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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; Shimizu, Yukie; Yoshida, Daisuke; Kano, Satoshi; Mizumachi, Takatsugu; Homma, Akihiro; Yasuda, Koichi; Onimaru, Rikiya; Sakai, Osamu; Kudo, Kohsuke; Shirato, Hiroki
- 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.
- Subjects
CANCER treatment; MACHINE learning; MAGNETIC resonance imaging; NASAL cavity; NASAL tumors; PARANASAL sinus cancer; SQUAMOUS cell carcinoma; QUANTITATIVE research; SYMPTOMS; TREATMENT effectiveness; PREDICTIVE tests
- Publication
Cancers, 2019, Vol 11, Issue 6, p800
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers11060800