Ms. Ma Jun Profile

Ma Jun

at Ocean Univ of China

SPIE Involvement:

Author

Publications (2)

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 | 11 December 2012 Paper

Distinction of gastric cancer tissue based on surface-enhanced Raman spectroscopy

Jun Ma, Hanjing Zhou, Longjing Gong, Shu Liu, Zhenghua Zhou, Weizheng Mao, Rong-er Zheng

Proceedings Volume 8553, 855328 (2012) https://doi.org/10.1117/12.999394

KEYWORDS: Tissues, Cancer, Surface enhanced Raman spectroscopy, Raman spectroscopy, Proteins, Diagnostics, Gold, Principal component analysis, Tumors, Tissue optics

Read Abstract +

Gastric cancer is one of the most common malignant tumors with high recurrence rate and mortality rate in China. This study aimed to evaluate the diagnostic capability of Surface-enhanced Raman spectroscopy (SERS) based on gold colloids for distinguishing gastric tissues. Gold colloids were directly mixed with the supernatant of homogenized tissues to heighten the Raman signal of various biomolecule. A total of 56 samples were collected from normal (30) and cancer (26). Raman spectra were obtained with a 785nm excitation in the range of 600-1800 cm^-1. Significant spectral differences in SERS mainly belong to nucleic acid, proteins and lipids, particularly in the range of 653, 726, 828, 963, 1004, 1032, 1088, 1130, 1243, 1369, 1474, 1596, 1723 cm^-1. PCA-LDA algorithms with leave-one-patient-out cross validation yielded diagnostic sensitivities of 90% (27/30), specificities of 88.5% (23/26), and accuracy of 89.3% (50/56), for classification of normal and cancer tissues. The receiver operating characteristic (ROC) surface is 0.917, illustrating the diagnostic utility of SERS together with PCA-LDA to identify gastric cancer from normal tissue. This work demonstrated the SERS techniques can be useful for gastric cancer detection, and it is also a potential technique for accurately identifying cancerous tumor, which is of considerable clinical importance to real-time diagnosis.

Proceedings Article | 10 June 2011 Paper

Near-infrared raman spectroscopy for detection of gastric cancer peritoneal dissemination in vivo

Jun Ma, Wei-zheng Mao, Ming Xu, Long-jing Gong, Yuan Gao, Han-jing Zhou, Rong-er Zheng

Proceedings Volume 8087, 808714 (2011) https://doi.org/10.1117/12.889431

KEYWORDS: Raman spectroscopy, Tissues, Cancer, Tumors, In vivo imaging, Proteins, Diagnostics, Tissue optics, Spectroscopy, Biomedical optics

Read Abstract +

The nude mice injected with human gastric cancer cells (SGC-7901) in their peritoneums were chosen as the animal models of gastric cancer peritoneal dissemination in this research. The Raman spectra at 785nm excitation of both these nude mice which were in different tumor planting periods and the normal counterpart were taken in vivo in the imitate laparotomy. 205 spectra were collected. The spectra of different tissue types were compared and classified by Support Vector Machine (SVM) algorithm. Significant differences were showed between normal and malignant tissues. The gastric cancer nodules had lower Raman intensities at 870, 1330, 1450, and 1660cm^-1, but higher at 1007, 1050, 1093 and 1209cm^-1, compared with normal tissues. Additionally, the spectra of malignant tissues had two peaks around 1330 cm^-1 (1297cm^-1 and 1331cm^-1), while the spectra of normal tissues had only one peak (1297cm^-1). The differences were attributed to the intensities of the stretching bands of the nucleic acid, protein and water. These features could be used to diagnose gastric cancer. The Support Vector Machine (SVM) algorithm was used to classify these spectra. For normal and malignant tissues, the sensitivity, specificity and accuracy were 95.73%, 70.73% and 90.73%, respectively, while for different tumor planting periods, they were 98.82%, 98.73% and 98.78%. The experimental results show that Raman spectra differ significantly between cancerous and normal gastric tissues, which provides the experimental basis for the diagnosis of gastric cancer by Raman spectroscopy technology. And SVM algorithm can give the well generalized classification performance for the samples, which expands the application of mathematical algorithms in the classification.

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