Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques
This study aims to detect colorectal cancer with near-infrared Raman spectroscopy and feature selection techniques. A total of 306 Raman spectra of colorectal cancer tissues and normal tissues are acquired from 44 colorectal cancer patients. Five diagnostically important Raman bands in the regions o...
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| Veröffentlicht in: | Optics express 2014-10, Vol.22 (21), p.25895-25908 |
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| container_issue | 21 |
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| container_title | Optics express |
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| creator | Li, Shaoxin Chen, Gong Zhang, Yanjiao Guo, Zhouyi Liu, Zhiming Xu, Junfa Li, Xueqiang Lin, Lin |
| description | This study aims to detect colorectal cancer with near-infrared Raman spectroscopy and feature selection techniques. A total of 306 Raman spectra of colorectal cancer tissues and normal tissues are acquired from 44 colorectal cancer patients. Five diagnostically important Raman bands in the regions of 815-830, 935-945, 1131-1141, 1447-1457 and 1665-1675 cm(-1) related to proteins, nucleic acids and lipids of tissues are identified with the ant colony optimization (ACO) and support vector machine (SVM). The diagnostic models built with the identified Raman bands provide a diagnostic accuracy of 93.2% for identifying colorectal cancer from normal Raman spectroscopy. The study demonstrates that the Raman spectroscopy associated with ACO-SVM diagnostic algorithms has great potential to characterize and diagnose colorectal cancer. |
| doi_str_mv | 10.1364/OE.22.025895 |
| format | Article |
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A total of 306 Raman spectra of colorectal cancer tissues and normal tissues are acquired from 44 colorectal cancer patients. Five diagnostically important Raman bands in the regions of 815-830, 935-945, 1131-1141, 1447-1457 and 1665-1675 cm(-1) related to proteins, nucleic acids and lipids of tissues are identified with the ant colony optimization (ACO) and support vector machine (SVM). The diagnostic models built with the identified Raman bands provide a diagnostic accuracy of 93.2% for identifying colorectal cancer from normal Raman spectroscopy. The study demonstrates that the Raman spectroscopy associated with ACO-SVM diagnostic algorithms has great potential to characterize and diagnose colorectal cancer.</description><identifier>ISSN: 1094-4087</identifier><identifier>EISSN: 1094-4087</identifier><identifier>DOI: 10.1364/OE.22.025895</identifier><identifier>PMID: 25401621</identifier><language>eng</language><publisher>United States</publisher><subject>Adenocarcinoma - diagnosis ; Adenocarcinoma - pathology ; Algorithms ; Colorectal Neoplasms - diagnosis ; Colorectal Neoplasms - pathology ; Female ; Humans ; Imaging, Three-Dimensional ; Male ; Middle Aged ; ROC Curve ; Spectrum Analysis, Raman - methods ; Support Vector Machine</subject><ispartof>Optics express, 2014-10, Vol.22 (21), p.25895-25908</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-141288760829e5962886f9a700fe73beac6082fe798ac061468d62cc1f6f57423</citedby><cites>FETCH-LOGICAL-c395t-141288760829e5962886f9a700fe73beac6082fe798ac061468d62cc1f6f57423</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25401621$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Shaoxin</creatorcontrib><creatorcontrib>Chen, Gong</creatorcontrib><creatorcontrib>Zhang, Yanjiao</creatorcontrib><creatorcontrib>Guo, Zhouyi</creatorcontrib><creatorcontrib>Liu, Zhiming</creatorcontrib><creatorcontrib>Xu, Junfa</creatorcontrib><creatorcontrib>Li, Xueqiang</creatorcontrib><creatorcontrib>Lin, Lin</creatorcontrib><title>Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques</title><title>Optics express</title><addtitle>Opt Express</addtitle><description>This study aims to detect colorectal cancer with near-infrared Raman spectroscopy and feature selection techniques. A total of 306 Raman spectra of colorectal cancer tissues and normal tissues are acquired from 44 colorectal cancer patients. Five diagnostically important Raman bands in the regions of 815-830, 935-945, 1131-1141, 1447-1457 and 1665-1675 cm(-1) related to proteins, nucleic acids and lipids of tissues are identified with the ant colony optimization (ACO) and support vector machine (SVM). The diagnostic models built with the identified Raman bands provide a diagnostic accuracy of 93.2% for identifying colorectal cancer from normal Raman spectroscopy. The study demonstrates that the Raman spectroscopy associated with ACO-SVM diagnostic algorithms has great potential to characterize and diagnose colorectal cancer.