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LITERATURE CITED

  • 1
    Brunning RD, Orazi A, Germing U, Le Beau MM, Porwit A, Baumann I, Vardiman JW, Hellstrom-Lindberg E. Myelodysplastic syndromes/neoplasms, overview. In: SwerdlowSH, CampoE, HarrisNL, JaffeES, PileriSA, SteinH, ThieleJ, VardimanJW, editors. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Lyon: International Agency for Research on Cancer; 2008. pp 87107.
  • 2
    Kussick SJ, Fromm JR, Rossini A, Li Y, Chang A, Norwood TH, Wood BL. Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am J Clin Pathol 2005; 124: 170181.
  • 3
    Loken MR, van de Loosdrecht A, Ogata K, Orfao A, Wells DA. Flow cytometry in myelodysplastic syndromes: Report from a working conference. Leuk Res 2008; 32: 517.
  • 4
    Stetler-Stevenson M, Arthur DC, Jabbour N, Xie XY, Molldrem J, Barrett AJ, Venzon D, Rick ME. Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood 2001; 98: 979987.
  • 5
    Wells DA, Benesch M, Loken MR, Vallejo C, Myerson D, Leisenring WM, Deeg HJ. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003; 102: 394403.
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    Wells DA, Ogata K. On flow cytometry in myelodysplastic syndromes, with caveats. Leuk Res 2008; 32: 209210.
  • 7
    Carter KM, Raich R, Finn WG, Hero AO III. FINE: Fisher information nonparametric embedding. IEEE Trans Pattern Anal Mach Intell 2009; 31: 20932098.
  • 8
    Finn WG, Carter KM, Raich R, Stoolman LM, Hero AO. Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects. Cytometry B Clin Cytom B 2009; 76B: 17.
  • 9
    Carter KM, Raich R, Finn WG, Hero AO. Information preserving component analysis: Data projections for flow cytometry analysis. IEEE J Select Topics Signal Process 2009; 3: 148158.
  • 10
    Miller DT, Stelzer GT. Contributions of flow cytometry to the analysis of the myelodysplastic syndrome. Clin Lab Med 2001; 21: 811828.
  • 11
    Truong F, Smith BR, Stachurski D, Cerny J, Medeiros LJ, Woda BA, Wang SA. The utility of flow cytometric immunophenotyping in cytopenic patients with a non-diagnostic bone marrow: A prospective study. Leuk Res 2009; 33: 10391046.
  • 12
    Bowen KL, Davis BH. Abnormal patterns of expression of CD16(FcR-III) and CD11b(CRIII) antigens by developing neutrophils in the bone marrow of patients with myelodysplastic syndrome. Lab Hematol 1997; 3: 292298.
  • 13
    Loken MR, Wells DA. The role of flow cytometry in myelodysplastic syndromes. J Natl Compr Canc Netw 2008; 6: 935941.
  • 14
    Maynadie M, Picard F, Husson B, Chatelain B, Cornet Y, Le Roux G, Campos L, Dromelet A, Lepelley P, Jouault H, et al. Immunophenotypic clustering of myelodysplastic syndromes. Blood 2002; 100: 23492356.
  • 15
    Stachurski D, Smith BR, Pozdnyakova O, Andersen M, Xiao Z, Raza A, Woda BA, Wang SA. Flow cytometric analysis of myelomonocytic cells by a pattern recognition approach is sensitive and specific in diagnosing myelodysplastic syndrome and related marrow diseases: Emphasis on a global evaluation and recognition of diagnostic pitfalls. Leuk Res 2008; 32: 215224.
  • 16
    Fujimoto H, Sakata T, Hamaguchi Y, Shiga S, Tohyama K, Ichiyama S, Wang FS, Houwen B. Flow cytometric method for enumeration and classification of reactive immature granulocyte populations. Cytometry 2000; 42: 371378.
  • 17
    van Lochem EG, van der Velden VH, Wind HK, te Marvelde JG, Westerdaal NA, van Dongen JJ Immunophenotypic differentiation patterns of normal hematopoiesis in human bone marrow: Reference patterns for age-related changes and disease-induced shifts. Cytometry B Clin Cytom B 2004; 60B: 113.
  • 18
    Roederer M, Hardy RR. Frequency difference gating: A multivariate method for identifying subsets that differ between samples. Cytometry 2001; 45: 5664.
  • 19
    Roederer M, Moore W, Treister A, Hardy RR, Herzenberg LA. Probability binning comparison: A metric for quantitating multivariate distribution differences. Cytometry 2001; 45: 4755.
  • 20
    Zamir E, Geiger B, Cohen N, Kam Z, Katz BZ. Resolving and classifying haematopoietic bone-marrow cell populations by multi-dimensional analysis of flow-cytometry data. Br J Haematol 2005; 129: 420431.
  • 21
    Zeng QT, Pratt JP, Pak J, Ravnic D, Huss H, Mentzer SJ. Feature-guided clustering of multi-dimensional flow cytometry datasets. J Biomed Inform 2007; 40: 325331.
  • 22
    Amari S, Nagaoka H. Differential-Geometrical Methods in Statistics. Berlin: Springer-Verlage; 1990.
  • 23
    Della Porta MG, Malcovati L, Invernizzi R, Travaglino E, Pascutto C, Maffioli M, Galli A, Boggi S, Pietra D, Vanelli L, et al. Flow cytometry evaluation of erythroid dysplasia in patients with myelodysplastic syndrome. Leukemia 2006; 20: 549555.
  • 24
    Wood B. 9-color and 10-color flow cytometry in the clinical laboratory. Arch Pathol Lab Med 2006; 130: 680690.