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

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Title: Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study
Authors: Fujima, Noriyuki Browse this author →KAKEN DB
Shimizu, Yukie Browse this author
Yoshida, Daisuke Browse this author
Kano, Satoshi Browse this author →KAKEN DB
Mizumachi, Takatsugu Browse this author →KAKEN DB
Homma, Akihiro Browse this author →KAKEN DB
Yasuda, Koichi Browse this author →KAKEN DB
Onimaru, Rikiya Browse this author →KAKEN DB
Sakai, Osamu Browse this author
Kudo, Kohsuke Browse this author →KAKEN DB
Shirato, Hiroki Browse this author →KAKEN DB
Keywords: Magnetic resonance imaging
Machine learning
Diffusion
Perfusion
Texture analysis
Squamous cell carcinoma of the head and neck
Issue Date: Jun-2019
Publisher: MDPI
Journal Title: Cancers
Volume: 11
Issue: 6
Start Page: 800
Publisher DOI: 10.3390/cancers11060800
PMID: 31185611
Abstract: 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.
Rights: https://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/75267
Appears in Collections:医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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