Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes

Cazzola M. Myelodysplastic syndromes. N Engl J Med. 2020;383:1358–74.

Article  CAS  PubMed  Google Scholar 

Koenig KL, Sahasrabudhe KD, Sigmund AM, Bhatnagar B. AML with Myelodysplasia-related changes: development, challenges, and treatment advances. Genes (Basel). 2020;11:845.

Article  CAS  PubMed  Google Scholar 

Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009;114:937–51.

Article  CAS  PubMed  Google Scholar 

Newell LF, Cook RJ. Advances in acute myeloid leukemia. BMJ. 2021;375:n2026.

Lachowiez CA, Long N, Saultz J, Gandhi A, Newell LF, Hayes-Lattin B, et al. Comparison and validation of the 2022 European LeukemiaNet guidelines in acute myeloid leukemia. Blood Adv. 2023;7:1899–909.

Article  PubMed  Google Scholar 

Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imaging Radiat Sci. 2019;50:477–87.

Article  PubMed  Google Scholar 

Koski E, Murphy J. AI in Healthcare. Stud Health Technol Inform. 2021;284:295–9.

PubMed  Google Scholar 

Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021;31:685–95.

Article  Google Scholar 

Bhalla S, Laganà A. Artificial intelligence for precision oncology. 2022. p. 249–68.

Eckardt J-N, Röllig C, Metzeler K, Kramer M, Stasik S, Georgi J-A, et al. Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Haematologica. 2023;108:690–704.

Article  CAS  PubMed  Google Scholar 

Duchmann M, Wagner-Ballon O, Boyer T, Cheok M, Fournier E, Guerin E, et al. Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML. Leukemia. 2022;36:656–63.

Article  CAS  PubMed  Google Scholar 

Roohi A, Faust K, Djuric U, Diamandis P. Unsupervised machine learning in pathology. Surg Pathol Clin. 2020;13:349–58.

Article  PubMed  Google Scholar 

Ghazal TM, Al Hamadi H, Umar Nasir M, Atta-Ur-Rahman, Gollapalli M, Zubair M, Adnan Khan M, Yeob Yeun C. Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction. Comput Intell Neurosci. 22022;022:1051388.

Habehh H, Gohel S. Machine learning in healthcare. Curr Genomics. 2021;22:291–300.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19:1236–46.

Article  PubMed  Google Scholar 

•• Radakovich N, Nagy M, Nazha A. Artificial intelligence in hematology: current challenges and opportunities. Curr Hematol Malig Rep. 2020;15:203–10. “Artificial intelligence in hematology: current challenges and opportunities” This paper underscores the profound impact of Artificial Intelligence as a transformative tool in the field of medicine at large, with a special emphasis on its significance in hematology.

Liu J, Yuan R, Li Y, Zhou L, Zhang Z, Yang J, et al. A deep learning method and device for bone marrow imaging cell detection. Ann Transl Med. 2022;10:208–208.

Article  PubMed  PubMed Central  Google Scholar 

•• Radakovich N, Sallman DA, Buckstein R, Brunner A, Dezern A, Mukerjee S, et al. A machine learning model of response to hypomethylating agents in myelodysplastic syndromes. iScience. 2022;25:104931. This paper showcases the remarkable capabilities of Machine Learning within the realm of Myelodysplastic Syndromes by successfully predicting treatment outcomes approximately midway through the regimen of hypomethylating agents.

Nath S, Marie A, Ellershaw S, Korot E, Keane PA. New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology. Br J Ophthalmol. 2022;106:889–92.

Article  PubMed  Google Scholar 

Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: Transformer for electronic health records. Sci Rep. 2020;10:7155.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cunningham AR, Behm HE, Ju A, et al. Long-Term Survival of Patients With Glioblastoma of the Pineal Gland: A ChatGPT-Assisted, Updated Case of a Multimodal Treatment Strategy Resulting in Extremely Long Overall Survival at a Site With Historically Poor Outcomes. Cureus. 2023;15(3):e36590.

