Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients
Background Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology. Aims The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorec...
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| Veröffentlicht in: | Digestive diseases and sciences 2021-04, Vol.66 (4), p.1227-1232 |
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| creator | Takahashi, Yumiko Hayano, Koichi Ohira, Gaku Imanishi, Shunsuke Hanaoka, Toshiharu Watanabe, Hiroki Hirata, Atsushi Kawasaki, Yohei Miyauchi, Hideaki Matsubara, Hisahiro |
| description | Background
Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology.
Aims
The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value.
Methods
This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at
b
= 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at
b
= 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan–Meier analysis.
Results
ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32,
p
= 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58,
p
= 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (
p
= 0.04,
p
= 0.03, respectively), and skewness was significantly correlated with OS (
p
= 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan–Meier analysis (
p
= 0.01,
p
= 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (
p
= 0.04).
Conclusions
Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer. |
| doi_str_mv | 10.1007/s10620-020-06318-y |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_2404044735</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A712933613</galeid><sourcerecordid>A712933613</sourcerecordid><originalsourceid>FETCH-LOGICAL-c508t-e9a3f1d99a863eaf4d2492a29934e7391ed6ff14ba57ec2a8d0d7f4fa5f74bdb3</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS0EopfCC7BAltiwSfFP4sTLy-WnlYqooIhlNNceB5ckLnZSKU_Aa-PoFioQQtbII_s7R_YcQp5ydsIZq18mzpRgBVtLSd4Uyz2y4VUtC1Gp5j7ZMK5yz7k6Io9SumKM6Zqrh-RIipJpXfEN-XHq0xS6CAPdjtAvyScaHH3tnZuTD2PxBX33dUJL33-kZwN0fuwoJAr0lQ8DxG8Y6RToRUTrzUQ_zfHG30C_euS-8wb6fqGXEWH12IU-RDRTBnYwmqy9gMnjOKXH5IGDPuGT2_2YfH775nJ3Wpx_eHe2254XpmLNVKAG6bjVGholEVxpRakFCK1libXUHK1yjpd7qGo0AhrLbO1KB5Wry73dy2Py4uB7HcP3GdPUDj4Z7HsYMcypzYPJq6xlldHnf6FXYY55SJmqWJknLoW6ozrosfWjC1MEs5q225oLLaXiMlMn_6Dysjh4E0Z0Pp__IRAHgYkhpYiuvY4-z3tpOWvX9NtD-i1ba02_XbLo2e2L5_2A9rfkV9wZkAcg5auxw3j3pf_Y_gR0ZLqu</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2504631326</pqid></control><display><type>article</type><title>Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients</title><source>SpringerLink Journals - AutoHoldings</source><creator>Takahashi, Yumiko ; Hayano, Koichi ; Ohira, Gaku ; Imanishi, Shunsuke ; Hanaoka, Toshiharu ; Watanabe, Hiroki ; Hirata, Atsushi ; Kawasaki, Yohei ; Miyauchi, Hideaki ; Matsubara, Hisahiro</creator><creatorcontrib>Takahashi, Yumiko ; Hayano, Koichi ; Ohira, Gaku ; Imanishi, Shunsuke ; Hanaoka, Toshiharu ; Watanabe, Hiroki ; Hirata, Atsushi ; Kawasaki, Yohei ; Miyauchi, Hideaki ; Matsubara, Hisahiro</creatorcontrib><description>Background
Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology.
Aims
The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value.
Methods
This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at
b
= 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at
b
= 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan–Meier analysis.
Results
ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32,
p
= 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58,
p
= 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (
p
= 0.04,
p
= 0.03, respectively), and skewness was significantly correlated with OS (
p
= 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan–Meier analysis (
p
= 0.01,
p
= 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (
p
= 0.04).
