Ninomiya, Kenta and Arimura, Hidetaka and Tanaka, Kentaro and Chan, Wai Yee and Kabata, Yutaro and Mizuno, Shinichi and Gowdh, Nadia Fareeda Muhammad and Yaakup, Nur Adura and Liam, Chong-Kin and Chai, Chee-Shee and Ng, Kwan Hoong (2023) Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients. Computer Methods and Programs in Biomedicine, 236. ISSN 0169-2607, DOI https://doi.org/10.1016/j.cmpb.2023.107544.
Full text not available from this repository.Abstract
Objectives: To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor ( EGFR ) Del19 and L858R mutation subtypes. Methods: In total, 154 patients (wild-type EGFR , 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation M] classification) as well as between the Del19 and L858R subtypes (subtype S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Cech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling. Results: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively. Conclusion: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features. (c) 2023 Elsevier B.V. Al lrights reserved.
| Item Type: | Article |
|---|---|
| Funders: | Center for Clinical and Translational Research of Kyushu University Hospital, Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) (JP21J12635) ; (JP20K08084) |
| Uncontrolled Keywords: | Radiogenomics; Computational topology; Molecularly targeted drugs; Precision medicine |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine > Biomedical Imaging Department Faculty of Medicine > Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Jul 2025 04:21 |
| Last Modified: | 28 Jul 2025 04:21 |
| URI: | http://eprints.um.edu.my/id/eprint/50795 |
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