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Proceedings Paper

Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis
Author(s): Maxine Tan; Nastaran Emaminejad; Wei Qian; Shenshen Sun; Yan Kang; Yubao Guan; Fleming Lure; Bin Zheng
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Paper Abstract

Stage I non-small-cell lung cancers (NSCLC) usually have favorable prognosis. However, high percentage of NSCLC patients have cancer relapse after surgery. Accurately predicting cancer prognosis is important to optimally treat and manage the patients to minimize the risk of cancer relapse. Studies have shown that an excision repair crosscomplementing 1 (ERCC1) gene was a potentially useful genetic biomarker to predict prognosis of NSCLC patients. Meanwhile, studies also found that chronic obstructive pulmonary disease (COPD) was highly associated with lung cancer prognosis. In this study, we investigated and evaluated the correlations between COPD image features and ERCC1 gene expression. A database involving 106 NSCLC patients was used. Each patient had a thoracic CT examination and ERCC1 genetic test. We applied a computer-aided detection scheme to segment and quantify COPD image features. A logistic regression method and a multilayer perceptron network were applied to analyze the correlation between the computed COPD image features and ERCC1 protein expression. A multilayer perceptron network (MPN) was also developed to test performance of using COPD-related image features to predict ERCC1 protein expression. A nine feature based logistic regression analysis showed the average COPD feature values in the low and high ERCC1 protein expression groups are significantly different (p < 0.01). Using a five-fold cross validation method, the MPN yielded an area under ROC curve (AUC = 0.669±0.053) in classifying between the low and high ERCC1 expression cases. The study indicates that CT phenotype features are associated with the genetic tests, which may provide supplementary information to help improve accuracy in assessing prognosis of NSCLC patients.

Paper Details

Date Published: 11 March 2014
PDF: 9 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371Q (11 March 2014); doi: 10.1117/12.2044350
Show Author Affiliations
Maxine Tan, The Univ. of Oklahoma (United States)
Nastaran Emaminejad, The Univ. of Oklahoma (United States)
Wei Qian, The Univ. of Texas at El Paso (United States)
Northeastern Univ. (China)
Shenshen Sun, Northeastern Univ. (China)
Yan Kang, Northeastern Univ. (China)
Yubao Guan, Guangzhou Medical Univ. (China)
Fleming Lure, The Univ. of Texas at El Paso (United States)
Northeastern Univ. (China)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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