We have developed a magnetic resonance (MR) image-based radiomic biopsy approach for estimation of malignancy grade in parotid gland cancer (PGC). Preoperative T1- and T2-weighted MR images of 39 PGC patients with 20 highand 19 intermediate-/low-malignancy grades were employed. High- versus intermediate-/low-malignancy grades were estimated using MR-radiomic biopsy approaches, i.e. 972 hand-crafted feature and transfer learning of five pre-trained deep learning (DL) architectures (AlexNet, GoogLeNet, VGG-16, ResNet-101, DenseNet-201). The 39 patients were divided into 70% for training datasets and 30% for test datasets. The hand-crafted features were extracted from cancer regions in T1- and T2-weighted MR images. Three features were selected as a radiomic signature by using a least absolute shrinkage and selection operator (LASSO), whose coefficients of three features were used for constructing the radiomic score (Rad-score). The two grade malignancy was estimated by using an optimal cut-off value of Rad-score. On the other hand, last three layers of the DL architectures were replaced with new three layers for the estimation task. The DL architectures were fine-tuned with training datasets and were evaluated with test datasets. The performances of the MR-radiomic biopsy approaches were assessed by using the accuracy and the area under the receiver operating characteristic curve (AUC). The VGG-16 demonstrated the best performance (accuracy=85.4%, AUC=0.906), but the other approaches showed worse performances (Rad-score: 83.3%, 0.830, AlexNet: 84.4%, 0.915, GoogLeNet: 84.9%, 0.884, ResNet-101: 84.9%, 0.918, DenseNet-201: 84.4%, 0.869) than the VGG-16. The VGG-16-based MR-radiomic biopsy could be feasible for the malignancy grade estimation of PGC.
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.