Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7–33.

Article  PubMed  Google Scholar 

Kawada K, Taketo MM. Significance and mechanism of lymph node metastasis in cancer progression. Cancer Res. 2011;71:1214–8.

Article  CAS  PubMed  Google Scholar 

Giuliano AE, Hunt KK, Ballman KV, Beitsch PD, Whitworth PW, et al. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. J Am Med Assoc. 2011;305:569–75.

Article  CAS  Google Scholar 

Holten-Rossing H, Talman MM, Jylling AMB, Laenkholm AV, Kristensson M, Vainer B. Application of automated image analysis reduces the workload of manual screening of sentinel lymph node biopsies in breast cancer. Histopathology. 2017;71:866–73.

Article  PubMed  Google Scholar 

Fujimoto N, He Y, D’Addio M, Tacconi C, Detmar M, Dieterich LC. Single-cell mapping reveals new markers and functions of lymphatic endothelial cells in lymph nodes. PLoS Biol. 2020;18:e3000704.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Núñez NG, Tosello Boari J, Ramos RN, Richer W, Cagnard N, Anderfuhren CD, et al. Tumor invasion in draining lymph nodes is associated with Treg accumulation in breast cancer patients. Nat Commun. 2020;11:3272.

Article  PubMed  PubMed Central  Google Scholar 

Nishiwada S, Sho M, Banwait JK, Yamamura K, Akahori T, Nakamura K, et al. A microRNA signature identifies pancreatic ductal adenocarcinoma patients at risk for lymph node metastases. Gastroenterology. 2020;159:562–74.

Article  CAS  PubMed  Google Scholar 

Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ. Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol. 2020;16:669–85.

Article  PubMed  PubMed Central  Google Scholar 

Yu F, Jung AW, Torne RV, Gonzalez S, Vhringer H, Shmatko A, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer. 2020;1:800–10.

Article  Google Scholar 

Wang X, Fang Y, Yang S, Zhu D, Wang M, Zhang J, et al. A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images. Med Image Anal. 2021;68:101914.

Article  PubMed  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.

Article  PubMed  PubMed Central  Google Scholar 

Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24:1559–67.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 2021;22:132–41.

Article  PubMed  Google Scholar 

Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594:106–10.

Article  CAS  PubMed  Google Scholar 

Hu Y, Su F, Dong K, Wang X, Zhao X, Jiang Y, et al. Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer. 2021;24:868–77.

Article  PubMed  Google Scholar 

Pham HHN, Futakuchi M, Bychkov A, Furukawa T, Kuroda K, Fukuoka J. Detection of lung cancer lymph node metastases from whole-slide histopathologic images using a two-step deep learning approach. Am J Pathol. 2019;189:2428–39.

Article  CAS  PubMed  Google Scholar 

Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. J Am Med Assoc. 2017;318:2199–210.

Article  Google Scholar 

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.

Article  PubMed  PubMed Central  Google Scholar 

Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.

Article  PubMed  PubMed Central  Google Scholar 

Huang KL, Mashl RJ, Wu Y, Ritter DI, Wang J, Oh C, et al. Pathogenic germline variants in 10,389 adult cancers. Cell. 2018;173:355–70.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ciga O, Xu T, Martel AL. Self supervised contrastive learning for digital histopathology. Mach Learn Appl. 2022;100198:1–14.

Google Scholar 

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: hierarchical vision transformer using shifted windows. arXiv [Preprint] 2021. Available from: https://doi.org/10.48550/arXiv.2103.14030.

Wang X, Yang S, Zhang J, Wang M, Zhang J, Yang W, et al. Transformer-based unsupervised contrastive learning for histopathological image classification. Med Image Anal. 2022;81:102559.

Article  PubMed  Google Scholar 

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

Article  CAS  PubMed  Google Scholar 

Shao Z, Bian H, Chen Y, Wang Y, Zhang J, Ji X, et al. TransMIL: transformer based correlated multiple instance learning for whole slide image classification. arXiv [Preprint] 2021. Available from: https://doi.org/10.48550/arXiv.2106.00908.

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

Article  PubMed  Google Scholar 

Lykke J, Roikjaer O, Jess P, Rosenberg J. Identification of risk factors associated with stage III disease in nonmetastatic colon cancer: results from a Prospective National Cohort Study. Ann Coloproctol. 2020;36:316–22.

Article  PubMed  PubMed Central  Google Scholar 

Hanna AN, Sinnamon AJ, Roses RE, Kelz RR, Elder DE, Xu X, et al. Relationship between age and likelihood of lymph node metastases in patients with intermediate thickness melanoma (1.01-4.00 mm): a National Cancer Database study. J Am Acad Dermatol. 2019;80:433–40.

Article  PubMed  Google Scholar 

Glimelius B, Tiret E, Cervantes A, Arnold D. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2013;24:vi81–8.

Article  PubMed  Google Scholar 

Senkus E, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rutgers E, et al. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26:v8–30.

Article  PubMed  Google Scholar 

Brinker TJ, Kiehl L, Schmitt M, Jutzi TB, Krieghoff-Henning EI, Krahl D, et al. Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. Eur J Cancer. 2021;154:227–34.

Article  PubMed  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.

Article  CAS  PubMed  Google Scholar 

Kwak MS, Lee HH, Yang JM, Cha JM, Jeon JW, Yoon JY, et al. Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images. Front Oncol. 2020;10:619803.

Article  PubMed  Google Scholar 

Zhao Y, Yang F, Fang Y, Liu H, Zhou N, Zhang J, et al. 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. 2020. p. 4837–46.

Wang L, Jiao Y, Qiao Y, Zeng N, Yu R. A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recognit Lett. 2020;135:244–8.

Article  Google Scholar