{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T09:01:44Z","timestamp":1780304504557,"version":"3.54.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Head and Neck Pathol"],"DOI":"10.1007\/s12105-026-01920-4","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:26:09Z","timestamp":1780298769000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations"],"prefix":"10.1007","volume":"20","author":[{"given":"Tabasum","family":"Majeed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Wajid","family":"Aalam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tariq Ahmad","family":"Masoodi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul Basit","family":"Ahanger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepika","family":"Misra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Nayyar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meraj Alam","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajaz A.","family":"Bhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzafar Rasool","family":"Bhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzafar Ahmad","family":"Macha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Assif","family":"Assad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"1920_CR1","doi-asserted-by":"crossref","unstructured":"Mehanna H, Paleri V, West C, Nutting C (2010) Head and neck cancer\u2014Part 1: Epidemiology, presentation, and prevention. BMJ, 341","DOI":"10.1136\/bmj.c4684"},{"key":"1920_CR2","first-page":"394","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394\u2013424","journal-title":"CA Cancer J Clin"},{"key":"1920_CR3","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s12094-020-02533-1","volume":"23","author":"R Mesia","year":"2021","unstructured":"Mesia R, Iglesias L, Lambea J, Mart\u00ednez-Trufero J, Soria A, Taberna M, Trigo J, Chaves M, Garc\u00eda-Casta\u00f1o A, Cruz J (2021) SEOM clinical guidelines for the treatment of head and neck cancer (2020). Clin Transl Oncol 23:913\u2013921","journal-title":"Clin Transl Oncol"},{"key":"1920_CR4","doi-asserted-by":"crossref","unstructured":"Marur S, Forastiere AA (2016) Head and neck squamous cell carcinoma: update on epidemiology, diagnosis, and treatment. In: Mayo clinic proceedings, Elsevier, pp. 386\u2013396","DOI":"10.1016\/j.mayocp.2015.12.017"},{"key":"1920_CR5","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1038\/s41571-022-00603-7","volume":"19","author":"M Lechner","year":"2022","unstructured":"Lechner M, Liu J, Masterson L, Fenton TR (2022) HPV-associated oropharyngeal cancer: epidemiology, molecular biology and clinical management. Nat reviews Clin Oncol 19:306\u2013327","journal-title":"Nat reviews Clin Oncol"},{"key":"1920_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1056\/NEJMoa0912217","volume":"363","author":"KK Ang","year":"2010","unstructured":"Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-T\u00e2n PF, Westra WH, Chung CH, Jordan RC, Lu C (2010) Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med 363:24\u201335","journal-title":"N Engl J Med"},{"key":"1920_CR7","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/s41572-020-00224-3","volume":"6","author":"DE Johnson","year":"2020","unstructured":"Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR (2020) Head and neck squamous cell carcinoma. Nat reviews Disease primers 6:92","journal-title":"Nat reviews Disease primers"},{"key":"1920_CR8","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1056\/NEJMra1715715","volume":"382","author":"LQ Chow","year":"2020","unstructured":"Chow LQ (2020) Head and neck cancer. N Engl J Med 382:60\u201372","journal-title":"N Engl J Med"},{"key":"1920_CR9","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.14639\/0392-100X-suppl.1-40-2020","volume":"40","author":"P Pisani","year":"2020","unstructured":"Pisani P, Airoldi M, Allais A, Valletti PA, Battista M, Benazzo M, Briatore R, Cacciola S, Cocuzza S, Colombo