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

Cancers

Daisuke Yoshida¹  Yukie Shimizu¹  Noriyuki Fujima¹  Kohsuke Kudo¹  Takatsugu Mizumachi²  Satoshi Kano²  Akihiro Homma²  Rikiya Onimaru³  Koichi Yasuda³  Hiroki Shirato³  Osamu Sakai⁴ 

[1] Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan;Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan;Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan;Departments of Radiology, Otolaryngology-Head and Neck Surgery, and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA;

关键词: magnetic resonance imaging;    machine learning;    diffusion;    perfusion;    texture analysis;    squamous cell carcinoma of the head and neck;   

DOI  :  10.3390/cancers11060800

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown