Dr. Hidemi Kamezawa Profile

Dr. Hidemi Kamezawa

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 2 March 2020 Paper

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

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

Proceedings Volume 11318, 1131818 (2020) https://doi.org/10.1117/12.2549462

KEYWORDS: Biopsy, Biological research, Cancer, Magnetic resonance imaging, Feature extraction

Read 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.

Proceedings Article | 27 March 2019 Paper

Radiomics-based malignancy prediction of parotid gland tumor

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

Proceedings Volume 11050, 1105019 (2019) https://doi.org/10.1117/12.2521362

KEYWORDS: Tumors, Magnetic resonance imaging, Feature extraction, Wavelets, Machine learning

Read 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.

Proceedings Article | 6 March 2018 Paper

Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network

H. Kamezawa, H. Arimura, M. Soufi

Proceedings Volume 10579, 1057918 (2018) https://doi.org/10.1117/12.2293415

KEYWORDS: Lung cancer, Cancer, Head, Neck, Artificial neural networks, Feature selection, Computed tomography, Feature extraction, Tumors, Wavelet transforms

Read Abstract +

The goal of this study was to investigate the survival prediction of squamous cell head and neck cancer (SCHNC) patients by using radiomic features that were selected using an artificial neural network (ANN). We employed computed tomography (CT) images of 86 squamous cell lung cancer (SCLC) patients for the feature selection, and 30 SCHNC patients for a test of the selected features. 486 radiomic features, i.e., statistic, texture, wavelet-based features, were extracted from the tumor regions in the CT images. The ANN was constructed for selecting 10 features that could classify the SCLC patients into shorter and longer survival groups than 2 years. The features were selected based on weights with strong links between the features and predicted survival in ANN. The survival times of the SCHNC patients, who were divided into two groups with respect to the median of each of the top 10 ranked features, were estimated using a Kaplan-Meier method. The statistical significant differences between survival curves of the two groups were assessed for the 10 features using a log-rank test. The homogeneity feature of the wavelet-based HHL image (HHL_Homogeneity) demonstrated a statistically significant difference (p < 0.01) between the two groups of SCHNC, but the other 9 features did not. Our results suggest that the 2-year survival of the SCHNC patients could be predicted by using at least the radiomic feature selected among the features for SCLC patients using the ANN-based feature selection approach.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years