Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope
The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric can...
Saved in:
| Published in | Contrast media and molecular imaging Vol. 2021; pp. 1 - 8 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Hindawi
14.09.2021
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1555-4309 1555-4317 1555-4317 |
| DOI | 10.1155/2021/1701447 |
Cover
| Abstract | The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (P<0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (P>0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (P>0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy. |
|---|---|
| AbstractList | The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (
< 0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (
> 0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (
> 0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy. The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (P < 0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (P > 0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (P > 0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy. The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (P < 0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (P > 0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (P > 0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy.The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (P < 0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (P > 0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (P > 0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy. The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant ( P < 0.05 ). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference ( P > 0.05 ). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications ( P > 0.05 ). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy. |
| Author | Zhao, Cansong Mo, Wenkui |
| AuthorAffiliation | 1 Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang, China 2 Department of General Surgery, Zhuji People's Hospital of Zhejiang, Zhuji 311800, Zhejiang, China |
| AuthorAffiliation_xml | – name: 2 Department of General Surgery, Zhuji People's Hospital of Zhejiang, Zhuji 311800, Zhejiang, China – name: 1 Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang, China |
| Author_xml | – sequence: 1 givenname: Wenkui orcidid: 0000-0002-3736-1219 surname: Mo fullname: Mo, Wenkui organization: Department of Thoracic SurgeryCancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Hangzhou 310022ZhejiangChina – sequence: 2 givenname: Cansong orcidid: 0000-0001-6532-8398 surname: Zhao fullname: Zhao, Cansong organization: Department of General SurgeryZhuji People’s Hospital of ZhejiangZhuji 311800ZhejiangChina |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34621143$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFUUtv1DAQtlARfcCNM8oRiYbasRMnF6RSlbLSIqQKztbUnmSNEjvYDtX-e7zapTwk4OSR53v58yk5ct4hIc8Zfc1YXV9UtGIXTFImhHxETvJVXQrO5NHDTLtjchrjF0qF4B1_Qo65aCrGBD8hauUSjqMd0KXichx8sGkzlW8hoik-wOAwWV3cYvQOnMZiNcFg3VBYV9yCsRrG4gZiCqiTn7bF4gyGYg0zBB-1n_EpedzDGPHZ4Twjn99df7p6X64_3qyuLtelFhVNpeGy5y1C0_BaYI59Z4TsGwHa9D2Trah507ai6QCgy6NGI2taNZQhq3oh-Rkp97qLm2F7D-Oo5mAnCFvFqNoVpXZFqUNRGf9mj5-XuwmNzs8P8JPjwarfN85u1OC_qRylrijLAi8PAsF_XTAmNdmoc5Xg0C9RVXVLm47Kbgd98avXg8mPX8iA8z1A59ZiwP5_2as_4NomSNbvktrxb6RXe9LGOgP39t8W3wHJ4bTC |
