Real-time colorectal cancer diagnosis using PR-OCT with deep learning
Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limit...
Gespeichert in:
| Veröffentlicht in: | Theranostics Jg. 10; H. 6; S. 2587 - 2596 |
|---|---|
| Hauptverfasser: | , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Australia
Ivyspring International Publisher Pty Ltd
01.01.2020
Ivyspring International Publisher |
| Schlagworte: | |
| ISSN: | 1838-7640, 1838-7640 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time.
: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas.
: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved.
: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned. |
|---|---|
| AbstractList | Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time.
: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas.
: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved.
: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned. Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) “optical biopsies” of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. Results: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. Conclusions: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned. Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. Results: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. Conclusions: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned.Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. Results: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. Conclusions: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned. |
| Author | Zhu, Quing Xu, Shiqi Abdelal, Heba Mutch, Matthew Chapman, William C. Zeng, Yifeng Alipour, Zahra Li, Shuying Chatterjee, Deyali |
| AuthorAffiliation | 3 Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine 5 Department of Radiology, Washington University School of Medicine 2 Department of Electrical & System Engineering, Washington University in St. Louis 4 Department of Pathology and Immunology, Washington University School of Medicine 1 Department of Biomedical Engineering, Washington University in St. Louis |
| AuthorAffiliation_xml | – name: 3 Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine – name: 4 Department of Pathology and Immunology, Washington University School of Medicine – name: 1 Department of Biomedical Engineering, Washington University in St. Louis – name: 2 Department of Electrical & System Engineering, Washington University in St. Louis – name: 5 Department of Radiology, Washington University School of Medicine |
| Author_xml | – sequence: 1 givenname: Yifeng surname: Zeng fullname: Zeng, Yifeng – sequence: 2 givenname: Shiqi surname: Xu fullname: Xu, Shiqi – sequence: 3 givenname: William C. surname: Chapman fullname: Chapman, William C. – sequence: 4 givenname: Shuying surname: Li fullname: Li, Shuying – sequence: 5 givenname: Zahra surname: Alipour fullname: Alipour, Zahra – sequence: 6 givenname: Heba surname: Abdelal fullname: Abdelal, Heba – sequence: 7 givenname: Deyali surname: Chatterjee fullname: Chatterjee, Deyali – sequence: 8 givenname: Matthew surname: Mutch fullname: Mutch, Matthew – sequence: 9 givenname: Quing surname: Zhu fullname: Zhu, Quing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32194821$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkVtrGzEQhUVJaC71S39AWOhLKWyi61p6KRTjpoFAQvC70GolW0aWHEmb0n9fuXZKEqKXEcw3hzNzzsBRiMEA8BnByyli8KqsQrykEArxAZwiTng77Sg8evE_AZOc17A-CrFA4iM4IRgJyjE6BfMHo3xb3MY0OvqYjC7KN1oFbVIzOLUMMbvcjNmFZXP_0N7NFs1vV1bNYMy28UalUDufwLFVPpvJoZ6Dxc_5Yvarvb27vpn9uG01hV1pGcI97q02AnKsNenp0FlLNdUMUaxtN9Wit4RbTgZlyYChIpZ0qoNCscqfg-972e3Yb8ygTShJeblNbqPSHxmVk687wa3kMj7JKWSYC14Fvh4EUnwcTS5y47I23qtg4pglJhx1GGOGK_rlDbqOYwp1O4mZ4JgxCEmlLl46-m_l-cAVgHtAp5hzMlZqV1RxcWfQeYmg3MUodzHKfzHWkW9vRp5V34H_AoXDnl0 |
