Application of an artificial neural network in the prognosis of chronic myeloid leukemia

Analytical and Quantitative Cytology and Histology

Volumn 33, Issue 6, 2011, Pages 335-339

  Dey, Pranab ^a   Lamba, Amit ^a   Kumari, Savita ^a   Marwaha, Neelam ^a  

^a POSTGRADUATE INSTITUTE OF MEDICAL EDUCATION AND RESEARCH   (India)

Author keywords

Chronic myeloid leukemia; Morphometry; Neural network

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER CLASSIFICATION; CANCER PROGNOSIS; CHRONIC MYELOID LEUKEMIA; CLINICAL ARTICLE; COMPUTER PROGRAM; HUMAN; MORPHOMETRICS; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

HUMANS; LEUKEMIA, MYELOGENOUS, CHRONIC, BCR-ABL POSITIVE; NEURAL NETWORKS (COMPUTER); PROGNOSIS; SENSITIVITY AND SPECIFICITY; SOFTWARE;

EID: 84861006385     PISSN: 08846812     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Dey P, Dey R. Artificial neural network: Mechanism and application in pathology. Indian J Pathol Microbiol 2002;45: 371-374
2. Jiang J, Trundle P, Ren J. Medical image analysis with artificial neural networks. Comput Med Imaging Graph 2010;34: 617-631
3. Adjouadi M, Ayala M, Cabrerizo M, Zong N, Lizarraga G, Rossman M. Classification of leukemia blood samples using neural networks. Ann Biomed Eng 2010;38:1473-1482
4. Ayer T, Chhatwal J, Alagoz O, Kahn CE Jr, Woods RW, Burnside ES. Informatics in radiology: Comparison of logistic regression and artificial neural network models in breast cancer risk estimation. Radiographics 2010;30:13-22
5. Lai KC, Chiang HC, Chen WC, Tsai FJ, Jeng LB. Artificial neural network-based study can predict gastric cancer staging. Hepatogastroenterology 2008;55:1859-1863
6. Oda S, Awai K, Suzuki K, Yanaga Y, Funama Y, MacMahon H, Yamashita Y. Performance of radiologists in detection of small pulmonary nodules on chest radiographs: Effect of rib suppression with a massive-training artificial neural network. AJR Am J Roentgenol 2009;193:W397-W402
7. Irwig L, Macaskill P, Farnsworth A, Wright RG, McCool J, Barratt A, Simpson JM. A randomized crossover trial of PAPNET for primary cervical screening. J Clin Epidemiol 2004;57:75-81
8. Karakitsos P, Stergiou EB, Pouliakis A, Tzivras M, Archimandritis A, Liossi AI, Kyrkou K. Potential of the back propagation neural network in the discrimination of benign from malignant gastric cells. Anal Quant Cytol Histol 1996;18: 245-250
9. Karakitsos P, Cochand-Priollet B, Guillausseau PJ, Pouliakis A. Potential of the back propagation neural network in the morphologic examination of thyroid lesions. Anal Quant Cytol Histol 1996;18:495-500
10. Hurst RE, Bonner RB, Ashenayi K, Veltri RW, Hemstreet GP 3rd. Neural netbased identification of cells expressing the p300 tumor-related antigen using fluorescence image analysis. Cytometry 1997;27:36-42
11. Truong H, Morimoto R, Walts AE, Erler B, Marchevsky A. Neural networks as an aid in the diagnosis of lymphocyterich effusions. Anal Quant Cytol Histol 1995;17:48-54
12. Tura S, Baccarani M, Corbelli G. Staging of chronic myeloid leukaemia. Br J Haematol 1981;47:105-119
13. Cervantes F, Rozman C. A multivariate analysis of prognostic factors in chronic myeloid leukemia. Blood 1982;60:1298-1304
14. Kantarjian HM, Keating MJ, Smith TL, Talpaz M, McCredie KB. Proposal for a simple synthesis prognostic staging system in chronic myelogenous leukemia. Am J Med 1990;88:1-8
15. Sokal JE, Cox EB, Baccarani M, Tura S, Gomez GA, Robertson JE, Tso CY, Braun TJ, Clarkson BD, Cervantes F. Prognostic discrimination in "good-risk" chronic granulocytic leukemia. Blood 1984;63:789-799
16. Lamba A, Dey P, Savita K, Marwaha N. Prognostic significance of the histomorphometric features of bone marrow trephine biopsies in patients with chronic myeloid leukemia. Anal Quant Cytol Histol 2007;29:370-376
17. Hasford J, Pfirrmann M, Hehlmann R, Allan NC, Baccarani M, Kluin-Nelemans JC, Alimena G, Steegmann JL, Ansari H. A new prognostic score for survival of patients with chronic myeloid leukemia treated with interferon alfa: Writing Committee for the Collaborative CML Prognostic Factors Project Group. J Natl Cancer Inst 1998;90:850-858
18. de Lavallade H, Apperley JF, Khorashad JS, Milojkovic D, Reid AG, Bua M, Szydlo R, Olavarria E, Kaeda J, Goldman JM, Marin D. Imatinib for newly diagnosed patients with chronic myeloid leukemia: Incidence of sustained responses in an intention-to-treat analysis. J Clin Oncol 2008;26:3358- 3363
19. Hochhaus A, O'Brien SG, Guilhot F, Druker BJ, Branford S, Foroni L, Goldman JM, Müller MC, Radich JP, Rudoltz M, Mone M, Gathmann I, Hughes TP, Larson RA; IRIS Investigators: Six year follow-up of patients receiving imatinib for the first-line treatment of chronic myeloid leukemia. Leukemia 2009;23:1054-1061
20. Hasford J, Baccarani M, Hoffmann V, Guilhot J, Saussele S, Rosti G, Guilhot F, Porkka K, Ossenkoppele G, Lindoerfer D, Simonsson B, Pfirrmann M, Hehlmann R. Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: The EUTOS score. Blood 2011;118:686-692