Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study
Background Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk can...
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| Veröffentlicht in: | Journal of translational medicine 2020-03, Vol.18 (1), p.129-129, Article 129 |
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| creator | Ji, Meng-Yao Yuan, Lei Lu, Shi-Min Gao, Meng-Ting Zeng, Zhi Zhan, Na Ding, Yi-Juan Liu, Zheng-Ru Huang, Ping-Xiao Lu, Cheng Dong, Wei-Guo |
| description | Background Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. Methods 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. Results The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. Conclusion Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients. |
| doi_str_mv | 10.1186/s12967-020-02297-w |
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| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2379063795</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A618737970</galeid><doaj_id>oai_doaj_org_article_43ac82cadd724a669f55924df372d21d</doaj_id><sourcerecordid>A618737970</sourcerecordid><originalsourceid>FETCH-LOGICAL-c563t-64a4f8f87d1040937232c4be73358a79a8c3b23ca06941e42ee248c9697facc73</originalsourceid><addsrcrecordid>eNqNks1u1DAUhSMEomXgBVggS2yQUIr_EscsKlUjKJUqsYG1dce-mXGVxIOddDQvwvPiacrQIhbI8o9uvnOsG5-ieM3oGWNN_SExrmtVUk7z5FqVuyfFKZNKl1Wj6qcPzifFi5RuKOWykvp5cSI4U02t6Wnx87KDwU0dRBKix2GE0YeB5BpJG9gicThi7P2Ajqz2xIZ-O80MdGQL4yZ0YX2oT50j3mUD3-4JrNcRU_K3SMapD5G0eSLE7kB22d9CtH4IPXwkQMbotx2WNosxkjRObv-yeNZCl_DV_b4ovn_-9G35pbz-enm1vLgubVWLsawlyLZpG-UYlVQLxQW3coVKiKoBpaGxYsWFBVpryVByRC4bq2utWrBWiUVxNfu6ADdmG30PcW8CeHNXCHFtII7edmikANtwC84pLqGudVtVmkvX5lsdZy57nc9e22nVozu0E6F7ZPr4y-A3Zh1ujaJKUdpkg3f3BjH8mDCNpvfJYpdfCMOUDBdKaS2r3N2iePsXehOmmN_kjtK0zkv1h1pDbsAPbcj32oOpuahZozKkaKbO_kHl4bD3NgzY-lx_JOCzwMaQUsT22COj5pBMMyfT5GSau2SaXRa9efh3jpLfUczA-xnY4Sq0yeYwWjxilNKKM8oEy6e8LYrm_-mlnyO7DNMwil_cJgIA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2379063795</pqid></control><display><type>article</type><title>Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>SpringerNature Journals</source><source>PubMed Central Open Access</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Springer Nature OA/Free Journals</source><creator>Ji, Meng-Yao ; Yuan, Lei ; Lu, Shi-Min ; Gao, Meng-Ting ; Zeng, Zhi ; Zhan, Na ; Ding, Yi-Juan ; Liu, Zheng-Ru ; Huang, Ping-Xiao ; Lu, Cheng ; Dong, Wei-Guo</creator><creatorcontrib>Ji, Meng-Yao ; Yuan, Lei ; Lu, Shi-Min ; Gao, Meng-Ting ; Zeng, Zhi ; Zhan, Na ; Ding, Yi-Juan ; Liu, Zheng-Ru ; Huang, Ping-Xiao ; Lu, Cheng ; Dong, Wei-Guo</creatorcontrib><description>Background Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. Methods 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. Results The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. Conclusion Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.</description><identifier>ISSN: 1479-5876</identifier><identifier>EISSN: 1479-5876</identifier><identifier>DOI: 10.1186/s12967-020-02297-w</identifier><identifier>PMID: 32178690</identifier><language>eng</language><publisher>LONDON: Springer Nature</publisher><subject>Adenocarcinoma ; Biological markers ; Biomarkers, Tumor ; Cancer genetics ; Cancer research ; Carcinoma ; Chemotherapy ; Clinical pathology ; Colon ; Colon adenocarcinoma ; Colon cancer ; Colorectal cancer ; Computer applications ; Data centers ; Diagnosis ; Diseases ; Genomes ; Genomics ; Gland heterogeneity ; Glands ; Histochemistry ; Humans ; Immunohistochemistry ; Life Sciences & Biomedicine ; Machine learning ; Medical examination ; Medical prognosis ; Medical research ; Medicine, Research & Experimental ; Morphology ; Neoplasm Recurrence, Local ; Neoplasm Staging ; Oncology ; Pathology ; Precision Medicine ; Prognosis ; Quantitative histopathology images ; Research & Experimental Medicine ; Retrospective Studies ; Science & Technology ; Surgery ; Survival analysis ; Technology application ; Tumors</subject><ispartof>Journal of translational medicine, 2020-03, Vol.18 (1), p.129-129, Article 129</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://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) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>3</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000521013100001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c563t-64a4f8f87d1040937232c4be73358a79a8c3b23ca06941e42ee248c9697facc73</citedby><cites>FETCH-LOGICAL-c563t-64a4f8f87d1040937232c4be73358a79a8c3b23ca06941e42ee248c9697facc73</cites><orcidid>0000-0002-7651-3924</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077008/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077008/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2115,27929,27930,28253,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32178690$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ji, Meng-Yao</creatorcontrib><creatorcontrib>Yuan, Lei</creatorcontrib><creatorcontrib>Lu, Shi-Min</creatorcontrib><creatorcontrib>Gao, Meng-Ting</creatorcontrib><creatorcontrib>Zeng, Zhi</creatorcontrib><creatorcontrib>Zhan, Na</creatorcontrib><creatorcontrib>Ding, Yi-Juan</creatorcontrib><creatorcontrib>Liu, Zheng-Ru</creatorcontrib><creatorcontrib>Huang, Ping-Xiao</creatorcontrib><creatorcontrib>Lu, Cheng</creatorcontrib><creatorcontrib>Dong, Wei-Guo</creatorcontrib><title>Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study</title><title>Journal of translational medicine</title><addtitle>J TRANSL MED</addtitle><addtitle>J Transl Med</addtitle><description>Background Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. Methods 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. Results The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. Conclusion Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.</description><subject>Adenocarcinoma</subject><subject>Biological markers</subject><subject>Biomarkers, Tumor</subject><subject>Cancer genetics</subject><subject>Cancer research</subject><subject>Carcinoma</subject><subject>Chemotherapy</subject><subject>Clinical pathology</subject><subject>Colon</subject><subject>Colon adenocarcinoma</subject><subject>Colon cancer</subject><subject>Colorectal cancer</subject><subject>Computer applications</subject><subject>Data centers</subject><subject>Diagnosis</subject><subject>Diseases</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Gland heterogeneity</subject><subject>Glands</subject><subject>Histochemistry</subject><subject>Humans</subject><subject>Immunohistochemistry</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine learning</subject><subject>Medical examination</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Medicine, Research & Experimental</subject><subject>Morphology</subject><subject>Neoplasm Recurrence, Local</subject><subject>Neoplasm Staging</subject><subject>Oncology</subject><subject>Pathology</subject><subject>Precision Medicine</subject><subject>Prognosis</subject><subject>Quantitative histopathology images</subject><subject>Research & Experimental Medicine</subject><subject>Retrospective Studies</subject><subject>Science & Technology</subject><subject>Surgery</subject><subject>Survival analysis</subject><subject>Technology 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early colon carcinoma: a triple-center study</title><author>Ji, Meng-Yao ; Yuan, Lei ; Lu, Shi-Min ; Gao, Meng-Ting ; Zeng, Zhi ; Zhan, Na ; Ding, Yi-Juan ; Liu, Zheng-Ru ; Huang, Ping-Xiao ; Lu, Cheng ; Dong, Wei-Guo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c563t-64a4f8f87d1040937232c4be73358a79a8c3b23ca06941e42ee248c9697facc73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adenocarcinoma</topic><topic>Biological markers</topic><topic>Biomarkers, Tumor</topic><topic>Cancer genetics</topic><topic>Cancer