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Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer
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Toward More Accurate Understanding of Lymph Node Metastasis Risk in Early Gastric Cancer
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Prediction of Lymph Node Metastasis in Early Gastric Cancer by Collagen Signature—Endoscopists’ Viewpoint—Reply
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Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker
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Japanese gastric cancer treatment guidelines 2014 (ver. 4)
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Gastric cancer: Asia and the world
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Risk factors for lymph node metastasis in non-sentinel node basins in early gastric cancer: sentinel node concept
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Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer
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Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study
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The Lymph Node and the Metastasis
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Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
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A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer
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Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
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Focal Loss for Dense Object Detection
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Tumor metastasis to lymph nodes requires YAP-dependent metabolic adaptation
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Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study
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Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy
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Imaging Steps of Lymphatic Metastasis Reveals That Vascular Endothelial Growth Factor-C Increases Metastasis by Increasing Delivery of Cancer Cells to Lymph Nodes: Therapeutic Implications
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Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks
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Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
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Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Additional file 1 of Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study
inferred← citeshttps://doi.org/10.6084/m9.figshare.23707075openalex-snapshot
Additional file 2 of Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study
inferred← citeshttps://doi.org/10.6084/m9.figshare.23707078openalex-snapshot
Additional file 3 of Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study
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Additional file 4 of Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study
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Additional file 5 of Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study
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Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review
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