ML Anthology

A Generic Customizable Framework for Inverse Local Consistency

Gérard Verfaillie, David Martinez, Christian Bessière
AAAI 1999 pp. 169-174
doi:10.1016/j.humpath.2003.10.008 /aaai/1999/verfaillie1999aaai-generic/

Abstract

The immunocytochemical detection of Tie-2/Tek, CD105, and CD31 was assessed in a large series (n = 905) of breast carcinomas on frozen sections. Results were correlated with patients' long-term outcome (median, 11.7 years) to define the respective prognostic significance of these markers. Univariate (Kaplan-Meier) analysis demonstrated that higher expression of CD31 (P = 0.032), CD105 (P = 0.001), and Tie-2/Tek (P = 0.025) correlated with poorer survival. However, only greater CD105 expression could significantly (P = 0.035) identify node-negative patients with poorer survival. Moreover, in multivariate analysis, CD105 and Tie-2/Tek, but not CD31, expression proved to be independent significant prognostic indicators. Marked expression of CD31 (P = 0.024), CD105 (P = 0.001), and Tie-2/Tek (P = 0.01) also correlated with higher risk of metastases in node-negative patients. It is concluded that CD105 immunoexpression in breast carcinomas is an independent prognostic indicator in node-negative patients, better in terms of overall survival than Tie-2/Tek and CD31. Also, Tie-2/Tek expression proved more sensitive than CD31 expression in terms of prognostic significance. Compared with CD31, CD105 and Tie-2/Tek have more clinical relevance for patient monitoring and also can serve as targets for specific therapy, such as CD105 immunotoxins or Tie-2/Tek pathway blockade, as recently suggested in experimental studies.

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Text

Verfaillie et al. "A Generic Customizable Framework for Inverse Local Consistency." AAAI Conference on Artificial Intelligence, 1999. doi:10.1016/j.humpath.2003.10.008

Markdown

[Verfaillie et al. "A Generic Customizable Framework for Inverse Local Consistency." AAAI Conference on Artificial Intelligence, 1999.](https://mlanthology.org/aaai/1999/verfaillie1999aaai-generic/) doi:10.1016/j.humpath.2003.10.008

BibTeX

@inproceedings{verfaillie1999aaai-generic,
  title     = {{A Generic Customizable Framework for Inverse Local Consistency}},
  author    = {Verfaillie, Gérard and Martinez, David and Bessière, Christian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1999},
  pages     = {169-174},
  doi       = {10.1016/j.humpath.2003.10.008},
  url       = {https://mlanthology.org/aaai/1999/verfaillie1999aaai-generic/}
}