Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review

Can Özlü , Aycan Acet , Berrin Korkut , Cumali Yalçın

  • Can Özlü: Kütahya Sağlık Bilimleri Üniversitesi
  • Aycan Acet: Kütahta Sağlık Bilimleri Üniversitesi
  • Berrin Korkut: Kütahta Sağlık Bilimleri Üniversitesi
  • Cumali Yalçın: Kütahya Evliya Çelebi Eğitim ve Araştırma Hastanesi
  • Year : 2024
  • Vol : 40
  • Issue : 3
  •  Page : 146-151
ABSTRACT
Artificial intelligence (AI) is characterized as a knowledge project that views knowledge as an object, draws conclusions from it, investigates and evaluates the forms that knowledge takes, and then uses these techniques to mimic human thought processes. Certain subfields of AI, such as machine learning and deep learning, bear a strong resemblance to medical specializations. The main driver of progress in this field is the ability of deep learning algorithms, or artificial neural networks with multiple processing layers, to simulate intricate nonlinear input-output interactions and to identify patterns and extract features from low-level data representations. AI in hematology has made dramatic progress in recent years. However, much of this progress is seemingly scattered and lacks a coherent structure that is difficult for a hematologist to follow. Chronic lymphocytic leukemia (CLL) is a special type of hematological cancer that occurs in older ages. Uncontrolled and gradual proliferation of monoclonal B cells in the blood, bone marrow, spleen and lymph nodes is responsible for the pathogenesis of the disease. Patients with CLL have extremely variable clinical courses, and overall survival times can range from several months to decades. Thirty percent of patients never need medical treatment. Given the extreme heterogeneity of CLL, the development of new prognostic models is inevitable. Presently, there is a growing body of research demonstrating the effectiveness of AI-based models in diagnosing and determining the prognosis of CLL. In this article, we will present an up-to-date review of AI in hematology, with a special focus on the radiology, pathology, genetics, diagnostic, therapeutic applications of AI from an AI perspective in the diagnosis of CLL, a specialized cancer of hematology that appears in advanced age. We will discuss a selection of selected papers demonstrating the current state-of-the-art use of AI in CLL and its potential future uses.
Keywords: Chronic lymphocytic leukemia, machine learning, diagnosis, artificial intelligence
Cite this Article As : Ozlu C, Acet A, Korkut B, Yalcin C. Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review. Selcuk Med J 2024;40(3): 146-151

Download Citation: Endnote/Zotero/Mendeley (RIS) RIS File

Download Citation: BibTeX BibTeX File

Conflict of interest : The authors declare that they have no conflict of interest.
Selcuk Medical Journal
2024, Vol. 40 (3)
ISSN: 1017-6616
E-ISSN: 2149-8059
Received : , Accepted : , Published Online :

References

  1. 1. L. Duan, L. Xu. Business Intelligence for Enterprise Systems: A Survey. IEEE Trans. Ind. Inf. 2012;8(3):679–687.
  2. 2. Vivek Kaul, Sarah Enslin, Seth A. Gross. History of artificial intelligence in medicine. Gastrointestinal Endoscopy. 2020;92(4):807-812.
  3. 3. L.D. Xu, Y. Lu, L. Li, Embedding Blockchain Technology into IoT for Security: a Survey, IEEE Internet of Things Journal. 2021;8(13):10452-10473.
  4. 4. N. Duan, L.Z. Liu, X.J. Yu et al. Classification of multichannel surface-electromyography signals based on convolutional neural networks, J. Industr. Integr. Manag. 2019;15:201–206.
  5. 5. Lenselink EB, Ten Dijke N, Bongers B et al. Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. Journal of cheminformatics. 2017;9:1-14.
  6. 6. Goh, GB, Siegel C, Vishnu A et al. Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. ArXiv. 2017;abs/1706.06689.
  7. 7. Unterthiner, T, Mayar A, Klambauer G et al. Deep learning as an opportunity in virtual screening. In: Proceedings of the deep learning workshop at NIPS. Cambridge, MA, United States: MIT Press. 2014;27:1–9.
  8. 8. Fidan K. Chronic lymphocytic leukemia. J Curr Hematol Oncol Res. 2023;1(3):59-67.
  9. 9. Ozbalak M, Erdogan Ozunal I, Berk S et al. Killer Immunoglobuline Like Receptor gene analysis in Chronic Lymphocytic Leukemia and its relation with autoimmunity. J Ist Faculty Med 2021;84(2):227-36.
  10. 10. Rai KR, Sawitsky A, Cronkite EP et al. Clinical staging of chronic lymphocytic leukemia. Blood. 1975;46(2):219-234.
  11. 11. Binet JL, Auquier A, Dighiero G, et al. A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis. Cancer. 1981;48(1):198-206.
  12. 12. International CLL-IPI working group. An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data. Lancet Oncol. 2016;17:779-790.
  13. 13. Coombes CE, Abrams ZB, Li S et al. Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. J Am Med Inform Assoc. 2020;27(7):1019-1027.
  14. 14. Chen D, Goyal G, Go RS et al. Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia. JCO Clin Cancer Inform. 2019;3:1-11.
  15. 15. Morabito F, Adornetto C, Monti P et al. Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy. Front Oncol. 2023;13:1198992.
  16. 16. Vergnolle I, Ceccomarini T, Canali A et al. Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms. Cytometry B Clin Cytom. Published online August 4, 2023.
  17. 17. Hoffmann J, Eminovic S, Wilhelm C et al. Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia. Curr Oncol. 2023;30(2):1903-1915.
  18. 18. Marionneaux S, Maslak P, Keohane EM. Morphologic identification of atypical chronic lymphocytic leukemia by digital microscopy. Int J Lab Hematol. 2014;36(4):459-64.
  19. 19. Masić N, Gagro A, Rabatić S et al. Decision-tree approach to the immunophenotype-based prognosis of the B-cell chronic lymphocytic leukemia. Am J Hematol. 1998;59(2):143-8.
  20. 20. Zhao M, Mallesh N, Höllein A et al. Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data. Cytometry A. 2020;97(10):1073-1080.
  21. 21. Salama ME, Otteson GE, Camp JJ et al. Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia. Cancers (Basel). 2022;14(10):2537.
  22. 22. Padmanabhan R, El Alaoui Y, Elomri A et al. Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results. Stud Health Technol Inform. 2023;305:279-282.
  23. 23. Agius R, Brieghel C, Andersen MA et al. Machine learning can identify newly diagnosed patients with CLL at high risk of infection. Nat Commun. 2020;17;11(1):363.
  24. 24. El Hussein S, Chen P, Medeiros LJ et al. Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia. J Pathol. 2022;256(1):4-14.
  25. 25. El Hussein S, Chen P, Medeiros LJ et al. Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia. Mod Pathol. 2022;35(8):1121-1125.
  26. 26. Wang Y, Liu H, Wang H et al. Enhancing morphological analysis of peripheral blood cells in chronic lymphocytic leukemia with an artificial intelligence-based tool. Leuk Res. 2023;130:107310.
  27. 27. Zhu Y, Gan X, Qin R et al. Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms. J Oncol. 2022;2022:3652107.
  28. 28. Agius R, Parviz M, Niemann CU. Artificial intelligence models in chronic lymphocytic leukemia–recommendations toward state-of-the-art. Leuk Lymphoma. 2022;63:265–78.
  29. 29. Graham G, Csicsery N, Stasiowski E et al. Genome-scale transcriptional dynamics and environmental biosensing. Proc Natl Acad Sci. 2020;117(6):3301–6.