research</topic><topic>Carcinoma</topic><topic>Chemotherapy</topic><topic>Clinical pathology</topic><topic>Colon</topic><topic>Colon adenocarcinoma</topic><topic>Colon cancer</topic><topic>Colorectal cancer</topic><topic>Computer applications</topic><topic>Data centers</topic><topic>Diagnosis</topic><topic>Diseases</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Gland heterogeneity</topic><topic>Glands</topic><topic>Histochemistry</topic><topic>Humans</topic><topic>Immunohistochemistry</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine learning</topic><topic>Medical examination</topic><topic>Medical prognosis</topic><topic>Medical research</topic><topic>Medicine, Research & Experimental</topic><topic>Morphology</topic><topic>Neoplasm Recurrence, Local</topic><topic>Neoplasm Staging</topic><topic>Oncology</topic><topic>Pathology</topic><topic>Precision Medicine</topic><topic>Prognosis</topic><topic>Quantitative histopathology images</topic><topic>Research & Experimental Medicine</topic><topic>Retrospective Studies</topic><topic>Science & Technology</topic><topic>Surgery</topic><topic>Survival analysis</topic><topic>Technology application</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ji, Meng-Yao</creatorcontrib><creatorcontrib>Yuan, Lei</creatorcontrib><creatorcontrib>Lu, Shi-Min</creatorcontrib><creatorcontrib>Gao, Meng-Ting</creatorcontrib><creatorcontrib>Zeng, Zhi</creatorcontrib><creatorcontrib>Zhan, Na</creatorcontrib><creatorcontrib>Ding, Yi-Juan</creatorcontrib><creatorcontrib>Liu, Zheng-Ru</creatorcontrib><creatorcontrib>Huang, Ping-Xiao</creatorcontrib><creatorcontrib>Lu, Cheng</creatorcontrib><creatorcontrib>Dong, Wei-Guo</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE 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Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</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><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of translational medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ji, Meng-Yao</au><au>Yuan, Lei</au><au>Lu, Shi-Min</au><au>Gao, Meng-Ting</au><au>Zeng, Zhi</au><au>Zhan, Na</au><au>Ding, Yi-Juan</au><au>Liu, Zheng-Ru</au><au>Huang, Ping-Xiao</au><au>Lu, Cheng</au><au>Dong, Wei-Guo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study</atitle><jtitle>Journal of translational medicine</jtitle><stitle>J TRANSL MED</stitle><addtitle>J Transl Med</addtitle><date>2020-03-16</date><risdate>2020</risdate><volume>18</volume><issue>1</issue><spage>129</spage><epage>129</epage><pages>129-129</pages><artnum>129</artnum><issn>1479-5876</issn><eissn>1479-5876</eissn><abstract>Background Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. Methods 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. Results The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. Conclusion Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.</abstract><cop>LONDON</cop><pub>Springer Nature</pub><pmid>32178690</pmid><doi>10.1186/s12967-020-02297-w</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7651-3924</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adenocarcinoma Biological markers Biomarkers, Tumor Cancer genetics Cancer research Carcinoma Chemotherapy Clinical pathology Colon Colon adenocarcinoma Colon cancer Colorectal cancer Computer applications Data centers Diagnosis Diseases Genomes Genomics Gland heterogeneity Glands Histochemistry Humans Immunohistochemistry Life Sciences & Biomedicine Machine learning Medical examination Medical prognosis Medical research Medicine, Research & Experimental Morphology Neoplasm Recurrence, Local Neoplasm Staging Oncology Pathology Precision Medicine Prognosis Quantitative histopathology images Research & Experimental Medicine Retrospective Studies Science & Technology Surgery Survival analysis Technology application Tumors |
| title | Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study |
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