BACKGROUND: For chronic myeloid leukemia, the FISH detection of t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes is an alternative method to bone marrow karyotyping for monitoring treatment. With automation, several drawbacks of manual analysis may be circumvented. In this article, the capabilities of a commercially available automated image acquisition and analysis system were determined by detecting t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes. METHODS: Three peripheral blood samples of normal adults, 21 samples of CML patients, and one sample of a t(9;22)(q34;q11) positive cell-line were used. RESULTS: Single nuclei with correctly detected signals amounted to 99.6% of nuclei analyzed after exclusion of overlapping nuclei and nuclei with incorrect signal detection. A cut-off value of 0.84 mum was defined to discriminate between translocation positive and negative nuclei based on the shortest distance between signals. Using this value, the false positive rate of the automated analysis for negative samples was 7.0%, whereas that of the manual analysis was 5.8%. Automated and manual results showed strong correlation (R(2) = 0.985), the mean difference of results was only 3.7%. CONCLUSIONS: A reliable and objective automated analysis of large numbers of cells is possible, avoiding interobserver variability and producing statistically more accurate results than manual evaluation. Copyright 2006 International Society for Analytical Cytology.
BACKGROUND: For chronic myeloid leukemia, the FISH detection of t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes is an alternative method to bone marrow karyotyping for monitoring treatment. With automation, several drawbacks of manual analysis may be circumvented. In this article, the capabilities of a commercially available automated image acquisition and analysis system were determined by detecting t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes. METHODS: Three peripheral blood samples of normal adults, 21 samples of CMLpatients, and one sample of a t(9;22)(q34;q11) positive cell-line were used. RESULTS: Single nuclei with correctly detected signals amounted to 99.6% of nuclei analyzed after exclusion of overlapping nuclei and nuclei with incorrect signal detection. A cut-off value of 0.84 mum was defined to discriminate between translocation positive and negative nuclei based on the shortest distance between signals. Using this value, the false positive rate of the automated analysis for negative samples was 7.0%, whereas that of the manual analysis was 5.8%. Automated and manual results showed strong correlation (R(2) = 0.985), the mean difference of results was only 3.7%. CONCLUSIONS: A reliable and objective automated analysis of large numbers of cells is possible, avoiding interobserver variability and producing statistically more accurate results than manual evaluation. Copyright 2006 International Society for Analytical Cytology.
Authors: Xingwei Wang; Bin Zheng; Roy R Zhang; Shibo Li; Xiaodong Chen; John J Mulvihill; Xianglan Lu; Hui Pang; Hong Liu Journal: Technol Cancer Res Treat Date: 2010-06
Authors: Jörn Erlecke; Isabell Hartmann; Martin Hoffmann; Torsten Kroll; Heike Starke; Anita Heller; Alexander Gloria; Herbert G Sayer; Tilman Johannes; Uwe Claussen; Thomas Liehr; Ivan F Loncarevic Journal: Mol Cytogenet Date: 2009-05-29 Impact factor: 2.009
Authors: Linping Hu; Kun Ru; Li Zhang; Yuting Huang; Xiaofan Zhu; Hanzhi Liu; Anders Zetterberg; Tao Cheng; Weimin Miao Journal: Biomark Res Date: 2014-02-05