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An active learning approach for rapid characterization of endothelial cells in human tumors.

Publication ,  Journal Article
Padmanabhan, RK; Somasundar, VH; Griffith, SD; Zhu, J; Samoyedny, D; Tan, KS; Hu, J; Liao, X; Carin, L; Yoon, SS; Flaherty, KT; Dipaola, RS ...
Published in: PloS one
January 2014

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.

Duke Scholars

Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2014

Volume

9

Issue

3

Start / End Page

e90495

Related Subject Headings

  • Time Factors
  • Signal Transduction
  • Problem-Based Learning
  • Kidney Neoplasms
  • Humans
  • General Science & Technology
  • Endothelial Cells
  • Computational Biology
  • Carcinoma, Renal Cell
  • Artificial Intelligence
 

Citation

APA
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Padmanabhan, R. K., Somasundar, V. H., Griffith, S. D., Zhu, J., Samoyedny, D., Tan, K. S., … Lee, W. M. F. (2014). An active learning approach for rapid characterization of endothelial cells in human tumors. PloS One, 9(3), e90495. https://doi.org/10.1371/journal.pone.0090495
Padmanabhan, Raghav K., Vinay H. Somasundar, Sandra D. Griffith, Jianliang Zhu, Drew Samoyedny, Kay See Tan, Jiahao Hu, et al. “An active learning approach for rapid characterization of endothelial cells in human tumors.PloS One 9, no. 3 (January 2014): e90495. https://doi.org/10.1371/journal.pone.0090495.
Padmanabhan RK, Somasundar VH, Griffith SD, Zhu J, Samoyedny D, Tan KS, et al. An active learning approach for rapid characterization of endothelial cells in human tumors. PloS one. 2014 Jan;9(3):e90495.
Padmanabhan, Raghav K., et al. “An active learning approach for rapid characterization of endothelial cells in human tumors.PloS One, vol. 9, no. 3, Jan. 2014, p. e90495. Epmc, doi:10.1371/journal.pone.0090495.
Padmanabhan RK, Somasundar VH, Griffith SD, Zhu J, Samoyedny D, Tan KS, Hu J, Liao X, Carin L, Yoon SS, Flaherty KT, Dipaola RS, Heitjan DF, Lal P, Feldman MD, Roysam B, Lee WMF. An active learning approach for rapid characterization of endothelial cells in human tumors. PloS one. 2014 Jan;9(3):e90495.

Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2014

Volume

9

Issue

3

Start / End Page

e90495

Related Subject Headings

  • Time Factors
  • Signal Transduction
  • Problem-Based Learning
  • Kidney Neoplasms
  • Humans
  • General Science & Technology
  • Endothelial Cells
  • Computational Biology
  • Carcinoma, Renal Cell
  • Artificial Intelligence