{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:08:26Z","timestamp":1774868906388,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hochschule Anhalt"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Esophageal cancer is widespread worldwide, with the highest rate in Asia. Early diagnosis plays a key role in increasing the survival rate. Early cancer detection as well as fast evaluation of tumor extent before and resection margins during\/after surgery are important to improve patients\u2019 outcomes. Hyperspectral imaging (HSI), as a noninvasive and contactless novel intraoperative technique, has shown promising results in cancer detecting in combination with artificial intelligence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this clinical study, the extent to which physiological parameters, such as water or hemoglobin content, differ in the esophagus, stomach, and cancer tissue, was examined. For this purpose, hyperspectral intraluminal recordings of affected tissue specimen were carried out. In addition, a classification of the three intraluminal tissue types (esophageal, stomach mucosa, and cancerous tissue) was performed by using two different convolutional neural networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis clearly demonstrated differences in hemoglobin concentration and water content between healthy and cancerous tissues, as well as among different tumor stages. As classification results, an averaged area under the curve score of 81\u2009\u00b1\u20093%, a sensitivity of 74\u2009\u00b1\u20098%, and a specificity of 89\u2009\u00b1\u20092% could be achieved across all tissue types using a hybrid convolutional neural network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>HSI has relevant potential for supporting the detection of tumorous tissue in esophageal cancer. However, further analyses including more detailed histopathologic correlation as \u201cgold standard\u201d are needed. Data augmentation and future multicenter studies have to be carried out. These steps may help to improve and sharpen our current findings, especially for esophageal cancerous tissue.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03514-x","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T07:36:24Z","timestamp":1758612984000},"page":"625-633","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of esophageal cancer by using hyperspectral data"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6954-4530","authenticated-orcid":false,"given":"Marianne","family":"Maktabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Hain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannes","family":"K\u00f6hler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Huber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren\u00e9","family":"Thieme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katrin","family":"Schierle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris","family":"Jansen-Winkeln","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ines","family":"Gockel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"3514_CR1","doi-asserted-by":"publisher","unstructured":"Ajani JA, D\u2019Amico TA, Bentrem DJ, Cooke D, Corvera C, Das P,  Enzinger PC, Enzler T, Farjah F, Gerdes H, Gibson M, Grierson P, Hofstetter WL,  Ilson DH, Jalal S, Keswani RN, Kim S, Kleinberg LR, Klempner S, Lacy J, Licciardi F, Ly QP,  Matkowskyj KA, McNamara M, Miller A, Mukherjee S,  Mulcahy MF, Outlaw D, Perry KA, Pimiento J,  Poultsides GA, Reznik S, Roses RE, Strong VE, Su S, Wang HL, Wiesner G, Willett CG, Yakoub D, Yoon H, McMillian NR, Pluchino LA (2023) Esophageal and esophagogastric junction cancers, version 22023, NCCN clinical practice guidelines in oncology. J Nat Compr Cancer Netw 21(4):393\u2013422. https:\/\/doi.org\/10.6004\/jnccn.2023.0019","DOI":"10.6004\/jnccn.2023.0019"},{"key":"3514_CR2","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.semradonc.2006.09.007","volume":"17","author":"JR Siewert","year":"2007","unstructured":"Siewert JR, Ott K (2007) Are squamous and adenocarcinomas of the esophagus the same disease? Semin Radiat Oncol 17:38\u201344. https:\/\/doi.org\/10.1016\/j.semradonc.2006.09.007","journal-title":"Semin Radiat Oncol"},{"key":"3514_CR3","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3322\/caac.21388","volume":"67","author":"MB Amin","year":"2017","unstructured":"Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP (2017) The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more \u201cpersonalized\u201d approach to cancer staging. CA Cancer J Clin 67:93\u201399. https:\/\/doi.org\/10.3322\/caac.21388","journal-title":"CA Cancer J Clin"},{"key":"3514_CR4","unstructured":"AWMF - Registriernummer 021\/023 OL, Leitlinienprogramm Onkologie, S3 - Leitlinie Diagnostik und Therapie der Plattenepithelkarzinome und Adenokarzinome des \u00d6sophagus, Langversion 4.0, (2023)."