Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 1026–1038 (2022).Article
PubMed
Google Scholar
Ghaffari Laleh, N. et al. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal. 79, 102474 (2022).Article
PubMed
Google Scholar
Foersch, S. et al. Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann. Oncol. 32, 1178–1187 (2021).Article
CAS
PubMed
Google Scholar
Klein, C. et al. Artificial intelligence for solid tumour diagnosis in digital pathology. Br. J. Pharmacol. 178, 4291–4315 (2021).Article
CAS
PubMed
Google Scholar
Woerl, A.-C. et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur. Urol. 78, 256–264 (2020).Article
CAS
PubMed
Google Scholar
Hong, R., Liu, W., DeLair, D., Razavian, N. & Fenyö, D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep. Med. 2, 100400 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16, e1002730 (2019).Article
PubMed
PubMed Central
Google Scholar
Ghaffari Laleh, N. et al. Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology. Proceedings of the MICCAI Workshop on Computational Pathology. PMLR 156, 81–93 (2021).Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med. 29, 430–439 (2023).Article
CAS
PubMed
Google Scholar
Wang, C.-W. et al. Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. Comput. Med. Imaging Graph. 99, 102093 (2022).Article
PubMed
Google Scholar
Ghaffari Laleh, N., Ligero, M., Perez-Lopez, R. & Kather, J. N. Facts and hopes on the use of artificial intelligence for predictive immunotherapy biomarkers in cancer. Clin. Cancer Res. 29, 316–323 (2023).Article
PubMed
Google Scholar
Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Kanavati, F. et al. Weakly-supervised learning for lung carcinoma classification using deep learning. Sci. Rep. 10, 9297 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Wang, X. et al. Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans. Cybern. 50, 3950–3962 (2020).Article
PubMed
Google Scholar
Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Bilal, M. et al. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. Health 3, e763–e772 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Schrammen, P. L. et al. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol. 256, 50–60 (2022).Article
CAS
PubMed
Google Scholar
Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406–1416.e11 (2020).Article
CAS
PubMed
Google Scholar
Zeng, Q. et al. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J. Hepatol. 77, 116–127 (2022).Article
CAS
PubMed
Google Scholar
Jaroensri, R. et al. Deep learning models for histologic grading of breast cancer and association with disease prognosis. NPJ Breast Cancer 8, 113 (2022).Article
PubMed
PubMed Central
Google Scholar
Li, C. et al. Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med. Image Anal. 53, 165–178 (2019).Article
PubMed
Google Scholar
Zheng, Q. et al. A weakly supervised deep learning model and human-machine fusion for accurate grading of renal cell carcinoma from histopathology slides. Cancers (Basel) 15, 3198 (2023).Article
PubMed
Google Scholar
Muti, H. S. et al. The Aachen Protocol for Deep Learning Histopathology: A Hands-on Guide for Data Preprocessing. Available at https://oa.mg/work/10.5281/zenodo.3694994 (2020).Graziani, M. et al. Attention-based interpretable regression of gene expression in histology. Interpretability of Machine Intelligence in Medical Image Computing: 5th International Workshop, iMIMIC 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings 44–60 (Springer-Verlag, 2022).Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Schmauch, B. et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat. Commun. 11, 1–15 (2020).Article
Google Scholar
Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).Article
CAS
PubMed
Google Scholar
Wagner, S. J. et al. Built to last? Reproducibility and reusability of deep learning algorithms in computational pathology. Mod. Pathol. 37, 100350 (2023).Article
PubMed
Google Scholar
Veldhuizen, G. P. et al. Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study. Gastric Cancer 26, 708–720 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Muti, H. S. et al. Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study. Eur. J. Cancer 194, 113335 (2023).Article
PubMed
Google Scholar
Saldanha, O. L. et al. Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning. Gastric Cancer 26, 264–274 (2023).Article
CAS
PubMed
Google Scholar
Niehues, J. M. et al. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: a retrospective multi-centric study. Cell Rep. Med. 4, 100980 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650–1661.e4 (2023).Jiang, X. et al. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit. Health 6, e33–e43 (2024).Article
CAS
PubMed
Google Scholar
Chatterji, S. et al. Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer. NPJ Breast Cancer 9, 91 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Hewitt, K. J. et al. Direct image to subtype prediction for brain tumors using deep learning. Neurooncol. Adv. 5, vdad139 (2023).PubMed
PubMed Central
Google Scholar
Saldanha, O. L. et al. Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. NPJ Precis. Oncol. 7, 35 (2023).Article
PubMed
PubMed Central
Google Scholar
Loeffler, C. M. L. et al. Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study. Preprint at https://www.medrxiv.org/content/10.1101/2023.03.08.23286975v1 (2023).El Nahhas, O. S. M. et al. Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat. Commun. 15, 1–253 (2024).
