Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms
| Autor: | Hamid Abdollahi, Habib Zaidi, Mathieu Hatt, Arman Rahmim, Isaac Shiri, Ghasem Hajianfar, Mehrdad Oveisi, Hasan Maleki, Saeed Ashrafinia |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Male Cancer Research Computer Science - Machine Learning Lung Neoplasms INFORMATION IMPACT Radiogenomics Gene mutation computer.software_genre medicine.disease_cause NSCLC Multimodal Imaging 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 0302 clinical medicine Carcinoma Non-Small-Cell Lung HETEROGENEITY RADIOMIC FEATURES medicine.diagnostic_test High-Throughput Nucleotide Sequencing Genomics K-RAS MUTATIONS 3. Good health ErbB Receptors Oncology Positron emission tomography Biological Physics (physics.bio-ph) Area Under Curve Female KRAS Algorithm Algorithms CT PET/CT EGFR CELL LUNG-CANCER IMAGES BIOMARKERS FOS: Physical sciences Feature selection Standardized uptake value Machine learning ddc:616.0757 Proto-Oncogene Proteins p21(ras) 03 medical and health sciences medicine Humans Radiology Nuclear Medicine and imaging Quantitative Biology - Genomics Physics - Biological Physics neoplasms Aged Genomics (q-bio.GN) PET-CT Receiver operating characteristic business.industry Physics - Medical Physics respiratory tract diseases ddc:616.8 PET FOS: Biological sciences Positron-Emission Tomography MARKER Multivariate Analysis Mutation Artificial intelligence Medical Physics (physics.med-ph) business GROWTH-FACTOR RECEPTOR computer |
| Zdroj: | Molecular Imaging and Biology, Vol. 22, No 4 (2020) pp. 1132-1148 Shiri, I, Maleki, H, Hajianfar, G, Abdollahi, H, Ashrafinia, S, Hatt, M, Zaidi, H, Oveisi, M & Rahmim, A 2020, ' Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms ', Molecular Imaging and Biology, vol. 22, no. 4, pp. 1132-1148 . https://doi.org/10.1007/s11307-020-01487-8 Molecular Imaging and Biology, 22(4), 1132-1148. SPRINGER |
| ISSN: | 1536-1632 |
| Popis: | Aim: In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on PET and CT multi-modalities radiomic features and machine learning (ML) algorithms. Methods: Our study involved 211 NSCLC cancer patient with PET and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed Six feature selection methods and twelve classifiers were used to predict gene status in patient and model evaluation was reported on independent validation sets (68 patients). Results: The best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). Conclusion: We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters. Comment: 42 pages,3 Figures,4 Tables, 13 Supplemental Figures, 11 Supplemental Tables |
| Databáze: | OpenAIRE |
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