A (2020) Metastatic disease in head & neck oncology. Acta Otorhinolaryngol Ital 40:S1","journal-title":"Acta Otorhinolaryngol Ital"},{"key":"1920_CR10","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.ijrobp.2011.07.014","volume":"83","author":"M Yao","year":"2012","unstructured":"Yao M, Lu M, Savvides PS, Rezaee R, Zender CA, Lavertu P, Buatti JM, Machtay M (2012) Distant metastases in head-and-neck squamous cell carcinoma treated with intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 83:684\u2013689","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"1920_CR11","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1016\/j.oraloncology.2012.03.013","volume":"48","author":"RP Takes","year":"2012","unstructured":"Takes RP, Rinaldo A, Silver CE, Haigentz M Jr., Woolgar JA, Triantafyllou A, Mondin V, Paccagnella D, de Bree R, Shaha AR, Hartl DM, Ferlito A (2012) Distant metastases from head and neck squamous cell carcinoma. Part I. Basic aspects. Oral Oncol 48:775\u2013779","journal-title":"Oral Oncol"},{"key":"1920_CR12","doi-asserted-by":"crossref","unstructured":"Pisani P, Airoldi M, Allais A, Aluffi Valletti P, Battista M, Benazzo M, Briatore R, Cacciola S, Cocuzza S, Colombo A, Conti B, Costanzo A, Della Vecchia L, Denaro N, Fantozzi C, Galizia D, Garzaro M, Genta I, Iasi GA, Krengli M, Landolfo V, Lanza GV, Magnano M, Mancuso M, Maroldi R, Masini L, Merlano MC, Piemonte M, Pisani S, Prina-Mello A, Prioglio L, Rugiu MG, Scasso F, Serra A, Valente G, Zannetti M, Zigliani A (2020) Metastatic disease in head & neck oncology, Acta otorhinolaryngologica Italica: organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale. 40:S1\u2013s86","DOI":"10.14639\/0392-100X-suppl.1-40-2020"},{"key":"1920_CR13","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.3174\/ajnr.A6066","volume":"40","author":"MS Chung","year":"2019","unstructured":"Chung MS, Choi YJ, Kim SO, Lee YS, Hong JY, Lee JH, Baek JH (2019) A scoring system for prediction of cervical lymph node metastasis in patients with head and neck squamous cell carcinoma. AJNR Am J Neuroradiol 40:1049\u20131054","journal-title":"AJNR Am J Neuroradiol"},{"key":"1920_CR14","doi-asserted-by":"publisher","first-page":"685104","DOI":"10.3389\/fgene.2021.685104","volume":"12","author":"Y Shen","year":"2021","unstructured":"Shen Y, Li L, Lu Y, Zhang M, Huang X, Tang X (2021) Establishment and validation of a comprehensive prognostic model for patients with hnscc metastasis. Front Genet 12:685104","journal-title":"Front Genet"},{"key":"1920_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2016.10.007","volume":"138","author":"XW Gao","year":"2017","unstructured":"Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed 138:49\u201356","journal-title":"Comput Methods Programs Biomed"},{"key":"1920_CR16","doi-asserted-by":"publisher","first-page":"105081","DOI":"10.1016\/j.cmpb.2019.105081","volume":"183","author":"R Jakubicek","year":"2020","unstructured":"Jakubicek R, Chmelik J, Jan J, Ourednicek P, Lambert L, Gavelli G (2020) Learning-based vertebra localization and labeling in 3D CT data of possibly incomplete and pathological spines. Comput Methods Programs Biomed 183:105081","journal-title":"Comput Methods Programs Biomed"},{"key":"1920_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s40644-016-0080-6","volume":"16","author":"AD King","year":"2016","unstructured":"King AD, Thoeny HC (2016) Functional MRI for the prediction of treatment response in head and neck squamous cell carcinoma: potential and limitations. Cancer imaging: official publication Int Cancer Imaging Soc 16:23","journal-title":"Cancer imaging: official publication Int Cancer Imaging Soc"},{"key":"1920_CR18","doi-asserted-by":"publisher","first-page":"105242","DOI":"10.1016\/j.cmpb.2019.105242","volume":"187","author":"MA Ebrahimighahnavieh","year":"2020","unstructured":"Ebrahimighahnavieh MA, Luo S, Chiong R (2020) Deep learning to detect