MR-radiomic biopsy for estimation of malignancy grade in parotid gland cancer

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

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

Abstract

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.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPo-Hao Chen, Thomas M. Deserno
PublisherSPIE
ISBN (Electronic)9781510634039
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications - Houston, United States
Duration: Feb 16 2020Feb 17 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11318
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CityHouston
Period2/16/202/17/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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