https://scholars.lib.ntu.edu.tw/handle/123456789/468863
標題: | Two-dimensional matrix algorithm using detrended fluctuation analysis to distinguish Burkitt and diffuse large B-cell lymphoma | 作者: | Yeh R.-G. CHUNG-WU LIN Abbod M.F. Shieh J.-S. |
公開日期: | 2012 | 卷: | 2012 | 起(迄)頁: | 947191 | 來源出版物: | Computational and Mathematical Methods in Medicine | 摘要: | A detrended fluctuation analysis (DFA) method is applied to image analysis. The 2-dimensional (2D) DFA algorithms is proposed for recharacterizing images of lymph sections. Due to Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), there is a significant different 5-year survival rates after multiagent chemotherapy. Therefore, distinguishing the difference between BL and DLBCL is very important. In this study, eighteen BL images were classified as group A, which have one to five cytogenetic changes. Ten BL images were classified as group B, which have more than five cytogenetic changes. Both groups A and B BLs are aggressive lymphomas, which grow very fast and require more intensive chemotherapy. Finally, ten DLBCL images were classified as group C. The short-term correlation exponent α1 values of DFA of groups A, B, and C were 0.370 ± 0.033, 0.382 ± 0.022, and 0.435 ± 0.053, respectively. It was found that α1 value of BL image was significantly lower (P < 0.05) than DLBCL. However, there is no difference between the groups A and B BLs. Hence, it can be concluded that α1 value based on DFA statistics concept can clearly distinguish BL and DLBCL image. ? 2012 Rong-Guan Yeh et al. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872784007&doi=10.1155%2f2012%2f947191&partnerID=40&md5=b7a0a143646c324b0dcb52b0cb54235c https://scholars.lib.ntu.edu.tw/handle/123456789/468863 |
ISSN: | 1748-670X | DOI: | 10.1155/2012/947191 | SDG/關鍵字: | Chemotherapy; Oncology; Detrended fluctuation analysis; Diffuse large B-cell lymphoma; Matrix algorithms; Short-term correlation; Survival rate; Value-based; Image classification; algorithm; article; Burkitt lymphoma; cancer chemotherapy; cancer survival; controlled study; cytogenetics; detrended fluctuation analysis; image analysis; large cell lymphoma; simulation; algorithm; biology; Burkitt lymphoma; differential diagnosis; human; image processing; information processing; large cell lymphoma; lymph node; methodology; pathology; statistical model; antineoplastic agent; Algorithms; Antineoplastic Agents; Automatic Data Processing; Burkitt Lymphoma; Computational Biology; Cytogenetics; Diagnosis, Differential; Humans; Image Processing, Computer-Assisted; Lymph Nodes; Lymphoma, Large B-Cell, Diffuse; Models, Statistical |
顯示於: | 病理學科所 |
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