</description><subject>Adenocarcinoma - diagnosis</subject><subject>Adenocarcinoma - pathology</subject><subject>Algorithms</subject><subject>Colorectal Neoplasms - diagnosis</subject><subject>Colorectal Neoplasms - pathology</subject><subject>Female</subject><subject>Humans</subject><subject>Imaging, Three-Dimensional</subject><subject>Male</subject><subject>Middle Aged</subject><subject>ROC Curve</subject><subject>Spectrum Analysis, Raman - methods</subject><subject>Support Vector Machine</subject><issn>1094-4087</issn><issn>1094-4087</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpNkL1PwzAQxS0EoqWwMaOMDKTYjuPEI6oKVKpUCcEcuZczDcpHsZ2h_PU4pCCm-3g_vdM9Qq4ZnbNEivvNcs75nPI0V-kJmTKqRCxonp3-6yfkwrkPSpnIVHZOJjwVlEnOpsSuSmx9ZSrQvuraSLdlBDttNXi01de47EwEXd1ZBK_rCHQLaKPeVe179KIb3UZuHyTbOej2hx8Lg9r3FiOHdVAGD4-wa6vPHt0lOTO6dnh1rDPy9rh8XTzH683TavGwjiFRqY-ZYDzPM0lzrjBVMgzSKJ1RajBLtqhhkEKvcg1UMiHzUnIAZqRJM8GTGbkdffe2G-76oqkcYF3rFrveFSEAySRTVAT0bkQhPOEsmmJvq0bbQ8FoMaRcbJYF58WYcsBvjs79tsHyD_6NNfkGGHR44w</recordid><startdate>20141020</startdate><enddate>20141020</enddate><creator>Li, Shaoxin</creator><creator>Chen, Gong</creator><creator>Zhang, Yanjiao</creator><creator>Guo, Zhouyi</creator><creator>Liu, Zhiming</creator><creator>Xu, Junfa</creator><creator>Li, Xueqiang</creator><creator>Lin, Lin</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20141020</creationdate><title>Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques</title><author>Li, Shaoxin ; Chen, Gong ; Zhang, Yanjiao ; Guo, Zhouyi ; Liu, Zhiming ; Xu, Junfa ; Li, Xueqiang ; Lin, Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-141288760829e5962886f9a700fe73beac6082fe798ac061468d62cc1f6f57423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adenocarcinoma - diagnosis</topic><topic>Adenocarcinoma - pathology</topic><topic>Algorithms</topic><topic>Colorectal Neoplasms - diagnosis</topic><topic>Colorectal Neoplasms - pathology</topic><topic>Female</topic><topic>Humans</topic><topic>Imaging, Three-Dimensional</topic><topic>Male</topic><topic>Middle Aged</topic><topic>ROC Curve</topic><topic>Spectrum Analysis, Raman - methods</topic><topic>Support Vector Machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Shaoxin</creatorcontrib><creatorcontrib>Chen, Gong</creatorcontrib><creatorcontrib>Zhang, Yanjiao</creatorcontrib><creatorcontrib>Guo, Zhouyi</creatorcontrib><creatorcontrib>Liu, Zhiming</creatorcontrib><creatorcontrib>Xu, Junfa</creatorcontrib><creatorcontrib>Li, Xueqiang</creatorcontrib><creatorcontrib>Lin, Lin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Optics express</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Shaoxin</au><au>Chen, Gong</au><au>Zhang, Yanjiao</au><au>Guo, Zhouyi</au><au>Liu, Zhiming</au><au>Xu, Junfa</au><au>Li, Xueqiang</au><au>Lin, Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques</atitle><jtitle>Optics express</jtitle><addtitle>Opt Express</addtitle><date>2014-10-20</date><risdate>2014</risdate><volume>22</volume><issue>21</issue><spage>25895</spage><epage>25908</epage><pages>25895-25908</pages><issn>1094-4087</issn><eissn>1094-4087</eissn><abstract>This study aims to detect colorectal cancer with near-infrared Raman spectroscopy and feature selection techniques. 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| ispartof | Optics express, 2014-10, Vol.22 (21), p.25895-25908 |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Adenocarcinoma - diagnosis Adenocarcinoma - pathology Algorithms Colorectal Neoplasms - diagnosis Colorectal Neoplasms - pathology Female Humans Imaging, Three-Dimensional Male Middle Aged ROC Curve Spectrum Analysis, Raman - methods Support Vector Machine |
| title | Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques |
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