| CitedBy_id | crossref_primary_10_1016_j_optlastec_2025_112652 crossref_primary_10_1097_AS9_0000000000000415 |
| Cites_doi | 10.1109/tmi.2016.2538465 10.31557/apjcp.2020.21.10.2883 10.1002/14651858.CD011389.pub2 10.1007/s10439-020-02479-z 10.1007/s00464-019-06886-9 10.1007/s00464-018-06630-9 10.1007/s11548-020-02223-x 10.1007/s00464-016-5310-2 10.1016/j.surg.2017.09.053 10.3748/wjg.15.3992 10.1259/bjr.20181044 10.1038/s41598-020-77733-4 10.1016/j.jiph.2019.08.011 10.3322/caac.21338 10.1109/icpr.2014.106 10.1186/s12885-015-1551-z 10.1002/mp.13264 10.3390/cancers11091235 10.5152/dir.2019.19375 10.1007/s00423-020-01853-8 10.1007/s12194-017-0406-5 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Wenkui Mo and Cansong Zhao. Copyright © 2021 Wenkui Mo and Cansong Zhao. 2021 |
| Copyright_xml | – notice: Copyright © 2021 Wenkui Mo and Cansong Zhao. – notice: Copyright © 2021 Wenkui Mo and Cansong Zhao. 2021 |
| DBID | RHU RHW RHX AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM ADTOC UNPAY |
| DOI | 10.1155/2021/1701447 |
| DatabaseName | Hindawi Publishing Complete Hindawi Publishing Subscription Journals Hindawi Publishing Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: RHX name: Hindawi Publishing Open Access url: http://www.hindawi.com/journals/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1555-4317 |
| Editor | Teekaraman, Yuvaraja |
| Editor_xml | – sequence: 1 givenname: Yuvaraja surname: Teekaraman fullname: Teekaraman, Yuvaraja |
| EndPage | 8 |
| ExternalDocumentID | 10.1155/2021/1701447 PMC8455201 34621143 10_1155_2021_1701447 |
| Genre | Journal Article |
| GroupedDBID | --- .3N .GA 05W 0R~ 1L6 1OC 33P 3SF 3V. 3WU 4.4 50Y 50Z 52M 52O 52T 52U 52V 52W 53G 5GY 702 7PT 7X7 7XC 8-0 8-1 8-3 8-4 8-5 8FE 8FH 8FI 8UM 930 A01 A03 AAESR AAFWJ AAJEY AAONW ABIJN ABPVW ADBBV ADIZJ AENEX AEUQT AFBPY AFKRA ALAGY ALMA_UNASSIGNED_HOLDINGS AMBMR AOIJS ATCPS ATUGU AZBYB AZVAB BAFTC BCNDV BENPR BHBCM BHPHI BPHCQ BROTX BRXPI BVXVI BYOGL CS3 D-6 D-7 D-E D-F DPXWK DU5 EBD EBS EMOBN F00 F01 F04 F21 F5P FYUFA G-S G.N GODZA GROUPED_DOAJ H.X HBH HCIFZ HHY HHZ HYE HZ~ IAO IHR ITC LAW LITHE LP6 LP7 M1P MK4 MY~ N04 N05 NF~ O66 O9- OIG OK1 P2P P2W P2X P2Z P4B P4D PATMY PQQKQ PROAC PYCSY Q.N QB0 R.K RHU RHW RHX RPM RWI RX1 RYL SUPJJ SV3 UB1 UKHRP W8V W99 WBKPD WIH WIJ WVDHM XV2 ~IA ~WT 24P AAMMB AAYXX AEFGJ AGXDD AIDQK AIDYY CITATION H13 PGMZT .Y3 31~ 88E 8FJ AAEVG AANHP AAZKR ABUWG ACBWZ ACCMX ACRPL ACXQS ACYXJ ADNMO AEIMD AFTUV AGFTA AGQPQ ALIPV ASPBG AVWKF AZFZN BDRZF CCPQU CGR CUY CVF ECM EIF EJD FEDTE HF~ HMCUK HVGLF LH4 LW6 NPM PHGZM PHGZT PJZUB PPXIY PSQYO WYUIH 7X8 5PM ADTOC UNPAY |
| ID | FETCH-LOGICAL-c420t-d37f38ea66354e555bd47f64acdff178453688469aaa9368ced7502601e12f473 |
| IEDL.DBID | UNPAY |
| ISSN | 1555-4309 1555-4317 |
| IngestDate | Sun Oct 26 03:26:48 EDT 2025 Tue Sep 30 16:41:36 EDT 2025 Fri Sep 05 12:37:41 EDT 2025 Mon Jul 21 06:03:06 EDT 2025 Wed Oct 01 01:50:05 EDT 2025 Thu Apr 24 23:02:56 EDT 2025 Sun Jun 02 18:51:55 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Wenkui Mo and Cansong Zhao. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c420t-d37f38ea66354e555bd47f64acdff178453688469aaa9368ced7502601e12f473 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Academic Editor: Yuvaraja Teekaraman |
