Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy

Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HS...

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Veröffentlicht in:	European journal of surgical oncology 2025-01, Vol.51 (1), p.106908, Article 106908
Hauptverfasser:	Nickel, F., Studier-Fischer, A., Özdemir, B., Odenthal, J., Müller, L.R., Knoedler, S., Kowalewski, K.F., Camplisson, I., Allers, M.M., Dietrich, M., Schmidt, K., Salg, G.A., Kenngott, H.G., Billeter, A.T., Gockel, I., Sagiv, C., Hadar, O.E., Gildenblat, J., Ayala, L., Seidlitz, S., Maier-Hein, L., Müller-Stich, B.P.
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Sprache:	eng
Schlagworte:	Anastomosis, Surgical - methods Anastomotic Leak Animals Esophagectomy - methods Esophagus - pathology Esophagus - surgery Gastric conduit Gastrostomy - methods Hyperspectral imaging Hyperspectral Imaging - methods Linear stapled anastomosis Machine Learning Minimally invasive esophagectomy Minimally Invasive Surgical Procedures - methods Models, Animal Porcine model Simulation Stomach - surgery Surgical Stapling - methods Swine Tissue perfusion
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container_title	European journal of surgical oncology
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creator	Nickel, F. Studier-Fischer, A. Özdemir, B. Odenthal, J. Müller, L.R. Knoedler, S. Kowalewski, K.F. Camplisson, I. Allers, M.M. Dietrich, M. Schmidt, K. Salg, G.A. Kenngott, H.G. Billeter, A.T. Gockel, I. Sagiv, C. Hadar, O.E. Gildenblat, J. Ayala, L. Seidlitz, S. Maier-Hein, L. Müller-Stich, B.P.
description	Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (−0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: −15.6 ± 11.5%, p = 0.0002; long-cranial: −20.4 ± 7.6%, p = 0.0126; long-caudal: −16.1 ± 9.4%, p
doi_str_mv	10.1016/j.ejso.2023.04.007
format	Article
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Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (−0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: −15.6 ± 11.5%, p = 0.0002; long-cranial: −20.4 ± 7.6%, p = 0.0126; long-caudal: −16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area. Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. The experimental model with HSI and ML allow for systematic optimization of gastric conduit perfusion and anastomotic technique while clinical translation will have to be proven.</description><identifier>ISSN: 0748-7983</identifier><identifier>ISSN: 1532-2157</identifier><identifier>EISSN: 1532-2157</identifier><identifier>DOI: 10.1016/j.ejso.2023.04.007</identifier><identifier>PMID: 37105869</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Anastomosis, Surgical - methods ; Anastomotic Leak ; Animals ; Esophagectomy - methods ; Esophagus - pathology ; Esophagus - surgery ; Gastric conduit ; Gastrostomy - methods ; Hyperspectral imaging ; Hyperspectral Imaging - methods ; Linear stapled anastomosis ; Machine Learning ; Minimally invasive esophagectomy ; Minimally Invasive Surgical Procedures - methods ; Models, Animal ; Porcine model ; Simulation ; Stomach - surgery ; Surgical Stapling - methods ; Swine ; Tissue perfusion</subject><ispartof>European journal of surgical oncology, 2025-01, Vol.51 (1), p.106908, Article 106908</ispartof><rights>2023 The Authors</rights><rights>Copyright © 2023 The Authors. 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Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (−0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: −15.6 ± 11.5%, p = 0.0002; long-cranial: −20.4 ± 7.6%, p = 0.0126; long-caudal: −16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area. Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. The experimental model with HSI and ML allow for systematic optimization of gastric conduit perfusion and anastomotic technique while clinical translation will have to be proven.