Kenta Ninomiya1, Hidetaka Arimura2, Wai Yee Chan3, Kentaro Tanaka4, Shinichi Mizuno5, Nadia Fareeda Muhammad Gowdh3, Nur Adura Yaakup3, Chong-Kin Liam6, Chee-Shee Chai7, Kwan Hoong Ng3. 1. Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 2. Faculty of Medical Sciences, Division of Medical Quantum Science, Department of Health Sciences, Kyushu University, Fukuoka, Japan. 3. Faculty of Medicine, Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia. 4. Department of Respiratory Medicine, Kyushu University Hospital, Fukuoka, Japan. 5. Division of Medical Sciences and Technology, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 6. Faculty of Medicine, Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia. 7. Faculty of Medicine and Health Science, Department of Medicine, University Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia.
Abstract
OBJECTIVES: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
OBJECTIVES: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLCpatients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLCpatients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
Authors: A Manhire; M Charig; C Clelland; F Gleeson; R Miller; H Moss; K Pointon; C Richardson; E Sawicka Journal: Thorax Date: 2003-11 Impact factor: 9.139
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