20 March 2015 Exploring new quantitative CT image features to improve assessment of lung cancer prognosis
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Abstract
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.
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Nastaran Emaminejad, Nastaran Emaminejad, Wei Qian, Wei Qian, Yan Kang, Yan Kang, Yubao Guan, Yubao Guan, Fleming Lure, Fleming Lure, Bin Zheng, 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); doi: 10.1117/12.2081617; https://doi.org/10.1117/12.2081617
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