Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients

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 p...

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Main Authors: Kenta, Ninomiya, Hidetaka, Arimura, Kentaro, Tanaka, Chan, Wai Yee, Yutaro, Kabata, Shinichi, Mizuno, Nadia Fareeda, Muhammad Gowdh, Nur Adura, Yaakup, Liam, Chong Kin, Chai, Chee Shee, Ng, Kwan Hoong
Format: Article
Language:English
Published: ELSEVIER 2023
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Online Access:http://ir.unimas.my/id/eprint/41725/1/Three-dimensional%20topological%20radiogenomics%20of%20epidermal%20growth%20factor%20receptor%20Del19%20and%20L858R%20mutation%20subtypes%20on%20computed%20tomography%20images%20of%20lung%20cancer%20patients%20-%20ScienceDirect.pdf
http://ir.unimas.my/id/eprint/41725/
https://www.sciencedirect.com/science/article/abs/pii/S0169260723002092
https://doi.org/10.1016/j.cmpb.2023.107544
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spelling my.unimas.ir.417252023-04-19T07:55:53Z http://ir.unimas.my/id/eprint/41725/ Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients Kenta, Ninomiya Hidetaka, Arimura Kentaro, Tanaka Chan, Wai Yee Yutaro, Kabata Shinichi, Mizuno Nadia Fareeda, Muhammad Gowdh Nur Adura, Yaakup Liam, Chong Kin Chai, Chee Shee Ng, Kwan Hoong R Medicine (General) 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 Čech 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. ELSEVIER 2023-04-13 Article PeerReviewed text en http://ir.unimas.my/id/eprint/41725/1/Three-dimensional%20topological%20radiogenomics%20of%20epidermal%20growth%20factor%20receptor%20Del19%20and%20L858R%20mutation%20subtypes%20on%20computed%20tomography%20images%20of%20lung%20cancer%20patients%20-%20ScienceDirect.pdf Kenta, Ninomiya and Hidetaka, Arimura and Kentaro, Tanaka and Chan, Wai Yee and Yutaro, Kabata and Shinichi, Mizuno and Nadia Fareeda, Muhammad Gowdh and Nur Adura, Yaakup 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. ISSN 1872-7565 https://www.sciencedirect.com/science/article/abs/pii/S0169260723002092 https://doi.org/10.1016/j.cmpb.2023.107544
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic R Medicine (General)
spellingShingle R Medicine (General)
Kenta, Ninomiya
Hidetaka, Arimura
Kentaro, Tanaka
Chan, Wai Yee
Yutaro, Kabata
Shinichi, Mizuno
Nadia Fareeda, Muhammad Gowdh
Nur Adura, Yaakup
Liam, Chong Kin
Chai, Chee Shee
Ng, Kwan Hoong
Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
description 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 Čech 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.
format Article
author Kenta, Ninomiya
Hidetaka, Arimura
Kentaro, Tanaka
Chan, Wai Yee
Yutaro, Kabata
Shinichi, Mizuno
Nadia Fareeda, Muhammad Gowdh
Nur Adura, Yaakup
Liam, Chong Kin
Chai, Chee Shee
Ng, Kwan Hoong
author_facet Kenta, Ninomiya
Hidetaka, Arimura
Kentaro, Tanaka
Chan, Wai Yee
Yutaro, Kabata
Shinichi, Mizuno
Nadia Fareeda, Muhammad Gowdh
Nur Adura, Yaakup
Liam, Chong Kin
Chai, Chee Shee
Ng, Kwan Hoong
author_sort Kenta, Ninomiya
title Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
title_short Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
title_full Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
title_fullStr Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
title_full_unstemmed Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients
title_sort three-dimensional topological radiogenomics of epidermal growth factor receptor del19 and l858r mutation subtypes on computed tomography images of lung cancer patients
publisher ELSEVIER
publishDate 2023
url http://ir.unimas.my/id/eprint/41725/1/Three-dimensional%20topological%20radiogenomics%20of%20epidermal%20growth%20factor%20receptor%20Del19%20and%20L858R%20mutation%20subtypes%20on%20computed%20tomography%20images%20of%20lung%20cancer%20patients%20-%20ScienceDirect.pdf
http://ir.unimas.my/id/eprint/41725/
https://www.sciencedirect.com/science/article/abs/pii/S0169260723002092
https://doi.org/10.1016/j.cmpb.2023.107544
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