Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

• Main Authors: Ninomiya, Kenta, Arimura, Hidetaka, Chan, Wai Yee, Tanaka, Kentaro, Mizuno, Shinichi, Nadia Fareeda, Muhammad Gowdh, Nur Adura, Yaakup, Liam, Chong Kin, Chai, Chee Shee, Ng, Kwan Hoong
• Format: Article
• Language: English
• Published: PLOS 2021
• Subjects: RC0254 Neoplasms. Tumors. Oncology (including Cancer); RZ Other systems of medicine
• Online Access: http://ir.unimas.my/id/eprint/33843/1/Chee%20Shee.pdf; http://ir.unimas.my/id/eprint/33843/; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244354; https://doi.org/10.1371/journal. pone.0244354

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.