| CitedBy_id | crossref_primary_10_1111_vco_12632 crossref_primary_10_1109_ACCESS_2020_3009412 crossref_primary_10_1155_2021_9025470 crossref_primary_10_1002_jbio_202000472 crossref_primary_10_1007_s00432_022_04263_z crossref_primary_10_1007_s11548_021_02457_3 crossref_primary_10_1038_s41598_024_55338_5 crossref_primary_10_3390_diagnostics14212459 crossref_primary_10_1002_jbio_202000276 crossref_primary_10_4018_IJSKD_326629 crossref_primary_10_1002_ijc_34053 crossref_primary_10_3389_fmed_2023_1128084 crossref_primary_10_1002_jbio_202400082 crossref_primary_10_1364_BOE_555185 crossref_primary_10_1038_s41598_024_78891_5 crossref_primary_10_3389_fonc_2023_1121838 crossref_primary_10_1007_s00414_025_03447_9 crossref_primary_10_1016_j_bspc_2025_108202 crossref_primary_10_3390_electronics13122298 crossref_primary_10_3748_wjg_v31_i36_110742 crossref_primary_10_1146_annurev_anchem_061622_014208 crossref_primary_10_3390_ijms25105337 crossref_primary_10_1002_jbio_202100349 crossref_primary_10_3390_diagnostics11091523 crossref_primary_10_3389_fonc_2022_925924 crossref_primary_10_1016_j_compbiomed_2020_104003 crossref_primary_10_1111_vco_12766 crossref_primary_10_1016_j_semcancer_2023_06_003 crossref_primary_10_3390_app11073119 crossref_primary_10_1007_s00432_023_04742_x |
| ContentType | Journal Article |
| Copyright | The author(s). 2020. 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. The author(s) 2020 |
| Copyright_xml | – notice: The author(s). – notice: 2020. 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. – notice: The author(s) 2020 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM |
| DOI | 10.7150/thno.40099 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest One Health & Nursing ProQuest Central China ProQuest Hospital Collection (Alumni) ProQuest Central ProQuest Health & Medical Complete ProQuest Health & Medical Research Collection Health Research Premium Collection ProQuest One Academic UKI Edition Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: PIMPY name: ProQuest - Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1838-7640 |
| EndPage | 2596 |
| ExternalDocumentID | PMC7052898 32194821 10_7150_thno_40099 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NCI NIH HHS grantid: R01 CA228047 – fundername: NCI NIH HHS grantid: T32 CA009621 – fundername: NCI NIH HHS grantid: R01 CA151570 – fundername: NIBIB NIH HHS grantid: R01 EB002136 |
| GroupedDBID | --- 53G 5VS 7X7 8FI 8FJ AAYXX ABUWG ADBBV ADRAZ AENEX AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV BENPR CCPQU CITATION DIK FYUFA GROUPED_DOAJ HMCUK HYE KQ8 M48 M~E O5R O5S OK1 PGMZT PHGZM PHGZT PIMPY RPM UKHRP ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c406t-512b2bfce9082cc3b4d6ff4c4c5142cf67c9bf38f83daf3d20a3f36a609a5cc3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 44 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000514450900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1838-7640 |
| IngestDate | Tue Nov 04 02:03:57 EST 2025 Thu Oct 02 06:38:17 EDT 2025 Tue Oct 07 07:27:26 EDT 2025 Mon Jul 21 05:50:50 EDT 2025 Sat Nov 29 06:11:34 EST 2025 Tue Nov 18 21:17:06 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | deep learning colorectal cancer optical biopsy optical coherence tomography (OCT) |
| Language | English |
| License | The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c406t-512b2bfce9082cc3b4d6ff4c4c5142cf67c9bf38f83daf3d20a3f36a609a5cc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interest exists. These authors contributed equally to this paper. |
| OpenAccessLink | https://www.proquest.com/docview/2598255003?pq-origsite=%requestingapplication% |
| PMID | 32194821 |
| PQID | 2598255003 |
| PQPubID | 5263173 |
| PageCount | 10 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7052898 proquest_miscellaneous_2381622252 proquest_journals_2598255003 pubmed_primary_32194821 crossref_citationtrail_10_7150_thno_40099 crossref_primary_10_7150_thno_40099 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-01-01 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: 2020-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Australia |
| PublicationPlace_xml | – name: Australia – name: Wyoming – name: Sydney |
| PublicationTitle | Theranostics |
| PublicationTitleAlternate | Theranostics |
| PublicationYear | 2020 |
| Publisher | Ivyspring International Publisher Pty Ltd Ivyspring International Publisher |
| Publisher_xml | – name: Ivyspring International Publisher Pty Ltd – name: Ivyspring International Publisher |
| SSID | ssj0000402919 |
| Score | 2.4422488 |
| Snippet | Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 2587 |