</description><subject>Anastomosis, Surgical - methods</subject><subject>Anastomotic Leak</subject><subject>Animals</subject><subject>Esophagectomy - methods</subject><subject>Esophagus - pathology</subject><subject>Esophagus - surgery</subject><subject>Gastric conduit</subject><subject>Gastrostomy - methods</subject><subject>Hyperspectral imaging</subject><subject>Hyperspectral Imaging - methods</subject><subject>Linear stapled anastomosis</subject><subject>Machine Learning</subject><subject>Minimally invasive esophagectomy</subject><subject>Minimally Invasive Surgical Procedures - methods</subject><subject>Models, Animal</subject><subject>Porcine model</subject><subject>Simulation</subject><subject>Stomach - surgery</subject><subject>Surgical Stapling - methods</subject><subject>Swine</subject><subject>Tissue perfusion</subject><issn>0748-7983</issn><issn>1532-2157</issn><issn>1532-2157</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcGO1DAQRC0EYmcXfoAD8pFLgh0ndiJxQStgkVbaC5wtj92ZeBTbwXYGhr_iD3GYhSOnlqpfldRdCL2ipKaE8rfHGo4p1A1pWE3amhDxBO1ox5qqoZ14inZEtH0lhp5doeuUjoSQgYnhObpigpKu58MO_XpYsnX2p8o2eBxGrLxKObiQrcYZ9OTttxWKavChLGJRdfBmtRkvEMc1bbbvNk94OhchLaBzVDO2Th2sP_wxOqUn6wHPoKLfROuLjuFHMVgHPhfeBQMzHkPEzvpinudzwU4q2RNgSGGZ1KFEB3d-gZ6Nak7w8nHeoK8fP3y5vavuHz59vn1_X-mWkFwNwgjGuVGcjiDMqPux551hfNCGKtEMe26MVgZaIvZ7owyjbd813BDCuNCM3aA3l9wlhvKClKWzScM8Kw9hTbLpiRgoox0paHNBdQwpRRjlUg5T8SwpkVtV8ii3quRWlSStLFUV0-vH_HXvwPyz_O2mAO8uAJQrTxaiTNqC12BsLK-QJtj_5f8GckCr0Q</recordid><startdate>20250101</startdate><enddate>20250101</enddate><creator>Nickel, F.</creator><creator>Studier-Fischer, A.</creator><creator>Özdemir, B.</creator><creator>Odenthal, J.</creator><creator>Müller, L.R.</creator><creator>Knoedler, S.</creator><creator>Kowalewski, K.F.</creator><creator>Camplisson, I.</creator><creator>Allers, M.M.</creator><creator>Dietrich, M.</creator><creator>Schmidt, K.</creator><creator>Salg, G.A.</creator><creator>Kenngott, H.G.</creator><creator>Billeter, A.T.</creator><creator>Gockel, I.</creator><creator>Sagiv, C.</creator><creator>Hadar, O.E.</creator><creator>Gildenblat, J.</creator><creator>Ayala, L.</creator><creator>Seidlitz, S.</creator><creator>Maier-Hein, L.</creator><creator>Müller-Stich, B.P.</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2931-6247</orcidid><orcidid>https://orcid.org/0000-0002-3964-3527</orcidid><orcidid>https://orcid.org/0000-0003-3612-7401</orcidid><orcidid>https://orcid.org/0000-0002-1291-2520</orcidid><orcidid>https://orcid.org/0000-0002-3574-2085</orcidid><orcidid>https://orcid.org/0000-0001-7423-713X</orcidid><orcidid>https://orcid.org/0000-0001-5312-9551</orcidid><orcidid>https://orcid.org/0000-0003-4910-9368</orcidid><orcidid>https://orcid.org/0000-0002-8552-8538</orcidid><orcidid>https://orcid.org/0000-0001-8927-4830</orcidid><orcidid>https://orcid.org/0000-0001-5798-8003</orcidid><orcidid>https://orcid.org/0000-0001-9653-2789</orcidid><orcidid>https://orcid.org/0000-0003-0960-038X</orcidid><orcidid>https://orcid.org/0000-0001-8373-9406</orcidid><orcidid>https://orcid.org/0000-0001-6066-8238</orcidid><orcidid>https://orcid.org/0000-0001-8682-9300</orcidid><orcidid>https://orcid.org/0000-0001-8724-4793</orcidid><orcidid>https://orcid.org/0000-0002-1122-4793</orcidid><orcidid>https://orcid.org/0000-0003-1123-346X</orcidid></search><sort><creationdate>20250101</creationdate><title>Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy</title><author>Nickel, F. ; 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Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (−0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: −15.6 ± 11.5%, p = 0.0002; long-cranial: −20.4 ± 7.6%, p = 0.0126; long-caudal: −16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area. Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. 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source	MEDLINE; Elsevier ScienceDirect Journals
subjects	Anastomosis, Surgical - methods Anastomotic Leak Animals Esophagectomy - methods Esophagus - pathology Esophagus - surgery Gastric conduit Gastrostomy - methods Hyperspectral imaging Hyperspectral Imaging - methods Linear stapled anastomosis Machine Learning Minimally invasive esophagectomy Minimally Invasive Surgical Procedures - methods Models, Animal Porcine model Simulation Stomach - surgery Surgical Stapling - methods Swine Tissue perfusion
title	Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T06%3A09%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimization%20of%20anastomotic%20technique%20and%20gastric%20conduit%20perfusion%20with%20hyperspectral%20imaging%20and%20machine%20learning%20in%20an%20experimental%20model%20for%20minimally%20invasive%20esophagectomy&rft.jtitle=European%20journal%20of%20surgical%20oncology&rft.au=Nickel,%20F.&rft.date=2025-01-01&rft.volume=51&rft.issue=1&rft.spage=106908&rft.pages=106908-&rft.artnum=106908&rft.issn=0748-7983&rft.eissn=1532-2157&rft_id=info:doi/10.1016/j.ejso.2023.04.007&rft_dat=%3Cproquest_cross%3E2807913150%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2807913150&rft_id=info:pmid/37105869&rft_els_id=S0748798323004444&rfr_iscdi=true