Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.
Google Scholar
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48.
Google Scholar
Ajani JA, D’Amico TA, Bentrem DJ, Chao J, Cooke D, Corvera C, et al. Gastric cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022;20:167–92.
Google Scholar
Shi RL, Chen Q, Ding JB, Yang Z, Pan G, Jiang D, et al. Increased number of negative lymph nodes is associated with improved survival outcome in node positive gastric cancer following radical gastrectomy. Oncotarget. 2016;7:35084–91.
Google Scholar
Sano T, Coit DG, Kim HH, Roviello F, Kassab P, Wittekind C, et al. Proposal of a new stage grouping of gastric cancer for TNM classification: International Gastric Cancer Association staging project. Gastric Cancer. 2017;20:217–25.
Google Scholar
de Burlet KJ, van den Hout MFCM, Putter H, Smit VTHBM, Hartgrink HH. Total number of lymph nodes in oncologic resections, is there more to be found? J Gastrointest Surg. 2015;19:943–8.
Google Scholar
Seeruttun SR, Xu L, Wang F, Yi X, Fang C, Liu Z, et al. A homogenized approach to classify advanced gastric cancer patients with limited and adequate number of pathologically examined lymph nodes. Cancer Commun (Lond). 2019;39:32.
Google Scholar
Arigami T, Uenosono Y, Yanagita S, Nakajo A, Ishigami S, Okumura H, et al. Clinical significance of lymph node micrometastasis in gastric cancer. Ann Surg Oncol. 2013;20:515–21.
Google Scholar
Lee CM, Cho JM, Jang YJ, Park SS, Park SH, Kim SJ, et al. Should lymph node micrometastasis be considered in node staging for gastric cancer? the significance of lymph node micrometastasis in gastric cancer. Ann Surg Oncol. 2015;22:765–71.
Google Scholar
Sekiguchi M, Oda I, Taniguchi H, Suzuki H, Morita S, Fukagawa T, et al. Risk stratification and predictive risk-scoring model for lymph node metastasis in early gastric cancer. J Gastroenterol. 2016;51:961–70.
Google Scholar
Li Y, Wang D, Li Y, Liu X, Chen D, Yuan C, et al. Clinical significance of lymph node micrometastasis in pN0 gastric cancer patients. Gastroenterol Res Pract. 2021;2021:6854646.
Google Scholar
Wang X, Yang X, Cai F, Cai M, Liu Y, Zhang L, et al. The key role of tumor budding in predicting the status of lymph node involvement in early gastric cancer patients: a clinical multicenter validation in China. Ann Surg Oncol. 2024;31:4224–35.
Google Scholar
Kim JY, Kim CH, Lee Y, Lee JH, Chae YS. Tumour infiltrating lymphocytes are predictors of lymph node metastasis in early gastric cancers. Pathology. 2017;49:589–95.
Google Scholar
Chen D, Chen G, Jiang W, Fu M, Liu W, Sui J, et al. Association of the collagen signature in the tumor microenvironment with lymph node metastasis in early gastric cancer. JAMA Surg. 2019;154:e185249.
Google Scholar
Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25:1054–6.
Google Scholar
Huang B, Tian S, Zhan N, Ma J, Huang Z, Zhang C, et al. Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: a retrospective multicentre study. EBiomedicine. 2021;73:103631.
Google Scholar
Zheng X, Wang R, Zhang X, Sun Y, Zhang H, Zhao Z, et al. A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology. Nat Commun. 2022;13:2790.
Google Scholar
Flinner N, Gretser S, Quaas A, Bankov K, Stoll A, Heckmann LE, et al. Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma. J Pathol. 2022;257:218–26.
Google Scholar
Wei Z, Zhao X, Chen J, Sun Q, Wang Z, Wang Y, et al. Deep learning-based stratification of gastric cancer patients from hematoxylin and eosin-stained whole slide images by predicting molecular features for immunotherapy response. Am J Pathol. 2023;193:1517–27.
Google Scholar
Brockmoeller S, Echle A, Ghaffari Laleh N, Eiholm S, Malmstrøm ML, Plato Kuhlmann T, et al. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J Pathol. 2022;256:269–81.
Google Scholar
Krogue JD, Azizi S, Tan F, Flament-Auvigne I, Brown T, Plass M, et al. Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. Commun Med (Lond). 2023;3:59.
Google Scholar
Zheng Q, Jian J, Wang J, Wang K, Fan J, Xu H, et al. Predicting lymph node metastasis status from primary muscle-invasive bladder cancer histology slides using deep learning: a retrospective multicenter study. Cancers (Basel). 2023;15:3000.
Google Scholar
Guo Q, Qu L, Zhu J, Li H, Wu Y, Wang S, et al. Predicting lymph node metastasis from primary cervical squamous cell carcinoma based on deep learning in histopathologic images. Mod Pathol. 2023;36:100316.
Google Scholar
Chen S, Xiang J, Wang X, Zhang J, Yang S, Yang W, et al. Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types. Br J Cancer. 2023;129:46–53.
