An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors

Autor: Vinay H. Somasundar, Drew Samoyedny, Sam S. Yoon, Lawrence Carin, Michael Feldman, Keith T. Flaherty, Kay See Tan, Daniel F. Heitjan, Priti Lal, William M. F. Lee, Jiahao Hu, Badrinath Roysam, Xuejun Liao, Jianliang Zhu, Sandra D. Griffith, Raghav Padmanabhan, Robert S. DiPaola
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Time Factors
Angiogenesis
Image Processing
Cancer Treatment
lcsh:Medicine
Bioinformatics
0302 clinical medicine
Engineering
Molecular Cell Biology
Data Mining
lcsh:Science
Image Cytometry
0303 health sciences
Multidisciplinary
Software Engineering
Kidney Neoplasms
3. Good health
Oncology
030220 oncology & carcinogenesis
Medicine
Antiangiogenesis Therapy
Algorithms
Research Article
Signal Transduction
Active learning (machine learning)
Computational biology
03 medical and health sciences
Text mining
Diagnostic Medicine
Artificial Intelligence
medicine
Cancer Detection and Diagnosis
Humans
Image analysis
Biology
Carcinoma
Renal Cell
030304 developmental biology
business.industry
Software Tools
lcsh:R
Computational Biology
Endothelial Cells
Problem-Based Learning
medicine.disease
Clear cell renal cell carcinoma
Statistical classification
Computer Science
Signal Processing
lcsh:Q
business
Cytometry
Zdroj: PLoS ONE
PLoS ONE, Vol 9, Iss 3, p e90495 (2014)
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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