Mr. Kenta Ninomiya Profile

Kenta Ninomiya

at Sanford Burnham Prebys Medical Discovery Institute

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Author

Publications (6)

Proceedings Article | 7 April 2023 Poster + Paper

Topological prediction models for relapse of stage I patients with non-small cell lung cancer prior to stereotactic ablative radiotherarpy

T. Kodama, H. Arimura, Y. Shirakawa, K. Ninomiya, T. Yoshitake, Y. Shioyama

Proceedings Volume 12465, 1246525 (2023) https://doi.org/10.1117/12.2653814

KEYWORDS: Tumors, Radiomics, Lung cancer, Education and training, Computed tomography, Wavelets, Binary data, Cancer, Ablation, Tumor growth modeling

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Proceedings Article | 10 March 2020 Presentation + Paper

Homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features

K. Ninomiya, H. Arimura

Proceedings Volume 11313, 113131N (2020) https://doi.org/10.1117/12.2548918

KEYWORDS: Lung cancer, Tumors, Computed tomography, Linear filtering, Cancer, Wavelets, Radiotherapy, Binary data, Health sciences, Statistical modeling

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Proceedings Article | 2 March 2020 Paper

MR-radiomic biopsy for estimation of malignancy grade in parotid gland cancer

H. Kamezawa, H. Arimura, R. Yasumatsu, K. Ninomiya

Proceedings Volume 11318, 1131818 (2020) https://doi.org/10.1117/12.2549462

KEYWORDS: Biopsy, Biological research, Cancer, Magnetic resonance imaging, Feature extraction

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Proceedings Article | 27 March 2019 Paper

Automated classification of histological subtypes of NSCLC using support vector machines with radiomic features

Masahiro Yamada, Hidetaka Arimura, Kenta Ninomiya, Mazen Soufi

Proceedings Volume 11050, 110500P (2019) https://doi.org/10.1117/12.2521511

KEYWORDS: Lung cancer, Computed tomography, Tumors, Biopsy, Tissues, Medical imaging, Health sciences, Cancer, Feature extraction, Feature selection

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Proceedings Article | 27 March 2019 Paper

Radiomics-based malignancy prediction of parotid gland tumor

H. Kamezawa, H. Arimura, R. Yasumatsu, K. Ninomiya, S. Haseai

Proceedings Volume 11050, 1105019 (2019) https://doi.org/10.1117/12.2521362

KEYWORDS: Tumors, Magnetic resonance imaging, Feature extraction, Wavelets, Machine learning

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Proceedings Article | 13 March 2018 Paper

Bayesian delineation framework of clinical target volumes for prostate cancer radiotherapy using an anatomical-features-based machine learning technique

K. Ninomiya, H. Arimura, M. Sasahara, T. Hirose, S. Ohga, Y. Umezu, H. Honda, T. Sasaki

Proceedings Volume 10576, 105761Z (2018) https://doi.org/10.1117/12.2293463

KEYWORDS: Bladder, Rectum, Radiotherapy, Prostate cancer, Computed tomography, Machine learning, Bone, Prostate, Bayesian inference

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Showing 5 of 6 publications

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