• Johnson SB, King AJ, Warner EL, Aneja S, Kann BH, Bylund CL. Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information. JNCI Cancer Spectr. 2023;7(2):pkad015. This paper highlights the substantial value of ChatGPT, one of the most cutting-edge and widely embraced AI tools of our time, in delivering accurate and reliable information regarding prevalent cancer myths and misconceptions.

Sallam M. ChatGPT Utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare. 2023;11:887.

Article  PubMed  PubMed Central  Google Scholar 

Kimura K, Tabe Y, Ai T, Takehara I, Fukuda H, Takahashi H, et al. A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA. Sci Rep. 2019;9:13385.

Article  PubMed  PubMed Central  Google Scholar 

Acevedo A, Merino A, Boldú L, Molina Á, Alférez S, Rodellar J. A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes. Comput Biol Med. 2021;134:104479.

Article  PubMed  Google Scholar 

Eckardt J-N, Schmittmann T, Riechert S, Kramer M, Sulaiman AS, Sockel K, et al. Deep learning identifies acute promyelocytic leukemia in bone marrow smears. BMC Cancer. 2022;22:201.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Radakovich N, Meggendorfer M, Malcovati L, Hilton CB, Sekeres MA, Shreve J, et al. A geno-clinical decision model for the diagnosis of myelodysplastic syndromes. Blood Adv. 2021;5:4361–9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

• Warnat-Herresthal S, Perrakis K, Taschler B, Becker M, Baßler K, Beyer M, et al. Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. iScience. 2020;23:100780. This paper underscores the immense potential of machine learning in harnessing transcriptomics data to effectively classify and sub-categorize Acute Myeloid Leukemia.

•• Nazha A, Komrokji R, Meggendorfer M, Jia X, Radakovich N, Shreve J, et al. Personalized prediction model to risk stratify patients with myelodysplastic syndromes. J Clin Oncol. 2021;39:3737–46. This was the first paper to demonstrate the superiority of Machine Learning models over the existing prognostic models.

Eckardt J-N, Röllig C, Metzeler K, Kramer M, Stasik S, Georgi J-A, et al. Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Haematologica. 2022;108:690–704.

Article  PubMed Central  Google Scholar 

Tazi Y, Arango-Ossa JE, Zhou Y, Bernard E, Thomas I, Gilkes A, et al. Unified classification and risk-stratification in acute myeloid leukemia. Nat Commun. 2022;13:4622.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fuse K, Uemura S, Tamura S, Suwabe T, Katagiri T, Tanaka T, et al. Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach. Cancer Med. 2019;8:5058–67.

Article  PubMed  PubMed Central  Google Scholar 

Shouval R, Labopin M, Bondi O, Mishan-Shamay H, Shimoni A, Ciceri F, et al. Prediction of allogeneic hematopoietic stem-cell transplantation mortality 100 days after transplantation using a machine learning algorithm: a European Group for blood and marrow transplantation acute leukemia working party retrospective data mining study. J Clin Oncol. 2015;33:3144–51.

Article  PubMed  Google Scholar 

Herold T, Jurinovic V, Batcha AMN, Bamopoulos SA, Rothenberg-Thurley M, Ksienzyk B, et al. A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia. Haematologica. 2018;103:456–65.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nazha A, Sekeres MA, Bejar R, Rauh MJ, Othus M, Komrokji RS, Barnard J, Hilton CB, Kerr CM, Steensma DP, DeZern A, Roboz G, Garcia-Manero G, Erba H, Ebert BL, Maciejewski JP. Genomic Biomarkers to Predict Resistance to Hypomethylating Agents in Patients With Myelodysplastic Syndromes Using Artificial Intelligence. JCO Precis Oncol. 2019;3:PO.19.00119.

留言 (0)

沒有登入
gif