-
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008; 01/2008
-
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008; 01/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Dobutamine stress echocardiography is a commonly used imaging modality for the diagnosis of coronary artery disease and the detection of myocardial viability. The major limitations are that it is operator dependent and that the analysis is subjective and qualitative resulting in interobserver variability. It is also tedious and time consuming. Consequently, several quantitative approaches have been proposed, such as acoustic quantification and color kinesis but none of these has proved to be fully quantitative. In this manuscript we describe the development of a new, quantitative technique based on tracking of both endocardium and epicardium providing information of endocardial excursion and myocardial thickening, a crucial parameter of wall function evaluation. Preliminary data indicate that the method is practical and feasible, but clinical trials are required to prove whether it will improve the sensitivity and specificity of dobutamine stress echocardiography.
European Heart Journal – Cardiovascular Imaging 01/2008; 8(6):431-7. · 2.32 Impact Factor
-
Renaud Seigneuric,
Maud H W Starmans,
Glenn Fung,
Balaji Krishnapuram,
Dimitry S A Nuyten,
Arie van Erk,
Michael G Magagnin,
Kasper M Rouschop, Sriram Krishnan,
R Bharat Rao,
Chris T A Evelo,
Adrian C Begg,
Bradly G Wouters,
Philippe Lambin
[show abstract]
[hide abstract]
ABSTRACT: Hypoxia is a common feature of solid tumors associated with therapy resistance, increased malignancy and poor prognosis. Several approaches have been developed with the hope of identifying patients harboring hypoxic tumors including the use of microarray based gene signatures. However, studies to date have largely ignored the strong time dependency of hypoxia-regulated gene expression. We hypothesized that use of time-dependent patterns of gene expression during hypoxia would enable development of superior prognostic expression signatures.
Using published data from the microarray study of Chi et al., we extracted gene signatures correlating with induction during either early or late hypoxic exposure. Gene signatures were derived from in vitro exposed human mammary epithelial cell line (HMEC) under 0% or 2% oxygen. Gene signatures correlating with early and late up-regulation were tested by means of Kaplan-Meier survival, univariate, and multivariate analysis on a patient data set with primary breast cancer treated conventionally (surgery plus on indication radiotherapy and systemic therapy).
We found that the two early hypoxia gene signatures extracted from 0% and 2% hypoxia showed significant prognostic power (log-rank test: p=0.004 at 0%, p=0.034 at 2%) in contrast to the late hypoxia signatures. Both early gene signatures were linked to the insulin pathway. From the multivariate Cox-regression analysis, the early hypoxia signature (p=0.254) was found to be the 4th best prognostic factor after lymph node status (p=0.002), tumor size (p=0.016) and Elston grade (p=0.111). On this data set it indeed provided more information than ER status or p53 status.
The hypoxic stress elicits a wide panel of temporal responses corresponding to different biological pathways. Early hypoxia signatures were shown to have a significant prognostic power. These data suggest that gene signatures identified from in vitro experiments could contribute to individualized medicine.
Radiotherapy and Oncology 07/2007; 83(3):374-82. · 5.58 Impact Factor
-
The Sixth International Conference on Machine Learning and Applications, ICMLA 2007, Cincinnati, Ohio, USA, 13-15 December 2007; 01/2007
-
IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007; 01/2007
-
SIGKDD Explorations. 01/2006; 8:3-10.
-
Fourth International Conference on Machine Learning and Applications, ICMLA 2005, Los Angeles, California, USA, 15-17 December 2005; 01/2005
-
IEEE Engineering in Medicine and Biology Magazine 26(2):56-63. · 2.06 Impact Factor
