An active learning approach for rapid characterization of endothelial cells in human tumors.
Abstract
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.
Type
Journal articleSubject
AlgorithmsArtificial Intelligence
Carcinoma, Renal Cell
Computational Biology
Endothelial Cells
Humans
Kidney Neoplasms
Problem-Based Learning
Signal Transduction
Time Factors
Permalink
https://hdl.handle.net/10161/8940Published Version (Please cite this version)
10.1371/journal.pone.0090495Publication Info
Padmanabhan, Raghav K; Somasundar, Vinay H; Griffith, Sandra D; Zhu, Jianliang; Samoyedny,
Drew; Tan, Kay See; ... Lee, William MF (2014). An active learning approach for rapid characterization of endothelial cells in human
tumors. PLoS One, 9(3). pp. e90495. 10.1371/journal.pone.0090495. Retrieved from https://hdl.handle.net/10161/8940.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011
Xuejun Liao
Adjunct Assistant Professor in the Department of Electrical and Computer Engineering
Alphabetical list of authors with Scholars@Duke profiles.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info