Alzheimer\u2019s disease from neuroimaging: a systematic literature review. Comput Methods Programs Biomed 187:105242","journal-title":"Comput Methods Programs Biomed"},{"key":"1920_CR19","first-page":"1","volume-title":"Value of PET imaging for radiation therapy","author":"C Lapa","year":"2021","unstructured":"Lapa C, Nestle U, Albert NL, Baues C, Beer A, Buck A, Budach V, B\u00fctof R, Combs SE, Derlin T, Eiber M, Fendler WP, Furth C, Gani C, Gkika E, Grosu AL, Henkenberens C, Ilhan H, L\u00f6ck S, Marnitz-Schulze S, Miederer M, Mix M, Nicolay NH, Niyazi M, P\u00f6ttgen C, R\u00f6del CM, Schatka I, Schwarzenboeck SM, Todica AS, Weber W, Wegen S, Wiegel T, Zamboglou C, Zips D, Z\u00f6phel K, Zschaeck S, Thorwarth D, Troost EGC (2021) Value of PET imaging for radiation therapy, vol 197. Organ der Deutschen Rontgengesellschaft \u2026 et al], Strahlentherapie und Onkologie, pp 1\u201323"},{"key":"1920_CR20","doi-asserted-by":"publisher","first-page":"e25496","DOI":"10.1097\/MD.0000000000025496","volume":"100","author":"H Jiang","year":"2021","unstructured":"Jiang H, Tan Q, He F, Yang W, Liu J, Zhou F, Zhang M (2021) Ultrasound in patients with treated head and neck carcinomas: a retrospective analysis for effectiveness of follow-up care. Medicine 100:e25496","journal-title":"Medicine"},{"key":"1920_CR21","doi-asserted-by":"publisher","first-page":"106629","DOI":"10.1016\/j.cmpb.2022.106629","volume":"215","author":"HR Torres","year":"2022","unstructured":"Torres HR, Morais P, Oliveira B, Birdir C, R\u00fcdiger M, Fonseca JC, Vila\u00e7a JL (2022) A review of image processing methods for fetal head and brain analysis in ultrasound images. Comput Methods Programs Biomed 215:106629","journal-title":"Comput Methods Programs Biomed"},{"key":"1920_CR22","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.critrevonc.2016.10.009","volume":"108","author":"J Castelli","year":"2016","unstructured":"Castelli J, De Bari B, Depeursinge A, Simon A, Devillers A, Jimenez GR, Prior J, Ozsahin M, De Crevoisier R, Bourhis J (2016) Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy. Crit Rev Oncol\/Hematol 108:40\u201351","journal-title":"Crit Rev Oncol\/Hematol"},{"issue":"1","key":"1920_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.54963\/entu.v15i1.871","volume":"15","author":"T Majeed","year":"2025","unstructured":"Majeed T, Assad A (2025) Advances in deep learning for head and neck cancer: datasets and applied methods. ENT Updates 15(1):1\u201326","journal-title":"ENT Updates"},{"key":"1920_CR24","first-page":"703","volume":"16","author":"K Bera","year":"2019","unstructured":"Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A (2019) Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology, Nature reviews. Clin Oncol 16:703\u2013715","journal-title":"Clin Oncol"},{"key":"1920_CR25","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1038\/s43018-020-0034-6","volume":"1","author":"O Troyanskaya","year":"2020","unstructured":"Troyanskaya O, Trajanoski Z, Carpenter A, Thrun S, Razavian N, Oliver N (2020) Artificial intelligence and cancer. Nat cancer 1:149\u2013152","journal-title":"Nat cancer"},{"key":"1920_CR26","doi-asserted-by":"publisher","first-page":"15600","DOI":"10.1038\/s41598-022-19278-2","volume":"12","author":"Z Hameed","year":"2022","unstructured":"Hameed Z, Garcia-Zapirain B, Aguirre JJ, Isaza-Ruget MA (2022) Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Sci Rep 12:15600","journal-title":"Sci Rep"},{"key":"1920_CR27","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.3390\/app10082929","volume":"10","author":"I Kandel","year":"2020","unstructured":"Kandel I, Castelli M (2020) A novel architecture to classify histopathology images using convolutional neural networks. Appl Sci 10:2929","journal-title":"Appl Sci"},{"key":"1920_CR28","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41379-021-00919-2","volume":"35","author":"V Baxi","year":"2022","unstructured":"Baxi V, Edwards R, Montalto M, Saha S (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 35:23\u201332","journal-title":"Mod Pathol"},{"key":"1920_CR29","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32:605\u2013617","journal-title":"J Digit Imaging"},{"key":"1920_CR30","doi-asserted-by":"publisher","first-page":"18802","DOI":"10.1038\/s41598-020-75708-z","volume":"10","author":"A Levy-Jurgenson","year":"2020","unstructured":"Levy-Jurgenson A, Tekpli X, Kristensen VN, Yakhini Z (2020) Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 10:18802","journal-title":"Sci Rep"},{"key":"1920_CR31","doi-asserted-by":"publisher","first-page":"105531","DOI":"10.1016\/j.cmpb.2020.105531","volume":"194","author":"K George","year":"2020","unstructured":"George K, Sankaran P, K PJ (2020) Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features. Comput Methods Programs Biomed 194:105531","journal-title":"Comput Methods Programs Biomed"},{"key":"1920_CR32","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/s10120-021-01158-9","volume":"24","author":"Y Hu","year":"2021","unstructured":"Hu Y, Su F, Dong K, Wang X, Zhao X, Jiang Y, Li J, Ji J, Sun Y (2021) Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric cancer: official J Int Gastric Cancer Association Japanese Gastric Cancer Association 24:868\u2013877","journal-title":"Gastric cancer: official J Int Gastric Cancer Association Japanese Gastric Cancer Association"},{"key":"1920_CR33","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1038\/s41467-021-21674-7","volume":"12","author":"X Wang","year":"2021","unstructured":"Wang X, Chen Y, Gao Y, Zhang H, Guan Z, Dong Z, Zheng Y, Jiang J, Yang H, Wang L, Huang X, Ai L, Yu W, Li H, Dong C, Zhou Z, Liu X, Yu G (2021) Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun 12:1637","journal-title":"Nat Commun"},{"key":"1920_CR34","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1038\/s41379-021-00838-2","volume":"34","author":"WY Chuang","year":"2021","unstructured":"Chuang WY, Chen CC, Yu WH, Yeh CJ, Chang SH, Ueng SH, Wang TH, Hsueh C, Kuo CF, Yeh CY (2021) Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images. Mod Pathol 34:1901\u20131911","journal-title":"Mod Pathol"},{"key":"1920_CR35","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1109\/TPAMI.2020.3019563","volume":"44","author":"H Pinckaers","year":"2022","unstructured":"Pinckaers H, van Ginneken B, Litjens G (2022) Streaming convolutional neural networks for end-to-end learning with multi-megapixel images. IEEE Trans Pattern Anal Mach Intell 44:1581\u20131590","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1920_CR36","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.1109\/TMI.2021.3066295","volume":"40","author":"H Pinckaers","year":"2021","unstructured":"Pinckaers H, Bulten W, van der Laak J, Litjens G (2021) Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels. IEEE Trans Med Imaging 40:1817\u20131826","journal-title":"IEEE Trans Med Imaging"},{"key":"1920_CR37","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1002\/lio2.742","volume":"7","author":"H Tang","year":"2022","unstructured":"Tang H, Li G, Liu C et al (2022) Diagnosis of lymph node metastasis in head and neck squamous cell carcinomausing deep learning. Laryngoscope Investig Otolaryngol 7:161\u2013169","journal-title":"Laryngoscope Investig Otolaryngol"},{"key":"1920_CR38","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1920_CR39","doi-asserted-by":"publisher","first-page":"3279","DOI":"10.1109\/TKDE.2021.3126456","volume":"35","author":"G Brauwers","year":"2021","unstructured":"Brauwers G, Frasincar F (2021) A general survey on attention mechanisms in deep learning. IEEE Trans Knowl Data Eng 35:3279\u20133298","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1920_CR40","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Xu T-X, Liu J-J, Liu Z-N, Jiang P-T, Mu T-J, Zhang S-H, Martin RR, Cheng M-M, Hu S-M (2022) Attention mechanisms in computer vision: a survey. Comput visual media 8:331\u2013368","journal-title":"Comput visual media"},{"key":"1920_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J big Data 8:1\u201374","journal-title":"J big Data"},{"key":"1920_CR42","unstructured":"Xie Y, Yang B, Guan Q, Zhang J, Wu Q, Xia Y (2023) Attention mechanisms in medical image segmentation: a survey, arXiv preprint arXiv:2305.17937"},{"key":"1920_CR43","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"B van Ehteshami Bejnordi","year":"2017","unstructured":"Ehteshami Bejnordi B, Veta M, van Diest PJ, van Ginneken B, Karssemeijer N, Litjens G, van der Laak J, Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q, Li Q, Chen H, Lin HJ, Heng PA, Ha\u00df C, Bruni E, Wong Q, Halici U, \u00d6ner M, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Ahmady Phoulady H, Kovalev V, Kalinovsky A, Liauchuk V, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Ven\u00e2ncio R (2017) Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318:2199\u20132210","journal-title":"JAMA"},{"key":"1920_CR44","unstructured":"Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv :160605718"},{"key":"1920_CR45","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.2991\/ijcis.d.210301.002","volume":"14","author":"H Elmannai","year":"2021","unstructured":"Elmannai H, Hamdi M, AlGarni A (2021) Deep learning models combining for breast cancer histopathology image classification. Int J Comput Intell Syst 14:1003","journal-title":"Int J Comput Intell Syst"},{"issue":"1","key":"1920_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.54963\/entu.v15i1.871","volume":"15","author":"T Majeed","year":"2025","unstructured":"Majeed T, Assad A (2025) Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods. ENT Updates 15(1):1\u201326","journal-title":"ENT Updates"},{"issue":"4","key":"1920_CR47","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen RJ, Lu MY, Wang J, Williamson DF, Rodig SJ, Lindeman NI, Mahmood F (2020) Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans Med Imaging 41(4):757\u2013770","journal-title":"IEEE Trans Med Imaging"},{"key":"1920_CR48","doi-asserted-by":"publisher","first-page":"e1008349","DOI":"10.1371\/journal.pcbi.1008349","volume":"16","author":"M Mu\u00f1oz-Aguirre","year":"2020","unstructured":"Mu\u00f1oz-Aguirre M, Ntasis VF, Rojas S, Guig\u00f3 R (2020) PyHIST: a histological image segmentation tool. PLoS Comput Biol 16:e1008349","journal-title":"PLoS Comput Biol"},{"key":"1920_CR49","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/2153-3539.119005","volume":"4","author":"A Goode","year":"2013","unstructured":"Goode A, Gilbert B, Harkes J, Jukic D, Satyanarayanan M (2013) OpenSlide: a vendor-neutral software foundation for digital pathology. J Pathol Inf 4:27","journal-title":"J Pathol Inf"},{"key":"1920_CR50","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1504\/IJWMC.2016.077215","volume":"10","author":"L-L Cao","year":"2016","unstructured":"Cao L-L, Ding S, Fu X-W, Chen L (2016) Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm. Int J Wireless Mobile Comput 10:272\u2013277","journal-title":"Int J Wireless Mobile Comput"},{"key":"1920_CR51","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graph Appl 21:34\u201341","journal-title":"IEEE Comput Graph Appl"},{"key":"1920_CR52","doi-asserted-by":"crossref","unstructured":"Lakshmanan B, Anand S, Jenitha T (2019) Stain removal through color normalization of haematoxylin and eosin images: a review. In: Journal of physics: conference series, IOP Publishing, p. 012108","DOI":"10.1088\/1742-6596\/1362\/1\/012108"},{"key":"1920_CR53","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1038\/s41698-021-00225-9","volume":"5","author":"H Qu","year":"2021","unstructured":"Qu H, Zhou M, Yan Z, Wang H, Rustgi VK, Zhang S, Gevaert O, Metaxas DN (2021) Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. NPJ precision Oncol 5:87","journal-title":"NPJ precision Oncol"},{"key":"1920_CR54","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1920_CR55","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211\u2013252","journal-title":"Int J Comput Vision"},{"key":"1920_CR56","unstructured":"Martin TA, Ye L, Sanders AJ, Lane J, Jiang WG (2013) Cancer invasion and metastasis: molecular and cellular perspective. Madame Curie Bioscience Database. Landes Bioscience"},{"key":"1920_CR57","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1038\/s41598-022-07038-1","volume":"12","author":"M Takamatsu","year":"2022","unstructured":"Takamatsu M, Yamamoto N, Kawachi H, Nakano K, Saito S, Fukunaga Y, Takeuchi K (2022) Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence. Sci Rep 12:2963","journal-title":"Sci Rep"},{"key":"1920_CR58","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.ejca.2021.08.039","volume":"157","author":"L Kiehl","year":"2021","unstructured":"Kiehl L, Kuntz S, H\u00f6hn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A (2021) Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer 157:464\u2013473","journal-title":"Eur J Cancer"},{"key":"1920_CR59","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1111\/bju.15386","volume":"128","author":"F Wessels","year":"2021","unstructured":"Wessels F, Schmitt M, Krieghoff-Henning E, Jutzi T, Worst TS, Waldbillig F, Neuberger M, Maron RC, Steeg M, Gaiser T (2021) Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int 128:352\u2013360","journal-title":"BJU Int"},{"key":"1920_CR60","doi-asserted-by":"crossref","unstructured":"Zhao Y, Yang F, Fang Y, Liu H, Zhou N, Zhang J, Sun J, Yang S, Menze B, Fan X (2020) Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 4837\u20134846","DOI":"10.1109\/CVPR42600.2020.00489"},{"key":"1920_CR61","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.csbj.2021.12.028","volume":"20","author":"J Yang","year":"2022","unstructured":"Yang J, Ju J, Guo L, Ji B, Shi S, Yang Z, Gao S, Yuan X, Tian G, Liang Y (2022) Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning. Comput Struct Biotechnol J 20:333\u2013342","journal-title":"Comput Struct Biotechnol J"},{"key":"1920_CR62","doi-asserted-by":"publisher","first-page":"113335","DOI":"10.1016\/j.ejca.2023.113335","volume":"194","author":"HS Muti","year":"2023","unstructured":"Muti HS, R\u00f6cken C, Behrens H-M, L\u00f6ffler CM, Reitsam NG, Grosser B, M\u00e4rkl B, Stange DE, Jiang X, Veldhuizen GP (2023) Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study. Eur J Cancer 194:113335","journal-title":"Eur J Cancer"},{"key":"1920_CR63","doi-asserted-by":"crossref","unstructured":"Ahanger AB, Aalam SW, Assad A, Macha MA, Bhat MR (2024) Alzhinet: an explainable self-attention based classification model to detect alzheimer from 3d volumetric MRI data. Int J Syst Assur Eng Manage, pp. 1\u201310","DOI":"10.1007\/s13198-024-02377-w"}],"container-title":["Head and Neck Pathology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12105-026-01920-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12105-026-01920-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12105-026-01920-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:03:05Z","timestamp":1780300985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12105-026-01920-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1920"],"URL":"https:\/\/doi.org\/10.1007\/s12105-026-01920-4","relation":{},"ISSN":["1936-0568"],"issn-type":[{"value":"1936-0568","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]},"assertion":[{"value":"9 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study utilized publicly available, de-identified data from The Cancer Genome Atlas (TCGA) and therefore did not require ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"All participants provided consent for publication of anonymized data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"59"}}