Google Scholar
Wang, X. et al. Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022).Article
PubMed
Google Scholar
Causality in digital medicine. Nat. Commun. 12, 5471 (2021).Wölflein, G. et al. Benchmarking pathology feature extractors for whole slide image classification. Preprint at https://arxiv.org/abs/2311.11772 (2023).Goode, A., Gilbert, B., Harkes, J., Jukic, D. & Satyanarayanan, M. OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013).Article
PubMed
PubMed Central
Google Scholar
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).Article
PubMed
PubMed Central
Google Scholar
Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750 (2021).Article
CAS
PubMed
Google Scholar
Paszke, A. et al. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (eds Wallach, H. et al.) Vol. 32 (Curran Associates, Inc., 2019).Jorge Cardoso, M. et al. MONAI: an open-source framework for deep learning in healthcare. Preprint at https://arxiv.org/abs/2211.02701 (2022).Martinez, K. & Cupitt, J. VIPS – a highly tuned image processing software architecture. In IEEE International Conference on Image Processing 2005. Genova, Italy II–574 (IEEE, 2005).Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M. & Madabhushi, A. HistoQC: an open-source quality control tool for digital pathology slides. JCO Clin. Cancer Inform. 3, 1–7 (2019).Article
PubMed
Google Scholar
Pedersen, A. et al. FastPathology: an open-source platform for deep learning-based research and decision support in digital pathology. IEEE Access 9, 58216–58229 (2021).Article
Google Scholar
Pocock, J. et al. TIAToolbox as an end-to-end library for advanced tissue image analytics. Commun. Med. (Lond.) 2, 120 (2022).Article
PubMed
Google Scholar
Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555–570 (2021).Article
PubMed
PubMed Central
Google Scholar
Verghese, G. et al. Computational pathology in cancer diagnosis, prognosis, and prediction—present day and prospects. J. Pathol. 260, 551–563 (2023).Article
PubMed
PubMed Central
Google Scholar
Saillard, C. et al. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nat. Commun. 14, 6695 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Greenson, J. K. et al. Pathologic predictors of microsatellite instability in colorectal cancer. Am. J. Surg. Pathol. 33, 126–133 (2009).Article
PubMed
PubMed Central
Google Scholar
Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).Ellis, M. J. et al. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov. 3, 1108–1112 (2013).Article
CAS
PubMed
PubMed Central
Google Scholar
Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1107–1110 (IEEE, 2009).Howard, F. M. et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12, 4423 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986).Article
CAS
PubMed
Google Scholar
Comes, M. C. et al. A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients. Sci. Rep. 12, 20366 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Jiang, S., Suriawinata, A. A. & Hassanpour, S. MHAttnSurv: multi-head attention for survival prediction using whole-slide pathology images. Comput. Biol. Med. 158, 106883 (2023).Article
PubMed
PubMed Central
Google Scholar
Sounderajah, V. et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 11, e047709 (2021).Article
PubMed
PubMed Central
Google Scholar
Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).Article
PubMed
PubMed Central
Google Scholar
Trautmann, K. et al. Chromosomal instability in microsatellite-unstable and stable colon cancer. Clin. Cancer Res. 12, 6379–6385 (2006).Article
CAS
PubMed
Google Scholar
Lin, E. I. et al. Mutational profiling of colorectal cancers with microsatellite instability. Oncotarget 6, 42334–42344 (2015).Article
PubMed
PubMed Central
Google Scholar
Boland, C. R. & Goel, A. Microsatellite instability in colorectal cancer. Gastroenterology 138, 2073–2087.e3 (2010).Article
CAS
PubMed
Google Scholar
Battaglin, F., Naseem, M., Lenz, H.-J. & Salem, M. E. Microsatellite instability in colorectal cancer: overview of its clinical significance and novel perspectives. Clin. Adv. Hematol. Oncol. 16, 735–745 (2018).PubMed
PubMed Central
Google Scholar
Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) 618–626 (IEEE, 2017).Pataki, B. Á. et al. HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening. Sci. Data 9, 370 (2022).Article
PubMed
PubMed Central
Google Scholar
Cheng, J. et al. Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma. Nat. Commun. 11, 1778 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar