Please use this identifier to cite or link to this item: http://dl.umsu.ac.ir/handle/Hannan/29647
Title: An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors
Authors: Padmanabhan, Raghav K.;Somasundar, Vinay H.;Griffith, Sandra D.;Zhu, Jianliang;Samoyedny, Drew;Tan, Kay See;Hu, Jiahao;Liao, Xuejun;Carin, Lawrence;Yoon, Sam S.;Flaherty, Keith T.;DiPaola, Robert S.;Heitjan, Daniel F.;Lal, Priti;Feldman, Michael D.;Roysam, Badrinath;Lee, William M. F.
subject: Biology;Molecular Cell Biology;Cytometry;Image Cytometry;Computer Science;Algorithms;Software Engineering;Software Tools;Engineering;Signal Processing;Data Mining;Image Processing;Medicine;Diagnostic Medicine;Oncology;Cancer Treatment;Antiangiogenesis Therapy;Cancer Detection and Diagnosis
Year: 2014
Publisher: Public Library of Science
Description: Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.
URI: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3946171/pdf/
http://nrs.harvard.edu/urn-3:HUL.InstRepos:12064439
Standard no: Padmanabhan, R. K., V. H. Somasundar, S. D. Griffith, J. Zhu, D. Samoyedny, K. S. Tan, J. Hu, et al. 2014. “An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors.” PLoS ONE 9 (3): e90495. doi:10.1371/journal.pone.0090495. http://dx.doi.org/10.1371/journal.pone.0090495.
1932-6203
Appears in Collections:HMS Scholarly Articles

Files in This Item:
Click on the URI links for accessing contents.


Items in HannanDL are protected by copyright, with all rights reserved, unless otherwise indicated.