Google Scholar
Gao F, Jiang L, Guo T, Lin J, Xu W, Yuan L, et al. Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images. J Transl Med. 2024;22:568.
Google Scholar
Muti HS, Röcken C, Behrens HM, Löffler CML, Reitsam NG, Grosser B, et al. Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study. Eur J Cancer. 2023;194:113335.
Google Scholar
Guo Z, Lan J, Wang J, Hu Z, Wu Z, Quan J, et al. Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning. Biomed Signal Process Control. 2023;86:105336.
Google Scholar
Zeng YJ, Zhang CD, Dai DQ. Impact of lymph node micrometastasis on gastric carcinoma prognosis: a meta-analysis. World J Gastroenterol. 2015;21:1628–35.
Google Scholar
Mpallas KD, Lagopoulos VI, Kamparoudis AG. Prognostic significance of solitary lymphnode metastasis and micrometastasis in gastric cancer. Front Surg. 2018;5:63.
Google Scholar
Tavares A, Wen X, Maciel J, Carneiro F, Dinis-Ribeiro M. Occult tumour cells in lymph nodes from gastric cancer patients: should isolated tumour cells also be considered?. Ann Surg Oncol. 2020;27:4204–15.
Google Scholar
Wang X, Chen Y, Gao Y, Zhang H, Guan Z, Dong Z, et al. Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun. 2021;12:1637.
Google Scholar
Huang SC, Chen CC, Lan J, Hsieh TY, Chuang HC, Chien MY, et al. Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings. Nat Commun. 2022;13:3347.
Google Scholar
WHO Classification of Tumours Editorial Board. WHO classification of tumours: digestive system tumours. 5th ed. Lyon: International Agency for Research on Cancer; 2019.
Amin MB, Edge SB, Greene FL (ed.). AJCC Cancer Staging Manual. 8th ed (Springer, New York, 2017).
Jiang Y, Liang X, Han Z, Wang W, Xi S, Li T, et al. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study. Lancet Digit Health. 2021;3:e371–82.
Google Scholar
Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng. 2021;5:555–70.
Google Scholar
Kang M, Song H, Park S, Yoo D, Pereira S. Benchmarking self-supervised learning on diverse pathology datasets, (2023). http://arxiv.org/abs/2212.04690.
Agnes A, Biondi A, Cananzi FM, Rausei S, Reddavid R, Laterza V, et al. Ratio-based staging systems are better than the 7th and 8th editions of the TNM in stratifying the prognosis of gastric cancer patients: a multicenter retrospective study. J Surg Oncol. 2019;119:948–57.
Google Scholar
Wang W, Yang YJ, Zhang RH, Deng JY, Sun Z, Seeruttun SR, et al. Standardizing the classification of gastric cancer patients with limited and adequate number of retrieved lymph nodes: an externally validated approach using real-world data. Mil Med Res. 2022;9:15.
Google Scholar
Zhou Y, Zhang GJ, Wang J, Zheng KY, Fu W. Current status of lymph node micrometastasis in gastric cancer. Oncotarget. 2017;8:51963–9.
Google Scholar
Nakajo A, Natsugoe S, Ishigami S, Matsumoto M, Nakashima S, Hokita S, et al. Detection and prediction of micrometastasis in the lymph nodes of patients with pN0 gastric cancer. Ann Surg Oncol. 2001;8:158–62.
Google Scholar
Fukagawa T, Sasako M, Mann GB, Sano T, Katai H, Maruyama K, et al. Immunohistochemically detected micrometastases of the lymph nodes in patients with gastric carcinoma. Cancer. 2001;92:753–60.
Google Scholar
Jeuck TLA, Wittekind C. Gastric carcinoma: stage migration by immunohistochemically detected lymph node micrometastases. Gastric Cancer. 2015;18:100–8.
Google Scholar
Tavares A, Monteiro-Soares M, Viveiros F, Maciel Barbosa J, Dinis-Ribeiro M. Occult tumor cells in lymph nodes of patients with gastric cancer: a systematic review on their prevalence and predictive role. Oncology. 2015;89:245–54.
Google Scholar
Wei T, Yuan X, Gao R, Johnston L, Zhou J, Wang Y, et al. Survival prediction of stomach cancer using expression data and deep learning models with histopathological images. Cancer Sci. 2023;114:690–701.
Google Scholar
Veldhuizen GP, Röcken C, Behrens HM, Cifci D, Muti HS, Yoshikawa T, et al. Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study. Gastric Cancer. 2023;26:708–20.
Google Scholar
Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBiomedicine. 2020;61:103054.
Google Scholar
Bokhorst JM, Ciompi F, Öztürk SK, Oguz Erdogan AS, Vieth M, Dawson H, et al. Fully automated tumor bud assessment in hematoxylin and eosin-stained whole slide images of colorectal cancer. Mod Pathol. 2023;36:100233.
Google Scholar
