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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.
Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T. Kather JN, et al. Nat Med. 2019 Jul;25(7):1054-1056. doi: 10.1038/s41591-019-0462-y. Epub 2019 Jun 3. Nat Med. 2019. PMID: 31160815 Free PMC article.
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohisto
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well t
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.
Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, Higgins J, Rubin DL, Shen J. Yamashita R, et al. Lancet Oncol. 2021 Jan;22(1):132-141. doi: 10.1016/S1470-2045(20)30535-0. Lancet Oncol. 2021. PMID: 33387492
A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential of a deep learning-based system for automated MSI prediction directly from haematoxylin and eosin (H&E)-sta …
A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential …
xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer.
Bustos A, Payá A, Torrubia A, Jover R, Llor X, Bessa X, Castells A, Carracedo Á, Alenda C. Bustos A, et al. Biomolecules. 2021 Nov 29;11(12):1786. doi: 10.3390/biom11121786. Biomolecules. 2021. PMID: 34944430 Free PMC article.
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. ...Methods to not only palliat …
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have signifi …
Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.
Leiby JS, Hao J, Kang GH, Park JW, Kim D. Leiby JS, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3068-3071. doi: 10.1109/EMBC48229.2022.9871553. Annu Int Conf IEEE Eng Med Biol Soc. 2022. PMID: 36085965
Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. ...Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep
Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. ...Making use of both w
Rapid Screening Using Pathomorphologic Interpretation to Detect BRAFV600E Mutation and Microsatellite Instability in Colorectal Cancer.
Fujii S, Kotani D, Hattori M, Nishihara M, Shikanai T, Hashimoto J, Hama Y, Nishino T, Suzuki M, Yoshidumi A, Ueno M, Komatsu Y, Masuishi T, Hara H, Esaki T, Nakamura Y, Bando H, Yamada T, Yoshino T. Fujii S, et al. Clin Cancer Res. 2022 Jun 13;28(12):2623-2632. doi: 10.1158/1078-0432.CCR-21-4391. Clin Cancer Res. 2022. PMID: 35363302
PURPOSE: Rapid decision-making is essential in precision medicine for initiating molecular targeted therapy for patients with cancer. This study aimed to extract pathomorphologic features that enable the accurate prediction of genetic abnormalities in cancer
PURPOSE: Rapid decision-making is essential in precision medicine for initiating molecular targeted therapy for patients with cancer. …
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer.
Schirris Y, Gavves E, Nederlof I, Horlings HM, Teuwen J. Schirris Y, et al. Med Image Anal. 2022 Jul;79:102464. doi: 10.1016/j.media.2022.102464. Epub 2022 Apr 29. Med Image Anal. 2022. PMID: 35596966
We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneit …
We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and …
Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.
Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Schrammen PL, et al. J Pathol. 2022 Jan;256(1):50-60. doi: 10.1002/path.5800. Epub 2021 Oct 22. J Pathol. 2022. PMID: 34561876 Free article.
Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. ...To address these issues, we present a new method for simultaneous tumor detection a
Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting gene