Loading…
Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer
Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognosti...
Saved in:
Published in: | Journal of Cancer 2021-01, Vol.12 (15), p.4561-4573 |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c380t-4edfdc23712874757aec4b3cd1191dcb2f07f4cb66819aef28f6b8339a0cc2553 |
---|---|
cites | |
container_end_page | 4573 |
container_issue | 15 |
container_start_page | 4561 |
container_title | Journal of Cancer |
container_volume | 12 |
creator | Li, Tengfei Yu, Zekuan Yang, Yan Fu, Zhongmao Chen, Ziang Li, Qi Zhang, Kundong Luo, Zai Qiu, Zhengjun Huang, Chen |
description | Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis. Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software. Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual. Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making. |
doi_str_mv | 10.7150/jca.58887 |
format | article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8210572</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2543709326</sourcerecordid><originalsourceid>FETCH-LOGICAL-c380t-4edfdc23712874757aec4b3cd1191dcb2f07f4cb66819aef28f6b8339a0cc2553</originalsourceid><addsrcrecordid>eNpdkU1r3DAQhkVJaUKaQ_-BoJfm4FRftqRLoYQ2CQQKpT2L8Vja1SJbW8kO7L-vNwmh6Vw0oIeHl3kJ-cDZleYt-7xDuGqNMfoNOeNG6sZ2nTr5Zz8lF7Xu2DrSCq3kO3IqFVfWCnZG8Cfs40DHJc2xGQ4TjBEppE0ucd6ONORCNwUONI6w8RQmSIcaK82BzltP61zyCHTvC_ppPhJ5ophTLh5nSBRhQl_ek7cBUvUXz-85-f3926_r2-b-x83d9df7BqVhc6P8EAYUUnNhtNKtBo-qlzhwbvmAvQhMB4V91xluwQdhQtcbKS0wRNG28px8efLul370wzFSgeT2ZQ1fDi5DdK9_prh1m_zgjOCs1WIVfHoWlPxn8XV2Y6zoU4LJ56U60SqpmZWiW9GP_6G7vJT1PEfKGsk0fxRePlFYcq3Fh5cwnLlje25tzz22J_8C72mNAA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2598307172</pqid></control><display><type>article</type><title>Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><creator>Li, Tengfei ; Yu, Zekuan ; Yang, Yan ; Fu, Zhongmao ; Chen, Ziang ; Li, Qi ; Zhang, Kundong ; Luo, Zai ; Qiu, Zhengjun ; Huang, Chen</creator><creatorcontrib>Li, Tengfei ; Yu, Zekuan ; Yang, Yan ; Fu, Zhongmao ; Chen, Ziang ; Li, Qi ; Zhang, Kundong ; Luo, Zai ; Qiu, Zhengjun ; Huang, Chen</creatorcontrib><description>Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis. Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software. Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual. Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.</description><identifier>ISSN: 1837-9664</identifier><identifier>EISSN: 1837-9664</identifier><identifier>DOI: 10.7150/jca.58887</identifier><identifier>PMID: 34149920</identifier><language>eng</language><publisher>Wyoming: Ivyspring International Publisher Pty Ltd</publisher><subject>Algorithms ; Annotations ; Chemotherapy ; Classification ; Colorectal cancer ; Committees ; Deep learning ; Medical prognosis ; Mortality ; Open source software ; Pathology ; Public domain ; Research Paper ; Surgery ; Tumors</subject><ispartof>Journal of Cancer, 2021-01, Vol.12 (15), p.4561-4573</ispartof><rights>2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The author(s) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-4edfdc23712874757aec4b3cd1191dcb2f07f4cb66819aef28f6b8339a0cc2553</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2598307172/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2598307172?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25732,27903,27904,36991,36992,44569,53770,53772,74873</link.rule.ids></links><search><creatorcontrib>Li, Tengfei</creatorcontrib><creatorcontrib>Yu, Zekuan</creatorcontrib><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Fu, Zhongmao</creatorcontrib><creatorcontrib>Chen, Ziang</creatorcontrib><creatorcontrib>Li, Qi</creatorcontrib><creatorcontrib>Zhang, Kundong</creatorcontrib><creatorcontrib>Luo, Zai</creatorcontrib><creatorcontrib>Qiu, Zhengjun</creatorcontrib><creatorcontrib>Huang, Chen</creatorcontrib><title>Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer</title><title>Journal of Cancer</title><description>Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis. Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software. Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual. Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.