Conclusions
Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer.</description><identifier>ISSN: 0163-2116</identifier><identifier>EISSN: 1573-2568</identifier><identifier>DOI: 10.1007/s10620-020-06318-y</identifier><identifier>PMID: 32409951</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Biochemistry ; Biomarkers ; Cancer ; Cancer therapies ; Colorectal cancer ; Drunk driving ; Gastroenterology ; Hepatology ; Kurtosis ; Lymphatic system ; Magnetic resonance imaging ; Medical colleges ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metastasis ; Oncology ; Oncology, Experimental ; Original Article ; Patient outcomes ; Patients ; Prognosis ; Regression analysis ; Skewness ; Statistical analysis ; Surgery ; Transplant Surgery ; Tumors</subject><ispartof>Digestive diseases and sciences, 2021-04, Vol.66 (4), p.1227-1232</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>COPYRIGHT 2021 Springer</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c508t-e9a3f1d99a863eaf4d2492a29934e7391ed6ff14ba57ec2a8d0d7f4fa5f74bdb3</citedby><cites>FETCH-LOGICAL-c508t-e9a3f1d99a863eaf4d2492a29934e7391ed6ff14ba57ec2a8d0d7f4fa5f74bdb3</cites><orcidid>0000-0003-4733-8220</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10620-020-06318-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10620-020-06318-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32409951$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Takahashi, Yumiko</creatorcontrib><creatorcontrib>Hayano, Koichi</creatorcontrib><creatorcontrib>Ohira, Gaku</creatorcontrib><creatorcontrib>Imanishi, Shunsuke</creatorcontrib><creatorcontrib>Hanaoka, Toshiharu</creatorcontrib><creatorcontrib>Watanabe, Hiroki</creatorcontrib><creatorcontrib>Hirata, Atsushi</creatorcontrib><creatorcontrib>Kawasaki, Yohei</creatorcontrib><creatorcontrib>Miyauchi, Hideaki</creatorcontrib><creatorcontrib>Matsubara, Hisahiro</creatorcontrib><title>Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients</title><title>Digestive diseases and sciences</title><addtitle>Dig Dis Sci</addtitle><addtitle>Dig Dis Sci</addtitle><description>Background
Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology.
Aims
The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value.
Methods
This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at
b
= 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at
b
= 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan–Meier analysis.
Results
ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32,
p
= 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58,
p
= 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (
p
= 0.04,
p
= 0.03, respectively), and skewness was significantly correlated with OS (
p
= 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan–Meier analysis (
p
= 0.01,
p
= 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (
p
= 0.04).
Conclusions
Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer.</description><subject>Biochemistry</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Colorectal cancer</subject><subject>Drunk driving</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Kurtosis</subject><subject>Lymphatic system</subject><subject>Magnetic resonance imaging</subject><subject>Medical colleges</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastasis</subject><subject>Oncology</subject><subject>Oncology, Experimental</subject><subject>Original Article</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Skewness</subject><subject>Statistical analysis</subject><subject>Surgery</subject><subject>Transplant Surgery</subject><subject>Tumors</subject><issn>0163-2116</issn><issn>1573-2568</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kc1u1TAQhS0EopfCC7BAltiwSfFP4sTLy-WnlYqooIhlNNceB5ckLnZSKU_Aa-PoFioQQtbII_s7R_YcQp5ydsIZq18mzpRgBVtLSd4Uyz2y4VUtC1Gp5j7ZMK5yz7k6Io9SumKM6Zqrh-RIipJpXfEN-XHq0xS6CAPdjtAvyScaHH3tnZuTD2PxBX33dUJL33-kZwN0fuwoJAr0lQ8DxG8Y6RToRUTrzUQ_zfHG30C_euS-8wb6fqGXEWH12IU-RDRTBnYwmqy9gMnjOKXH5IGDPuGT2_2YfH775nJ3Wpx_eHe2254XpmLNVKAG6bjVGholEVxpRakFCK1libXUHK1yjpd7qGo0AhrLbO1KB5Wry73dy2Py4uB7HcP3GdPUDj4Z7HsYMcypzYPJq6xlldHnf6FXYY55SJmqWJknLoW6ozrosfWjC1MEs5q225oLLaXiMlMn_6Dysjh4E0Z0Pp__IRAHgYkhpYiuvY4-z3tpOWvX9NtD-i1ba02_XbLo2e2L5_2A9rfkV9wZkAcg5auxw3j3pf_Y_gR0ZLqu</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Takahashi, Yumiko</creator><creator>Hayano, Koichi</creator><creator>Ohira, Gaku</creator><creator>Imanishi, Shunsuke</creator><creator>Hanaoka, Toshiharu</creator><creator>Watanabe, Hiroki</creator><creator>Hirata, Atsushi</creator><creator>Kawasaki, Yohei</creator><creator>Miyauchi, Hideaki</creator><creator>Matsubara, Hisahiro</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4733-8220</orcidid></search><sort><creationdate>20210401</creationdate><title>Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients</title><author>Takahashi, Yumiko ; Hayano, Koichi ; Ohira, Gaku ; Imanishi, Shunsuke ; Hanaoka, Toshiharu ; Watanabe, Hiroki ; Hirata, Atsushi ; Kawasaki, Yohei ; Miyauchi, Hideaki ; Matsubara, Hisahiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c508t-e9a3f1d99a863eaf4d2492a29934e7391ed6ff14ba57ec2a8d0d7f4fa5f74bdb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biochemistry</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Colorectal cancer</topic><topic>Drunk driving</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Kurtosis</topic><topic>Lymphatic system</topic><topic>Magnetic resonance imaging</topic><topic>Medical colleges</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastasis</topic><topic>Oncology</topic><topic>Oncology, Experimental</topic><topic>Original Article</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Skewness</topic><topic>Statistical analysis</topic><topic>Surgery</topic><topic>Transplant Surgery</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Takahashi, Yumiko</creatorcontrib><creatorcontrib>Hayano, Koichi</creatorcontrib><creatorcontrib>Ohira, Gaku</creatorcontrib><creatorcontrib>Imanishi, Shunsuke</creatorcontrib><creatorcontrib>Hanaoka, Toshiharu</creatorcontrib><creatorcontrib>Watanabe, Hiroki</creatorcontrib><creatorcontrib>Hirata, Atsushi</creatorcontrib><creatorcontrib>Kawasaki, Yohei</creatorcontrib><creatorcontrib>Miyauchi, Hideaki</creatorcontrib><creatorcontrib>Matsubara, Hisahiro</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Digestive diseases and sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Takahashi, Yumiko</au><au>Hayano, Koichi</au><au>Ohira, Gaku</au><au>Imanishi, Shunsuke</au><au>Hanaoka, Toshiharu</au><au>Watanabe, Hiroki</au><au>Hirata, Atsushi</au><au>Kawasaki, Yohei</au><au>Miyauchi, Hideaki</au><au>Matsubara, Hisahiro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients</atitle><jtitle>Digestive diseases and sciences</jtitle><stitle>Dig Dis Sci</stitle><addtitle>Dig Dis Sci</addtitle><date>2021-04-01</date><risdate>2021</risdate><volume>66</volume><issue>4</issue><spage>1227</spage><epage>1232</epage><pages>1227-1232</pages><issn>0163-2116</issn><eissn>1573-2568</eissn><abstract>Background
Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology.
Aims
The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value.
Methods
This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at
b
= 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at
b
= 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan–Meier analysis.
Results
ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32,
p
= 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58,
p
= 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (
p
= 0.04,
p
= 0.03, respectively), and skewness was significantly correlated with OS (
p
= 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan–Meier analysis (
p
= 0.01,
p
= 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (
p
= 0.04).
Conclusions
Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>32409951</pmid><doi>10.1007/s10620-020-06318-y</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0003-4733-8220</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0163-2116 |
| ispartof | Digestive diseases and sciences, 2021-04, Vol.66 (4), p.1227-1232 |
| issn | 0163-2116 1573-2568 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2404044735 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Biochemistry Biomarkers Cancer Cancer therapies Colorectal cancer Drunk driving Gastroenterology Hepatology Kurtosis Lymphatic system Magnetic resonance imaging Medical colleges Medical prognosis Medicine Medicine & Public Health Metastasis Oncology Oncology, Experimental Original Article Patient outcomes Patients Prognosis Regression analysis Skewness Statistical analysis Surgery Transplant Surgery Tumors |
| title | Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients |
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