| ORCID | 0000-0002-3736-1219 0000-0001-6532-8398 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://downloads.hindawi.com/journals/cmmi/2021/1701447.pdf |
| PMID | 34621143 |
| PQID | 2580690791 |
| PQPubID | 23479 |
| PageCount | 8 |
| ParticipantIDs | unpaywall_primary_10_1155_2021_1701447 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8455201 proquest_miscellaneous_2580690791 pubmed_primary_34621143 crossref_primary_10_1155_2021_1701447 crossref_citationtrail_10_1155_2021_1701447 hindawi_primary_10_1155_2021_1701447 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-09-14 |
| PublicationDateYYYYMMDD | 2021-09-14 |
| PublicationDate_xml | – month: 09 year: 2021 text: 2021-09-14 day: 14 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Contrast media and molecular imaging |
| PublicationTitleAlternate | Contrast Media Mol Imaging |
| PublicationYear | 2021 |
| Publisher | Hindawi |
| Publisher_xml | – name: Hindawi |
| References | 11 22 12 23 S. Kitano (13) 1994; 4 14 15 16 17 18 19 1 3 4 5 M. Cesaretti (2) 2016; 71 6 7 8 9 20 10 21 |
| References_xml | – ident: 11 doi: 10.1109/tmi.2016.2538465 – volume: 4 start-page: 146 issue: 2 year: 1994 ident: 13 article-title: Laparoscopy-assisted billroth I gastrectomy publication-title: Surgical Laparoscopy & Endoscopy – volume: 71 start-page: 270 issue: 4 year: 2016 ident: 2 article-title: In vivo medical imaging technologies: new possibility in diagnosis of gastric cancer publication-title: Minerva Chirurgica – ident: 15 doi: 10.31557/apjcp.2020.21.10.2883 – ident: 5 doi: 10.1002/14651858.CD011389.pub2 – ident: 16 doi: 10.1007/s10439-020-02479-z – ident: 14 doi: 10.1007/s00464-019-06886-9 – ident: 17 doi: 10.1007/s00464-018-06630-9 – ident: 23 doi: 10.1007/s11548-020-02223-x – ident: 19 doi: 10.1007/s00464-016-5310-2 – ident: 18 doi: 10.1016/j.surg.2017.09.053 – ident: 20 doi: 10.3748/wjg.15.3992 – ident: 4 doi: 10.1259/bjr.20181044 – ident: 21 doi: 10.1038/s41598-020-77733-4 – ident: 7 doi: 10.1016/j.jiph.2019.08.011 – ident: 1 doi: 10.3322/caac.21338 – ident: 10 doi: 10.1109/icpr.2014.106 – ident: 12 doi: 10.1186/s12885-015-1551-z – ident: 22 doi: 10.1002/mp.13264 – ident: 9 doi: 10.3390/cancers11091235 – ident: 3 doi: 10.5152/dir.2019.19375 – ident: 6 doi: 10.1007/s00423-020-01853-8 – ident: 8 doi: 10.1007/s12194-017-0406-5 |
| SSID | ssj0044393 |
| Score | 2.2476807 |
| Snippet | The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref hindawi |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1 |
| SubjectTerms | Adult Aged Algorithms Female Gastrectomy - standards Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Laparoscopy - standards Lymph Node Excision - standards Magnetic Resonance Imaging Male Middle Aged Operative Time Stomach Neoplasms - diagnostic imaging Stomach Neoplasms - pathology Stomach Neoplasms - surgery Treatment Outcome |
| SummonAdditionalLinks | – databaseName: Hindawi Publishing Open Access dbid: RHX link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fT9swELYGEmMvCNgGZWzyJOBlsoabc5w8smmsTCsPCKS-Rf4VWqlNKkiF-O93l6YRBbbxFivnyLlzct_5zp8ZO7DWeRulVuTaxAK0VSLJgxXKu64M4GJvaGmgfx73ruDXQA0akqTbpyl89HYUnsuvRBsOoFfYShJT5dZFb7D44QL61LqOXiklIDpOF_Xtj_oueZ61IYW8d6PngOXT-sj1WTE193dmPH7gfE432UaDGvnJ3Mxb7FUottnrfpMXf8uys5ZZs-In4-sSQ_7hRHxDF-V531wXtFWR01I98WsEfjapDyfio4JfmDpTw38a2jbiqnJyz2lj2Q3_jX6UuC7LaXjHrk5_XH7vieboBOGge1wJH-k8SoIhPAEBFWM96DwG43yeS52AiuIEoUdqjEnx0gWP0IHoxYLs5qCj92y1KIuwy7h0GCVjJ4-KB7DSOsR4FmRQ1mAz6rAvC7VmruEVp-MtxlkdXyiVkRGyxggddthKT-d8Gn-RO2gs9B-xzwvzZfhdULLDFKGc3WZdlRAJs05lh-3Mzdk-KYIY417AoeslQ7cCxLm9fKcYDWvubdScQszUYUftlPjnAPde9h4f2BtqUiGKhH22Wt3MwkdEO5X9VM_1P5gD9mw priority: 102 providerName: Hindawi Publishing |
| Title | Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope |
| URI | https://dx.doi.org/10.1155/2021/1701447 https://www.ncbi.nlm.nih.gov/pubmed/34621143 https://www.proquest.com/docview/2580690791 https://pubmed.ncbi.nlm.nih.gov/PMC8455201 https://downloads.hindawi.com/journals/cmmi/2021/1701447.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 2021 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1555-4317 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0044393 issn: 1555-4317 databaseCode: RPM dateStart: 20170101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFD7aOsF44X4pl8pIgxeUDjd2nEq8FMToEJ3QRKXygCLfslZrkqpNNY1fj0_qRJS7eLOVE8c-Po6_Yx9_BjhQShsV9lWQChkFTCgexKlVATe6Ry3TkZG4NDA6iYZj9n7CJzvwqj4LY5AivpBm1Z2iT3oxq_7WXq-rQ51lM_TX6SHyiDMmuguT7sJexB0Sb8He-OTj4HNFkcp5wMIqwsOnqajj3jnfKmJrRrriP_srwPlz3OT-Ol_Iyws5n383KR3dgC91czaxKOfddam6-usPTI__296bcN2jVTLYmNct2LH5bbg68vvxdyA5bhg9SzKYnxXLWTnNgtduajRkJM9yPCJJcIsAeT0sOc6qS5HILCenstohIu8kHlfRZZFdEjzQtiQf3PyNHJvFwt6F8dHbT2-Ggb-yIdCs97IMTCjSMLYScQyzTvHKMJFGTGqTplTEjIdR7CBPX0rZd0ltjYMsSGtmaS9lIrwHrbzI7QMgVDvv3L1k4ihmTFGlHbZUjFqupMuGbXhRd1uiPZ85XqsxTyq_hvME9ZZ4vbXhWSO92PB4_EbuwHfEX8Se1uaRuPGImywyt8V6lfR4jOTPok_bcH9jLk1JIYucv81c1cWWITUCyPW9_SSfTSvOb6c57rBaG543JvfHCj78V8FHcA2zGAJD2WNolcu1feJwVqk6sHs6nHSqBbCOH1rfAATJJWk |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED-NTny88M3oBshIgxeUDjd2nEp7KYixITohRKXxMEX-yhqtSao21bT99fhSJ6J8i7dYuTj2-Rz_Lnf-GWBXKW1UOFBBKmQUMKF4EKdWBdzoPrVMR0bir4HRcXQ4Zh9O-MkG7Dd7YQxSxJfSLHoT9Ekvsvpr7fW62NN5nqG_TveQR5wx0ZuZ9BpsRtwh8Q5sjo8_Db_WFKmcByysMzz8NRVN3jvna1WsrUjX_Wt_BTh_zpu8uSxm8vJCTqffLUoHd-C06c4qF-W8t6xUT1_9wPT4v_29C7c9WiXDlXndgw1b3IcbIx-PfwDJUcvoWZHh9KycZ9UkD964pdGQkTwrcIskwRAB8npYcpTXhyKRrCCfZR0hIu8lblfRVZlfEtzQNicf3fqNHJvlzD6E8cG7L28PA39kQ6BZ_3UVmFCkYWwl4hhmneKVYSKNmNQmTamIGQ-j2EGegZRy4C61NQ6yIK2Zpf2UifARdIqysI-BUO28c_eQiaOYMUWVdthSMWq5kq4YduFVM2yJ9nzmeKzGNKn9Gs4T1Fvi9daFF630bMXj8Ru5XT8QfxF73phH4uYjBllkYcvlIunzGMmfxYB2YWtlLm1NIYucv81c08WaIbUCyPW9fqfIJjXnt9Mcd1itCy9bk_tjA7f_VXAHbmERU2AoewKdar60Tx3OqtQzP52-AVTYI0Y |
| 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=Intelligent+Algorithm-Based+Magnetic+Resonance+Imaging+in+Radical+Gastrectomy+under+Laparoscope&rft.jtitle=Contrast+media+and+molecular+imaging&rft.au=Mo%2C+Wenkui&rft.au=Zhao%2C+Cansong&rft.date=2021-09-14&rft.issn=1555-4317&rft.eissn=1555-4317&rft.volume=2021&rft.spage=1701447&rft_id=info:doi/10.1155%2F2021%2F1701447&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1555-4309&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1555-4309&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1555-4309&client=summon |