</description><subject>Algorithms</subject><subject>Annotations</subject><subject>Chemotherapy</subject><subject>Classification</subject><subject>Colorectal cancer</subject><subject>Committees</subject><subject>Deep learning</subject><subject>Medical prognosis</subject><subject>Mortality</subject><subject>Open source software</subject><subject>Pathology</subject><subject>Public domain</subject><subject>Research Paper</subject><subject>Surgery</subject><subject>Tumors</subject><issn>1837-9664</issn><issn>1837-9664</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpdkU1r3DAQhkVJaUKaQ_-BoJfm4FRftqRLoYQ2CQQKpT2L8Vja1SJbW8kO7L-vNwmh6Vw0oIeHl3kJ-cDZleYt-7xDuGqNMfoNOeNG6sZ2nTr5Zz8lF7Xu2DrSCq3kO3IqFVfWCnZG8Cfs40DHJc2xGQ4TjBEppE0ucd6ONORCNwUONI6w8RQmSIcaK82BzltP61zyCHTvC_ppPhJ5ophTLh5nSBRhQl_ek7cBUvUXz-85-f3926_r2-b-x83d9df7BqVhc6P8EAYUUnNhtNKtBo-qlzhwbvmAvQhMB4V91xluwQdhQtcbKS0wRNG28px8efLul370wzFSgeT2ZQ1fDi5DdK9_prh1m_zgjOCs1WIVfHoWlPxn8XV2Y6zoU4LJ56U60SqpmZWiW9GP_6G7vJT1PEfKGsk0fxRePlFYcq3Fh5cwnLlje25tzz22J_8C72mNAA</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Li, Tengfei</creator><creator>Yu, Zekuan</creator><creator>Yang, Yan</creator><creator>Fu, Zhongmao</creator><creator>Chen, Ziang</creator><creator>Li, Qi</creator><creator>Zhang, Kundong</creator><creator>Luo, Zai</creator><creator>Qiu, Zhengjun</creator><creator>Huang, Chen</creator><general>Ivyspring International Publisher Pty Ltd</general><general>Ivyspring International Publisher</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</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>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20210101</creationdate><title>Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer</title><author>Li, Tengfei ; Yu, Zekuan ; Yang, Yan ; Fu, Zhongmao ; Chen, Ziang ; Li, Qi ; Zhang, Kundong ; Luo, Zai ; Qiu, Zhengjun ; Huang, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-4edfdc23712874757aec4b3cd1191dcb2f07f4cb66819aef28f6b8339a0cc2553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Annotations</topic><topic>Chemotherapy</topic><topic>Classification</topic><topic>Colorectal cancer</topic><topic>Committees</topic><topic>Deep learning</topic><topic>Medical prognosis</topic><topic>Mortality</topic><topic>Open source software</topic><topic>Pathology</topic><topic>Public domain</topic><topic>Research Paper</topic><topic>Surgery</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Tengfei</creatorcontrib><creatorcontrib>Yu, Zekuan</creatorcontrib><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Fu, Zhongmao</creatorcontrib><creatorcontrib>Chen, Ziang</creatorcontrib><creatorcontrib>Li, Qi</creatorcontrib><creatorcontrib>Zhang, Kundong</creatorcontrib><creatorcontrib>Luo, Zai</creatorcontrib><creatorcontrib>Qiu, Zhengjun</creatorcontrib><creatorcontrib>Huang, Chen</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content (ProQuest)</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of Cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Tengfei</au><au>Yu, Zekuan</au><au>Yang, Yan</au><au>Fu, Zhongmao</au><au>Chen, Ziang</au><au>Li, Qi</au><au>Zhang, Kundong</au><au>Luo, Zai</au><au>Qiu, Zhengjun</au><au>Huang, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer</atitle><jtitle>Journal of Cancer</jtitle><date>2021-01-01</date><risdate>2021</risdate><volume>12</volume><issue>15</issue><spage>4561</spage><epage>4573</epage><pages>4561-4573</pages><issn>1837-9664</issn><eissn>1837-9664</eissn><abstract>Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis. Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software. Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual. Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.</abstract><cop>Wyoming</cop><pub>Ivyspring International Publisher Pty Ltd</pub><pmid>34149920</pmid><doi>10.7150/jca.58887</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1837-9664 |
ispartof | Journal of Cancer, 2021-01, Vol.12 (15), p.4561-4573 |
issn | 1837-9664 1837-9664 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8210572 |
source | Publicly Available Content (ProQuest); PubMed Central |
subjects | Algorithms Annotations Chemotherapy Classification Colorectal cancer Committees Deep learning Medical prognosis Mortality Open source software Pathology Public domain Research Paper Surgery Tumors |
title | Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T20%3A05%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Rapid%20multi-dynamic%20algorithm%20for%20gray%20image%20analysis%20of%20the%20stroma%20percentage%20on%20colorectal%20cancer&rft.jtitle=Journal%20of%20Cancer&rft.au=Li,%20Tengfei&rft.date=2021-01-01&rft.volume=12&rft.issue=15&rft.spage=4561&rft.epage=4573&rft.pages=4561-4573&rft.issn=1837-9664&rft.eissn=1837-9664&rft_id=info:doi/10.7150/jca.58887&rft_dat=%3Cproquest_pubme%3E2543709326%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c380t-4edfdc23712874757aec4b3cd1191dcb2f07f4cb66819aef28f6b8339a0cc2553%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2598307172&rft_id=info:pmid/34149920&rfr_iscdi=true |