},{"key":"3514_CR5","unstructured":"AWMF-Registernummer: 032\/009OL, Leilinienprogramm Onkologie, S3-Leitlinie Diagnostik und Therapie der Adenokarzinome des Magens und \u00f6sophagogastralen \u00dcbergang, Langversion 3.0 (2025)."},{"key":"3514_CR6","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/s00104-019-0954-9","volume":"90","author":"AH H\u00f6lscher","year":"2019","unstructured":"H\u00f6lscher AH, Gockel I, Porschen R (2019) Updated German S3 guidelines on esophageal cancer and supplements from a surgical perspective. Chir Z Alle Geb Oper Medizen 90:398\u2013402. https:\/\/doi.org\/10.1007\/s00104-019-0954-9","journal-title":"Chir Z Alle Geb Oper Medizen"},{"key":"3514_CR7","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1159\/000065835","volume":"19","author":"V Velanovich","year":"2002","unstructured":"Velanovich V, Hollingsworth J, Suresh P, Ben-Menachem T (2002) Relationship of gastroesophageal reflux disease with adenocarcinoma of the distal esophagus and cardia. Dig Surg 19:349\u2013353. https:\/\/doi.org\/10.1159\/000065835","journal-title":"Dig Surg"},{"key":"3514_CR8","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1056\/NEJM199903183401101","volume":"340","author":"J Lagergren","year":"1999","unstructured":"Lagergren J, Bergstr\u00f6m R, Lindgren A, Nyr\u00e9n O (1999) Symptomatic gastroesophageal reflux as a risk factor for esophageal adenocarcinoma. N Engl J Med 340:825\u2013831. https:\/\/doi.org\/10.1056\/NEJM199903183401101","journal-title":"N Engl J Med"},{"key":"3514_CR9","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1093\/annonc\/mds244","volume":"24","author":"F Turati","year":"2013","unstructured":"Turati F, Tramacere I, La Vecchia C, Negri E (2013) A meta-analysis of body mass index and esophageal and gastric cardia adenocarcinoma. Ann Oncol 24:609\u2013617. https:\/\/doi.org\/10.1093\/annonc\/mds244","journal-title":"Ann Oncol"},{"key":"3514_CR10","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1111\/j.1440-1746.2009.05927.x","volume":"24","author":"K Takizawa","year":"2009","unstructured":"Takizawa K, Matsuda T, Kozu T, Eguchi T, Kato H, Nakanishi Y, Hijikata A, Saito D (2009) Lymph node staging in esophageal squamous cell carcinoma: a comparative study of endoscopic ultrasonography versus computed tomography. J Gastroenterol Hepatol 24:1687\u20131691. https:\/\/doi.org\/10.1111\/j.1440-1746.2009.05927.x","journal-title":"J Gastroenterol Hepatol"},{"key":"3514_CR11","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.asjsur.2016.10.005","volume":"41","author":"F-L Huang","year":"2018","unstructured":"Huang F-L, Yu S-J (2018) Esophageal cancer: risk factors, genetic association, and treatment. Asian J Surg 41:210\u2013215. https:\/\/doi.org\/10.1016\/j.asjsur.2016.10.005","journal-title":"Asian J Surg"},{"key":"3514_CR12","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1159\/000439473","volume":"31","author":"V Meves","year":"2015","unstructured":"Meves V, Behrens A, Pohl J (2015) Diagnostics and early diagnosis of esophageal cancer. Visc Med 31:315\u2013318. https:\/\/doi.org\/10.1159\/000439473","journal-title":"Visc Med"},{"key":"3514_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/jbio.201800324","author":"Y Li","year":"2019","unstructured":"Li Y, Xie X, Yang X, Guo L, Liu Z, Zhao X, Luo Y, Jia W, Huang F, Zhu S, Chen Z, Chen X, Wei Z, Zhang W (2019) Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine. J Biophotonics. https:\/\/doi.org\/10.1002\/jbio.201800324","journal-title":"J Biophotonics"},{"key":"3514_CR14","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1055\/a-1030-3232","volume":"145","author":"I Gockel","year":"2020","unstructured":"Gockel I, Jansen-Winkeln B, Sucher R, Rayes N, Thieme R, Moulla Y, Niebisch S, Rademacher S, Seehofer D, Schierle K, Bl\u00e4ker H, Neumuth T, Melzer A, Maktabi M, K\u00f6hler H, Chalopin C (2020) Hyperspektral-Imaging (HSI) \u2013 eine verl\u00e4ssliche Gewebedifferenzierung? Zentralbl Chir 145:125\u2013129. https:\/\/doi.org\/10.1055\/a-1030-3232","journal-title":"Zentralbl Chir"},{"key":"3514_CR15","doi-asserted-by":"publisher","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu G, Fei B (2014) Medical hyperspectral imaging: a review. J Biomed Opt 19:010901. https:\/\/doi.org\/10.1117\/1.JBO.19.1.010901","journal-title":"J Biomed Opt"},{"key":"3514_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JBO.24.1.016002","volume":"24","author":"EJM Baltussen","year":"2019","unstructured":"Baltussen EJM, Kok END, Brouwer de Koning SG, Sanders J, Aalbers AGJ, Kok NFM, Beets GL, Flohil CC, Bruin SC, Kuhlmann KFD, Sterenborg HJCM, Ruers TJM (2019) Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery. J Biomed Opt 24:1. https:\/\/doi.org\/10.1117\/1.JBO.24.1.016002","journal-title":"J Biomed Opt"},{"key":"3514_CR17","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1364\/BOE.381257","volume":"11","author":"M Halicek","year":"2020","unstructured":"Halicek M, Dormer JD, Little JV, Chen AY, Fei B (2020) Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomed Opt Express 11:1383. https:\/\/doi.org\/10.1364\/BOE.381257","journal-title":"Biomed Opt Express"},{"issue":"12","key":"3514_CR18","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.1158\/1078-0432.CCR-18-2089","volume":"25","author":"E Kho","year":"2019","unstructured":"Kho E, de Boer LL, Van de Vijver KK, van Duijnhoven F, Vrancken Peeters MJT, Sterenborg HJ, Ruers TJ (2019) Hyperspectral imaging for resection margin assessment during cancer surgery. Clin Cancer Res 25(12):3572\u20133580. https:\/\/doi.org\/10.1158\/1078-0432.CCR-18-2089","journal-title":"Clin Cancer Res"},{"key":"3514_CR19","doi-asserted-by":"publisher","unstructured":"Kim B, Kehtarnavaz N, LeBoulluec P, Liu H, Peng Y, & Euhus D (2013) Automation of ROI extraction in hyperspectral breast images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3658-3661). IEEE.https:\/\/doi.org\/10.1109\/EMBC.2013.6610336.","DOI":"10.1109\/EMBC.2013.6610336"},{"key":"3514_CR20","doi-asserted-by":"publisher","first-page":"101922","DOI":"10.1016\/j.pdpdt.2020.101922","volume":"31","author":"IH Aboughaleb","year":"2020","unstructured":"Aboughaleb IH, Aref MH, El-Sharkawy YH (2020) Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer. Photodiagnosis Photodyn Ther 31:101922. https:\/\/doi.org\/10.1016\/j.pdpdt.2020.101922","journal-title":"Photodiagnosis Photodyn Ther"},{"key":"3514_CR21","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13005-021-00292-0","volume":"17","author":"DGE Thiem","year":"2021","unstructured":"Thiem DGE, R\u00f6mer P, Gielisch M, Al-Nawas B, Schl\u00fcter M, Pla\u00df B, K\u00e4mmerer PW (2021) Hyperspectral imaging and artificial intelligence to detect oral malignancy \u2013 part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network. Head Face Med 17:38. https:\/\/doi.org\/10.1186\/s13005-021-00292-0","journal-title":"Head Face Med"},{"key":"3514_CR22","doi-asserted-by":"publisher","first-page":"14043","DOI":"10.1038\/s41598-019-50313-x","volume":"9","author":"M Halicek","year":"2019","unstructured":"Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, Sumer BD, Fei B (2019) Head and neck cancer detection in digitized whole-slide histology using convolutional neural networks. Sci Rep 9:14043. https:\/\/doi.org\/10.1038\/s41598-019-50313-x","journal-title":"Sci Rep"},{"key":"3514_CR23","doi-asserted-by":"publisher","first-page":"920","DOI":"10.3390\/s19040920","volume":"19","author":"H Fabelo","year":"2019","unstructured":"Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Pi\u00f1eiro J, Sosa C, O\u2019Shanahan A, Bisshopp S, Espino C, M\u00e1rquez M, Hern\u00e1ndez M, Carrera D, Morera J, Callico G, Sarmiento R, Fei B (2019) Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors 19:920. https:\/\/doi.org\/10.3390\/s19040920","journal-title":"Sensors"},{"key":"3514_CR24","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1002\/jbio.201600021","volume":"10","author":"M Hohmann","year":"2017","unstructured":"Hohmann M, Kanawade R, Kl\u00e4mpfl F, Douplik A, Mudter J, Neurath MF, Albrecht H (2017) In-vivo multispectral video endoscopy towards in-vivo hyperspectral video endoscopy. J Biophotonics 10:553\u2013564. https:\/\/doi.org\/10.1002\/jbio.201600021","journal-title":"J Biophotonics"},{"key":"3514_CR25","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1002\/lsm.23055","volume":"51","author":"A Hosking","year":"2019","unstructured":"Hosking A, Coakley BJ, Chang D, Talebi-Liasi F, Lish S, Lee SW, Zong AM, Moore I, Browning J, Jacques SL, Krueger JG, Kelly KM, Linden KG, Gareau DS (2019) Hyperspectral imaging in automated digital dermoscopy screening for melanoma. Lasers Surg Med 51:214\u2013222. https:\/\/doi.org\/10.1002\/lsm.23055","journal-title":"Lasers Surg Med"},{"key":"3514_CR26","doi-asserted-by":"publisher","first-page":"e202100231","DOI":"10.1002\/jbio.202100231","volume":"15","author":"MA Calin","year":"2022","unstructured":"Calin MA, Parasca SV (2022) Automatic detection of basal cell carcinoma by hyperspectral imaging. J Biophotonics 15:e202100231. https:\/\/doi.org\/10.1002\/jbio.202100231","journal-title":"J Biophotonics"},{"key":"3514_CR27","doi-asserted-by":"publisher","unstructured":"Eggert D, Bengs M, Westermann S, Gessert N, Gerstner A O, M\u00fcller N A. & Laffers, W (2020). In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (conference presentation). In: Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020 (Vol. 11213, p. 112130L). SPIE. https:\/\/doi.org\/10.1117\/12.2557496.","DOI":"10.1117\/12.2557496"},{"key":"3514_CR28","doi-asserted-by":"publisher","first-page":"e0193721","DOI":"10.1371\/journal.pone.0193721","volume":"13","author":"H Fabelo","year":"2018","unstructured":"Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, Callic\u00f3 GM, Bulstrode H, Szolna A, Pi\u00f1eiro JF, Kabwama S, Madro\u00f1al D, Lazcano R, J-O\u2019Shanahan A, Bisshopp S, Hern\u00e1ndez M, B\u00e1ez A, Yang G-Z, Stanciulescu B, Salvador R, Ju\u00e1rez E, Sarmiento R (2018) Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS ONE 13:e0193721. https:\/\/doi.org\/10.1371\/journal.pone.0193721","journal-title":"PLoS ONE"},{"key":"3514_CR29","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.3390\/diagnostics11101810","volume":"11","author":"T Collins","year":"2021","unstructured":"Collins T, Maktabi M, Barberio M, Bencteux V, Jansen-Winkeln B, Chalopin C, Marescaux J, Hostettler A, Diana M, Gockel I (2021) Automatic recognition of colon and esophagogastric cancer with machine learning and hyperspectral imaging. Diagnostics 11:1810. https:\/\/doi.org\/10.3390\/diagnostics11101810","journal-title":"Diagnostics"},{"key":"3514_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07524-6","author":"M Maktabi","year":"2022","unstructured":"Maktabi M, Wichmann Y, K\u00f6hler H, Ahle H, Lorenz D, Bange M, Braun S, Gockel I, Chalopin C, Thieme R (2022) Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-022-07524-6","journal-title":"Sci Rep"},{"key":"3514_CR31","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1515\/cdbme-2020-3066","volume":"6","author":"M Maktabi","year":"2020","unstructured":"Maktabi M, K\u00f6hler H, Chalopin C, Neumuth T, Wichmann Y, Jansen-Winkeln B, Gockel I, Thieme R, Ahle H, Lorenz D, Bange M, Braun S (2020) Semi-automatic decision-making process in histopathological specimens from Barrett\u2019s carcinoma patients using hyperspectral imaging (HSI). Curr Dir Biomed Eng 6:261\u2013263. https:\/\/doi.org\/10.1515\/cdbme-2020-3066","journal-title":"Curr Dir Biomed Eng"},{"key":"3514_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-02016-x","author":"M Maktabi","year":"2019","unstructured":"Maktabi M, K\u00f6hler H, Ivanova M, Jansen-Winkeln B, Takoh J, Niebisch S, Rabe SM, Neumuth T, Gockel I, Chalopin C (2019) Tissue classification of oncologic esophageal resectates based on hyperspectral data. Int J Comput Assist Radiol Surg. https:\/\/doi.org\/10.1007\/s11548-019-02016-x","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3514_CR33","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/TBME.2008.915680","volume":"55","author":"EJ Seibel","year":"2008","unstructured":"Seibel EJ, Carroll RE, Dominitz JA, Johnston RS, Melville CD, Lee CM, Seitz SM, Kimmey MB (2008) Tethered capsule endoscopy, a low-cost and high-performance alternative technology for the screening of esophageal cancer and Barrett\u2019s esophagus. IEEE Trans Biomed Eng 55:1032\u20131042. https:\/\/doi.org\/10.1109\/TBME.2008.915680","journal-title":"IEEE Trans Biomed Eng"},{"key":"3514_CR34","doi-asserted-by":"publisher","first-page":"4593","DOI":"10.3390\/cancers13184593","volume":"13","author":"C-L Tsai","year":"2021","unstructured":"Tsai C-L, Mukundan A, Chung C-S, Chen Y-H, Wang Y-K, Chen T-H, Tseng Y-S, Huang C-W, Wu I-C, Wang H-C (2021) Hyperspectral imaging combined with artificial intelligence in the early detection of esophageal cancer. Cancers 13:4593. https:\/\/doi.org\/10.3390\/cancers13184593","journal-title":"Cancers"},{"key":"3514_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-32139-1","author":"I-C Wu","year":"2018","unstructured":"Wu I-C, Syu H-Y, Jen C-P, Lu M-Y, Chen Y-T, Wu M-T, Kuo C-T, Tsai Y-Y, Wang H-C (2018) Early identification of esophageal squamous neoplasm by hyperspectral endoscopic imaging. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-018-32139-1","journal-title":"Sci Rep"},{"key":"3514_CR36","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1088\/0967-3334\/37\/11\/2064","volume":"37","author":"A Holmer","year":"2016","unstructured":"Holmer A, Tetschke F, Marotz J, Malberg H, Markgraf W, Thiele C, Kulcke A (2016) Oxygenation and perfusion monitoring with a hyperspectral camera system for chemical based tissue analysis of skin and organs. Physiol Meas 37:2064\u20132078. https:\/\/doi.org\/10.1088\/0967-3334\/37\/11\/2064","journal-title":"Physiol Meas"},{"key":"3514_CR37","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.3390\/cancers15072157","volume":"15","author":"M Tkachenko","year":"2023","unstructured":"Tkachenko M, Chalopin C, Jansen-Winkeln B, Neumuth T, Gockel I, Maktabi M (2023) Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers 15:2157. https:\/\/doi.org\/10.3390\/cancers15072157","journal-title":"Cancers"},{"key":"3514_CR38","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10120-015-0537-5","volume":"19","author":"G Roviello","year":"2016","unstructured":"Roviello G, Petrioli R, Marano L, Polom K, Marrelli D, Perrella A, Roviello F (2016) Angiogenesis inhibitors in gastric and gastroesophageal junction cancer. Gastric Cancer 19:31\u201341. https:\/\/doi.org\/10.1007\/s10120-015-0537-5","journal-title":"Gastric Cancer"},{"key":"3514_CR39","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.2741\/1973","volume":"11","author":"A Islam","year":"2006","unstructured":"Islam A (2006) Angiogenic switch in Barrett\u2019s adenocarcinoma: the role of vascular endothelial growth factor. Front Biosci 11:2336. https:\/\/doi.org\/10.2741\/1973","journal-title":"Front Biosci"},{"key":"3514_CR40","first-page":"456","volume":"62","author":"D Kajdaniuk","year":"2011","unstructured":"Kajdaniuk D, Marek B, Foltyn W, Kos-Kud\u0142a B (2011) Vascular endothelial growth factor (VEGF) - part 2: in endocrinology and oncology. Endokrynol Pol 62:456\u2013464","journal-title":"Endokrynol Pol"},{"key":"3514_CR41","doi-asserted-by":"publisher","first-page":"967","DOI":"10.3390\/cancers13050967","volume":"13","author":"B Jansen-Winkeln","year":"2021","unstructured":"Jansen-Winkeln B, Barberio M, Chalopin C, Schierle K, Diana M, K\u00f6hler H, Gockel I, Maktabi M (2021) Feedforward artificial neural network-based colorectal cancer detection using hyperspectral imaging: a step towards automatic optical biopsy. Cancers (Basel) 13:967. https:\/\/doi.org\/10.3390\/cancers13050967","journal-title":"Cancers (Basel)"},{"key":"3514_CR42","doi-asserted-by":"publisher","first-page":"102488","DOI":"10.1016\/j.media.2022.102488","volume":"80","author":"S Seidlitz","year":"2022","unstructured":"Seidlitz S, Sellner J, Odenthal J, \u00d6zdemir B, Studier-Fischer A, Kn\u00f6dler S, Ayala L, Adler TJ, Kenngott HG, Tizabi M, Wagner M, Nickel F, M\u00fcller-Stich BP, Maier-Hein L (2022) Robust deep learning-based semantic organ segmentation in hyperspectral images. Med Image Anal 80:102488. https:\/\/doi.org\/10.1016\/j.media.2022.102488","journal-title":"Med Image Anal"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03514-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03514-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03514-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:21:22Z","timestamp":1774866082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03514-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["3514"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03514-x","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"13 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that all human and animal studies have been approved and performed in accordance with ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal participate"}}]}}