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  1. 10 学術雑誌論文
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Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network

https://teikyo-u.repo.nii.ac.jp/records/2063931
https://teikyo-u.repo.nii.ac.jp/records/2063931
b40e4726-2cdd-4583-9126-86e0f16d0a4b
Item type Multiple(1)
公開日 2025-09-10
タイトル
タイトル Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network
言語 en
作成者 亀澤, 秀美

× 亀澤, 秀美

en kamezawa, hidemi

ja 亀澤, 秀美

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Arimura, H.

× Arimura, H.

Arimura, H.
Arimura
H.

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Soufi, M.

× Soufi, M.

Soufi, M.
Soufi
M.

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寄与者
姓 Chen
寄与者
姓 Zhang
内容記述
内容記述タイプ Abstract
内容記述 <p>The goal of this study was to investigate the survival prediction of squamous cell head and neck cancer (SCHNC) patients by using radiomic features that were selected using an artificial neural network (ANN). We employed computed tomography (CT) images of 86 squamous cell lung cancer (SCLC) patients for the feature selection, and 30 SCHNC patients for a test of the selected features. 486 radiomic features, i.e., statistic, texture, wavelet-based features, were extracted from the tumor regions in the CT images. The ANN was constructed for selecting 10 features that could classify the SCLC patients into shorter and longer survival groups than 2 years. The features were selected based on weights with strong links between the features and predicted survival in ANN. The survival times of the SCHNC patients, who were divided into two groups with respect to the median of each of the top 10 ranked features, were estimated using a Kaplan-Meier method. The statistical significant differences between survival curves of the two groups were assessed for the 10 features using a log-rank test. The homogeneity feature of the wavelet-based HHL image (HHL-Homogeneity) demonstrated a statistically significant difference (p &lt; 0.01) between the two groups of SCHNC, but the other 9 features did not. Our results suggest that the 2-year survival of the SCHNC patients could be predicted by using at least the radiomic feature selected among the features for SCLC patients using the ANN-based feature selection approach.</p>
言語 en
日付
日付 2018
日付タイプ Issued
Language
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_f744
資源タイプ conference proceedings
関連情報
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 10.1117/12.2293415
関連情報
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 9781510616479
関連情報
関連タイプ inSeries
関連名称 Progress in Biomedical Optics and Imaging - Proceedings of SPIE
収録物識別子
収録物識別子タイプ ISSN
収録物識別子 1605-7422
収録物名
収録物名 Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
言語 en
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