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20 March 2015 Exploring new quantitative CT image features to improve assessment of lung cancer prognosis
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Due to the promotion of lung cancer screening, more Stage I non-small-cell lung cancers (NSCLC) are currently detected, which usually have favorable prognosis. However, a high percentage of the patients have cancer recurrence after surgery, which reduces overall survival rate. To achieve optimal efficacy of treating and managing Stage I NSCLC patients, it is important to develop more accurate and reliable biomarkers or tools to predict cancer prognosis. The purpose of this study is to investigate a new quantitative image analysis method to predict the risk of lung cancer recurrence of Stage I NSCLC patients after the lung cancer surgery using the conventional chest computed tomography (CT) images and compare the prediction result with a popular genetic biomarker namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes. In this study, we developed and tested a new computer-aided detection (CAD) scheme to segment lung tumors and initially compute 35 tumor-related morphologic and texture features from CT images. By applying a machine learning based feature selection method, we identified a set of 8 effective and non-redundant image features. Using these features we trained a naïve Bayesian network based classifier to predict the risk of cancer recurrence. When applying to a test dataset with 79 Stage I NSCLC cases, the computed areas under ROC curves were 0.77±0.06 and 0.63±0.07 when using the quantitative image based classifier and ERCC1, respectively. The study results demonstrated the feasibility of improving accuracy of predicting cancer prognosis or recurrence risk using a CAD-based quantitative image analysis method.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nastaran Emaminejad, Wei Qian, Yan Kang, Yubao Guan, Fleming Lure, and Bin Zheng "Exploring new quantitative CT image features to improve assessment of lung cancer prognosis", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141M (20 March 2015);

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