Radiomics-based Malignancy Prediction of Parotid Gland Tumor

被引：0

作者：

Kamezawa, H. ^{[1
,2
]}

Arimura, H. ^{[2
]}

Yasumatsu, R. ^{[2
]}

Ninomiya, K. ^{[3
]}

Haseai, S. ^{[3
]}

机构：

[1] Teikyo Univ, Fac Fukuoka Med Technol, 6-22 Misaki Machi, Omuta, Fukuoka 8368505, Japan

[2] Kyushu Univ, Fac Med Sci, Higashi Ku, 3-1-1 Maidashi, Fukuoka, Fukuoka 8128582, Japan

[3] Kyushu Univ, Grad Sch Med Sci, Higashi Ku, 3-1-1 Maidashi, Fukuoka, Fukuoka 8128582, Japan

来源：

INTERNATIONAL FORUM ON MEDICAL IMAGING IN ASIA 2019 | 2019年 / 11050卷

关键词：

Radiomics; Malignancy prediction; Parotid gland tumor; Machine learning classifier; TEXTURAL FEATURES;

D O I：

10.1117/12.2521362

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页数：4