Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

Fujima, Noriyuki; Shimizu, Yukie; Yoshida, Daisuke; Kano, Satoshi; Mizumachi, Takatsugu; Homma, Akihiro; Yasuda, Koichi; Onimaru, Rikiya; Sakai, Osamu; Kudo, Kohsuke; Shirato, Hiroki

Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

Fujima, Noriyuki; Shimizu, Yukie; Yoshida, Daisuke et al.

Cancers, 2019, 11(6), 800

Permalink : https://hdl.handle.net/2115/75267

このアイテムのアクセス数:640件（2026-08-18 11:24 集計）

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https://doi.org/10.3390/cancers11060800			68

論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/75267
タイトル	Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study
著者	Fujima, Noriyuki ; Shimizu, Yukie ; Yoshida, Daisuke ; Kano, Satoshi ; Mizumachi, Takatsugu ; Homma, Akihiro ; Yasuda, Koichi ; Onimaru, Rikiya ; Sakai, Osamu ; Kudo, Kohsuke ; Shirato, Hiroki
e-Rad_Researcher 80431360 著者名 Fujima, Noriyuki
著者名 Shimizu, Yukie
著者名 Yoshida, Daisuke
e-Rad_Researcher 00374421 著者名 Kano, Satoshi
e-Rad_Researcher 00507577 著者名 Mizumachi, Takatsugu
e-Rad_Researcher 30312359 著者名 Homma, Akihiro
e-Rad_Researcher 00431362 著者名 Yasuda, Koichi
e-Rad_Researcher 80374461 著者名 Onimaru, Rikiya
著者名 Sakai, Osamu
e-Rad_Researcher 10374232 著者名 Kudo, Kohsuke
e-Rad_Researcher 20187537 著者名 Shirato, Hiroki
言語	英語
キーワード	Magnetic resonance imaging
	Machine learning
	Diffusion
	Perfusion
	Texture analysis
	Squamous cell carcinoma of the head and neck
発行日	2019-06
出版者	MDPI
収録物名	Cancers
巻(号)	11(6)
ページ	800
抄録	The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
権利情報	Creative Commons Attribution 4.0 International
EISSN	2072-6694
関連情報	PMID 31185611
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.3390/cancers11060800