Mengmeng Jiang1, Yiqian Zhang2, Junshen Xu3, Min Ji2, Yinglong Guo4, Yixian Guo1, Jie Xiao5, Xiuzhong Yao4, Hongcheng Shi5, Mengsu Zeng6. 1. Shanghai Institute of Medical Imaging, 180 Fenglin Road, Xuhui District, Shanghai, China. 2. Research Collaboration, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Road, Shanghai. 3. Department of Engineering Physics, Tsinghua University, Tsinghua University, Hai Dian, Beijing, People's Republic of China. 4. Shanghai Institute of Medical Imaging, Department of Radiology, Zhongshan Hospital, Fudan University, 138 Fenglin Road, Shanghai, People's Republic of China. 5. Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 130 Dongan Road, Shanghai, People's Republic of China. 6. Shanghai Institute of Medical Imaging, Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, 180 Fenglin Road, Shanghai, People's Republic of China.
Abstract
OBJECTIVE: The aim of this study was to investigate whether quantitative and qualitative features extracted from PET/computed tomography (CT) can be used as imaging biomarkers for evaluating epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer patients. METHODS: Eighty patients with stage II and III non-small cell lung cancer from January 2017 to December 2017 were included in this study. All patients underwent PET/CT examination before operation. Patients with 30 EGFR positive and 50 EGFR negative were confirmed by pathological verification and gene detection. Least absolute shrinkage and selection operator was used for analysis and selection of imaging features. Support vector machine was used to classify EGFR positive/negative using the selected features. Ten-fold cross validation was used to estimate the accuracy. RESULTS: A total of 512 quantitative features (radiomic features) were extracted from PET/CT (256 for PET and 256 for CT), and 12 qualitative features (semantic features) were extracted from CT. A total of 35 features were finally retained after least absolute shrinkage and selection operator (31 quantitative features and 4 qualitative features). The 35 selected features were significantly associated with EGFR mutation status. A predictive model was built using PET/CT data. Its performance was revealed as 0.953 using the area under the receiver operating characteristic curve. CONCLUSION: A predictive model using PET/CT images might be used to detect EGFR mutation status in non-small cell lung cancer patients.
OBJECTIVE: The aim of this study was to investigate whether quantitative and qualitative features extracted from PET/computed tomography (CT) can be used as imaging biomarkers for evaluating epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancerpatients. METHODS: Eighty patients with stage II and III non-small cell lung cancer from January 2017 to December 2017 were included in this study. All patients underwent PET/CT examination before operation. Patients with 30 EGFR positive and 50 EGFR negative were confirmed by pathological verification and gene detection. Least absolute shrinkage and selection operator was used for analysis and selection of imaging features. Support vector machine was used to classify EGFR positive/negative using the selected features. Ten-fold cross validation was used to estimate the accuracy. RESULTS: A total of 512 quantitative features (radiomic features) were extracted from PET/CT (256 for PET and 256 for CT), and 12 qualitative features (semantic features) were extracted from CT. A total of 35 features were finally retained after least absolute shrinkage and selection operator (31 quantitative features and 4 qualitative features). The 35 selected features were significantly associated with EGFR mutation status. A predictive model was built using PET/CT data. Its performance was revealed as 0.953 using the area under the receiver operating characteristic curve. CONCLUSION: A predictive model using PET/CT images might be used to detect EGFR mutation status in non-small cell lung cancerpatients.
Authors: Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba Journal: Eur J Hybrid Imaging Date: 2020-12-09