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27 March 2019 Radiomics-based malignancy prediction of parotid gland tumor
H. Kamezawa, H. Arimura, R. Yasumatsu, K. Ninomiya, S. Haseai
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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 1105019 (2019) https://doi.org/10.1117/12.2521362
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore
Abstract
We have investigated an approach for prediction of parotid gland tumor (PGT) malignancy on preoperative magnetic resonance (MR) images. The PGT regions were segmented on the MR images of 42 patients. A total of 972 radiomic features were extracted from tumor regions in T1- and T2-weighted MR images. Five features were selected as a radiomic biomarker from the 972 features by using a least absolute shrinkage and selection operator (LASSO). Malignancies of PGTs (high grade versus intermediate and low grades) were predicted by using random forest (RF) and k-nearest neighbors (k-NN) with the radiomic biomarker. The proposed approach was evaluated using the accuracy and the mean area under the receiver operating characteristic curve (AUC) based on a leave-one-out cross validation test. The accuracy and AUC of the malignancy prediction of PGTs were 73.8% and 0.88 for the RF and 88.1% and 0.95 for the k-NN, respectively. Our results suggested that the radiomics-based k-NN approach using preoperative MR images could be feasible to predict the malignancy of PGT.
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H. Kamezawa, H. Arimura, R. Yasumatsu, K. Ninomiya, and S. Haseai "Radiomics-based malignancy prediction of parotid gland tumor", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105019 (27 March 2019); https://doi.org/10.1117/12.2521362
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KEYWORDS
Tumors
Magnetic resonance imaging
Feature extraction
Wavelets
Machine learning
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