| SubjectTerms | Aged Aged, 80 and over Biopsy Classification Colon - diagnostic imaging Colorectal cancer Colorectal Neoplasms - diagnostic imaging Deep Learning Diagnosis, Computer-Assisted - methods Endoscopy Humans Image Processing, Computer-Assisted - methods Middle Aged Neural networks Noise Pattern recognition Research Paper Surveillance Tomography, Optical Coherence - methods Tumors |
| Title | Real-time colorectal cancer diagnosis using PR-OCT with deep learning |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/32194821 https://www.proquest.com/docview/2598255003 https://www.proquest.com/docview/2381622252 https://pubmed.ncbi.nlm.nih.gov/PMC7052898 |
| Volume | 10 |
| WOSCitedRecordID | wos000514450900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1838-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402919 issn: 1838-7640 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1838-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402919 issn: 1838-7640 databaseCode: 7X7 dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest - Publicly Available Content Database customDbUrl: eissn: 1838-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402919 issn: 1838-7640 databaseCode: PIMPY dateStart: 20190101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1838-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402919 issn: 1838-7640 databaseCode: BENPR dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB61Sw_lAH0BoYBctZceXILjjZNTBWhRe2AbrfawPUV-xLASyi774MhvZybJpiyteuESyfJEiTzj8cx49H0AX7TQBrdQlzsjHZdaKm6MTbkWRmG0H6NfDiuyCdXvJ6NRmjUFt3nTVrnyiZWjdhNLNfJjQUhzGE6H0ffpLSfWKLpdbSg0XsIG0WaTnauRamssaKAiPUlrVFKFoc_x4rqcfJMUFq2fQ38Fl097JB8dOhfbz_3dN7DVhJvstLaPt_CiKN_B5iMQwvfQG2CsyIljnhGCNXlAfMOSNcyYqzvxxnNGDfJXLBvwX-dDRtVb5opiyhrWiasPMLzoDc9_8IZcgVs8wxccD3ojjLcFcZ5bG6GqYu-llRZDKGF9rGxqfJT4JHLaR06EOvJRrOMw1V2U34FOOSmLPWCJcEapVFshid7PpomLZaG9EXHXY0IWwNfVSue2AR4n_oubHBMQ0kpOWskrrQTwuZWd1nAb_5Q6WC163my5ef5nxQP41E7jZqEbEF0WkyXK0DUpZbgigN1av-1nIvTdMhEnAag1zbcCBMS9PlOOrytAbhV2MW9N9v__Wx_htaBkvarfHEBnMVsWh_DK3i3G89lRZbnVMzmCjbNePxvg6PK-h6Ps52X2-wG5kAEg |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceD8CBYyAQw-mqeONkwNCqLRq1bJU1R72FvnZrtRml90UxI_iPzKTF11A3Hrg7EniZD7Py843AK-10AaX0IA7Ix2XWipujM25FkZhtJ-iXY7rZhNqOMzG4_xwBX50_8LQscrOJtaG2k0t1cg3BDHNYTgdJ-9nXzh1jaLd1a6FRgOLff_9G6Zsi3d7H1G_b4TY2R5t7fK2qwC36Lwqjh7OCBOsp2bf1iY4xzQEaaXF2EHYkCqbm5BkIUucDokTsU5Ckuo0zvUA5fG2V-AqmnFFuZ4aq76kg-tB5Jt5Q4KqMNLaqE7K6VtJUdiy2_sjlv39SOYFH7dz-z_7OnfgVhtMsw8N-u_Cii_vwc0LFIv3YfsII2FeTc48I35usu94hSWsz5lrzhlOFoyO_x-zwyP-eWvEqDbNnPcz1vbUOH4Ao8t4jYewWk5L_xhYJpxRKtdWSGpeaPPMpdLrYEQ6CJhuRrDeKbawLa06dfc4LTC9IhAUBIKiBkEEr3rZWUMm8leptU7HRWtQFsUvBUfwsh9GU0D7O7r003OUoU1gyt9FBI8aOPWPSdAzyUxsRqCWgNYLEM348kg5OanpxlU8wKw8e_Lvab2A67ujTwfFwd5w_yncEFSWqCtVa7Bazc_9M7hmv1aTxfx5vWgYFJcMw5-l7Fpq |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFCE4lHcxFFgEHDgscdcbPw4IobYRUSFYVQ7lZO2ud9tIxQmJ24qf1n_HjF80gLj1wNlje-15fTM7OwPwSgmlUYUGPNcy51LJiGttEq6EjhDth2iX_WrYRDQex4eHSboGF-1ZGCqrbG1iZajzmaEceV9QpzmE037Qd01ZRLo7fD__zmmCFO20tuM0ahHZtz_OMXxbvhvtIq9fCzHcm-x85M2EAW7QkZUcvZ0W2hlLg7-NCXC9oXPSSIM4QhgXRibRLohdHOTKBbnwVeCCUIV-ogZIj4-9BuuIyKXowXo6-px-7RI8qB0i2U7qlqgR4q5-eVzM3krCZKtO8A9k-3uB5iWPN7z9H_-rO7DRwGz2odaLu7Bmi3tw61Lzxfuwd4AYmZfTb5ZR526y_HiHIS1YsLyuQJwuGR0MOGLpAf-yM2GUtWa5tXPWTNs4egCTq_iMh9ArZoV9BCwWuY6iRBkhaayhSeI8lFY5LcKBw0DUgzctkzPTNFynuR8nGQZeJBAZCURWCYQHLzvaed1m5K9UWy2_s8bULLNfzPbgRXcZjQTt_KjCzk6RhraHKbIXHmzWotW9JkCfJWOx7UG0InQdATUgX71STI-rRuSRP8B4PX7872U9hxsofdmn0Xj_CdwUlK-oUlhb0CsXp_YpXDdn5XS5eNZoEIPsiuXwJx4kZLk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Real-time+colorectal+cancer+diagnosis+using+PR-OCT+with+deep+learning&rft.jtitle=Theranostics&rft.au=Zeng%2C+Yifeng&rft.au=Xu%2C+Shiqi&rft.au=Chapman%2C+William+C.&rft.au=Li%2C+Shuying&rft.date=2020-01-01&rft.pub=Ivyspring+International+Publisher&rft.eissn=1838-7640&rft.volume=10&rft.issue=6&rft.spage=2587&rft.epage=2596&rft_id=info:doi/10.7150%2Fthno.40099&rft_id=info%3Apmid%2F32194821&rft.externalDocID=PMC7052898 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1838-7640&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1838-7640&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1838-7640&client=summon |