Automated Detection of Immunofluorescently Labeled Cytomegalovirus-Infected Cells in Isolated Peripheral Blood Leukocytes Using Decision Tree Analysis

Cytometry Part A

Volumn 58, Issue 2, 2004, Pages 147-156

  Ladanyi, Andras ^a   Sher, Andrew C ^a   Herlitz, Anna ^a   Bergsrud, Danielle E ^a   Kraeft, Stine Kathrein ^a   Kepros, Jane ^a   McDaid, Gizelda ^a   Ferguson, David ^b   Landry, Marie L ^b   Chen, Lan Bo ^a  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b YALE NEW HAVEN HOSPITAL   (United States)

Author keywords

Automated microscopy; CMV; Decision tree analysis; Immunofluorescence; Rare event detection

Indexed keywords

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATION; CELL LABELING; COST EFFECTIVENESS ANALYSIS; CYTOMEGALOVIRUS; DECISION THEORY; HUMAN; HUMAN CELL; IMAGE CYTOMETRY; IMMUNOFLUORESCENCE; LEUKOCYTE; MICROSCOPY; PRIORITY JOURNAL; RELIABILITY; SENSITIVITY AND SPECIFICITY; VIRUS DETECTION; VIRUS INFECTION;

EID: 11144357006     PISSN: 15524922     EISSN: None     Source Type: Journal    
DOI: 10.1002/cyto.a.20016     Document Type: Article

References (30)

1. Nichols WG, Boeckh M. Recent advances in the therapy and prevention of CMV infections. J Clin Virol 2000;16:25-40.
2. Mori T, Okamoto S, Matsuoka S, Yajima T, Wakui M, Watanabe R, Ishida A, Iwao Y, Mukai M, Hibi T, Ikeda Y. Risk-adapted pre-emptive therapy for cytomegalovirus disease in patients undergoing allogeneic bone marrow transplantation. Bone Marrow Transplant 2000;25: 765-769.
3. Daly JS, Kopasz A, Anandakrishnan R, Robins T, Mehta S, Halvorsen M, Katz E. Preemptive strategy for ganciclovir administration against cytomegalovirus in liver transplantation recipients. Am J Transplant 2002;2:955-958.
4. Mengelle C, Pasquier C, Rostaing L, Sandres-Saune K, Puel J, Berges L, Righi L, Bouquies C, Izopet J. Quantitation of human cytomegalovirus in recipients of solid organ transplants by real-time quantitative PCR and pp65 antigenemia. J Med Virol 2003;69:225-231.
5. Li H, Dummer JS, Estes WR, Meng S, Wright PF, Tang YW. Measurement of human cytomegalovirus loads by quantitative real-time PCR for monitoring clinical intervention in transplant recipients. J Clin Microbiol 2003;41:187-191.
6. Griscelli F, Barrois M, Chauvin S, Lastere S, Bellet D, Bourhis JH. Quantification of human cytomegalovirus DNA in bone marrow transplant recipients by real-time PCR. J Clin Microbiol 2001;39:4362-439.
7. Yakushiji K, Gondo H, Kamezaki K, Shigematsu K, Hayashi S, Kuroiwa M, Taniguchi S, Ohno Y, Takase K, Numata A, Aoki K, Kato K, Nagafuji K, Shimoda K, Okamura T, Kinukawa N, Kasuga N, Sata M, Harada M. Monitoring of cytomegalovirus reactivation after allogeneic stem cell transplantation: comparison of an antigenemia assay and quantitative real-time polymerase chain reaction. Bone Marrow Transplant 2002;29:599-606.
8. Ploem-Zaaijer JJ, Mesker WE, Boland GJ, Sloos WC, van de Rijke FM, Jiwa M, Raap AK. Automated image cytometry for detection of rare, viral antigen-positive cells in peripheral blood. Cytometry 1994;15: 199-206.
9. Oosterwijk JC, Knepfle CF, Mesker WE, Vrolijk H, Sloos WC, Pattenier H, Ravkin I, van Ommen GJ, Kanhai HH, Tanke HJ. Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood. Am J Hum Genet 1998;63:1783-1792.
10. Bajaj S, Welsh JB, Leif RC, Price JH. Ultra-rare-event detection performance of a custom scanning cytometer on a model preparation of fetal nRBCs. Cytometry 2000;39:285-294.
11. Mehes G, Lorch T, Ambros PF. Quantitative analysis of disseminated tumor cells in the bone marrow by automated fluorescence image analysis. Cytometry 2000;42:357-362.
12. Borgen E, Naume B, Nesland JM, Nowels KW, Pavlak N, Ravkin I, Goldbard S. Use of automated microscopy for the detection of disseminated tumor cells in bone marrow samples. Cytometry 2001;46: 215-221.
13. Witzig TE, Bossy B, Kimlinger T, Roche PC, Ingle JN, Grant C, Donohue J, Suman VJ, Harrington D, Torre-Bueno J, Bauer KD. Detection of circulating cytokeratin-positive cells in the blood of breast cancer patients using immunomagnetic enrichment and digital microscopy. Clin Cancer Res 2002;8:1085-1091.
14. Pelikan DM, Mesker WE, Scherjon SA, Kanhai HH, Tanke HJ. Improvement of the Kleihauer-Betke test by automated detection of fetal erythrocytes in maternal blood. Cytometry 2003;54B:1-9.
15. Kraeft SK, Sutherland R, Gravelin L, Hu GH, Ferland LH, Richardson P, Elias A, Chen LB. Detection and analysis of cancer cells in blood and bone marrow using a rare event imaging system. Clin Cancer Res 2000;6:434-442.
16. Kraeft SK, Salgia R, Chen LB. Detection and analysis of lung cancer cells from body fluids using a rare event imaging system. Methods Mol Med 2003;75:423-430.
17. Quinlan JR. C4.5: programs for machine learning. San Mateo, CA: Morgan Kaufmann; 1993. 302 p.
18. Freund Y. Boosting a weak algorithm by majority. In: Proceedings of the third annual workshop on computational learning theory. San Francisco, CA: Morgan Kaufmann; 1990. p 202-216.
19. Raubertas RF, Rodewald LE, Humiston SG, Szilagyi PG. ROC curves for classification trees. Med Decis Making 1994;14:169-174.
20. Albertini MC, Teodori L, Piatti E, Piacentini MP, Accorsi A, Rocchi MB. Automated analysis of morphometric parameters for accurate definition of erythrocyte cell shape. Cytometry 2003;52A:12-18.
21. Francois C, Decaestecker C, De Lathouwer O, Moreno C, Peltier A, Roumeguere T, Danguy A, Pasteels JL, Wespes E, Salmon I, van Velthoven R, Kiss R. Improving the prognostic value of histopathological grading and clinical staging in renal cell carcinomas by means of computer-assisted microscopy. J Pathol 1999;187:313-320.
22. van der Laak JA, Siebers AG, Cuijpers VM, Pahlplatz MM, de Wilde PC, Hanselaar AG. Automated identification of diploid reference cells in cervical smears using image analysis. Cytometry 2002;47:256-264.
23. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001;23:89-109.
24. Decaestecker C, Remmelink M, Salmon I, Camby I, Goldschmidt D, Petein M, Van Ham P, Pasteels JL, Kiss R. Methodological aspects of using decision trees to characterise leiomyomatous tumors. Cytometry 1996;24:83-92.
25. Hothorn T, Lausen B. Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy. Artif Intell Med 2003;27: 65-79.
26. Cualing H, Kothari R, Balachander T. Immunophenotypic diagnosis of acute leukemia by using decision tree induction. Lab Invest 1999;79: 205-212.
27. Yeaton P, Sears RJ, Ledent T, Salmon I, Kiss R, Decaestecker C. Discrimination between chronic pancreatitis and pancreatic adenocarcinoma using artificial intelligence-related algorithms based on image cytometry-generated variables. Cytometry 1998;32:309-316.
28. Decaestecker C, van Velthoven R, Petein M, Janssen T, Salmon I, Pasteels JL, van Ham P, Schulman C, Kiss R. The use of the decision tree technique and image cytometry to characterize aggressiveness in World Health Organization (WHO) grade 11 superficial transitional cell carcinomas of the bladder. J Pathol 1996;178:274-283.
29. Perner P, Perner H, Muller B. Mining knowledge for HEp-2 cell image classification. Artif Intell Med 2002;26:161-173.
30. Gerna G, Revello MG, Percivalle E, Morini F. Comparison of different immunostaining techniques and monoclonal antibodies to the lower matrix phosphoprotein (pp65) for optimal quantitation of human cytomegalovirus antigenemia. J Clin Microbiol 1992;30:1232-1237.