Radiomics-based malignancy prediction of parotid gland tumor

H. Kamezawa, H. Arimura, R. Yasumatsu, K. Ninomiya, S. Haseai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsHiroshi Fujita, Jong Hyo Kim, Feng Lin
PublisherSPIE
ISBN (Electronic)9781510627758
DOIs
Publication statusPublished - Jan 1 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: Jan 7 2019Jan 9 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11050
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
CountrySingapore
CitySingapore
Period1/7/191/9/19

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Kamezawa, H., Arimura, H., Yasumatsu, R., Ninomiya, K., & Haseai, S. (2019). Radiomics-based malignancy prediction of parotid gland tumor. In H. Fujita, J. H. Kim, & F. Lin (Eds.), International Forum on Medical Imaging in Asia 2019 [1105019] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11050). SPIE. https://doi.org/10.1117/12.2521362