronic Myeloid Leukemia Patients At Presentation At A Tertiary Care Teaching Hospital In Addis Ababa, Ethiopia: A Cross-Sectional Study. BMC Cancer, 24(1), 1–10. https://doi.org/10.1186/s12885-024-12282-x
Xu, J. H., Jia, H. H., Jeong, J., & Zhao, Z. (2023). Deep Learning for Detecting and Elucidating Human T-Cell Leukemia Virus Type 1 Integration in The Human Genome. Patterns, 4(2), 100674, 10.1016/j.patter.2022.100674
Published
2024-10-31
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Copyright (c) 2024 Mulyadi Mulyadi, Renillia Renillia, Dessy Dessy
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