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

• Published in: Cancers Vol. 11; no. 6; p. 800
• Main Authors: Fujima, Noriyuki, Shimizu, Yukie, Yoshida, Daisuke, Kano, Satoshi, Mizumachi, Takatsugu, Homma, Akihiro, Yasuda, Koichi, Onimaru, Rikiya, Sakai, Osamu, Kudo, Kohsuke, Shirato, Hiroki
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.06.2019; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Cancer therapies; Chemotherapy; Clinical outcomes; Head & neck cancer; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical prognosis; Medicine; Metastasis; Morphology; NMR; Nuclear magnetic resonance; Otolaryngology; Perfusion; Physical characteristics; Radiation therapy; Squamous cell carcinoma; Surgery; Japan; diffusion; magnetic resonance imaging; perfusion; machine learning; squamous cell carcinoma of the head and neck; texture analysis
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers11060800

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.