On the public citation graph

Deep Learning Radiopathomics Model for Predicting Vessels Encapsulating Tumor Clusters And Survival in Hepatocellular Carcinoma: A Multicenter Study

https://doi.org/10.2139/ssrn.4781315
0 signed · 25 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/hep.27760openalex-extracted
A novel vascular pattern promotes metastasis of hepatocellular carcinoma in an epithelial–mesenchymal transition–independent manner
inferredcites →https://doi.org/10.1002/hep.30366openalex-extracted
Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Predictor of Sorafenib Benefit in Patients with Hepatocellular Carcinoma
inferredcites →https://doi.org/10.1002/hep.30814openalex-extracted
Vessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma
inferredcites →https://doi.org/10.1002/jmri.28745openalex-extracted
Deep Learning Radiomics Model of Dynamic Contrast‐Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma
inferredcites →https://doi.org/10.1007/s00330-018-5787-2openalex-extracted
Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature
inferredcites →https://doi.org/10.1007/s12072-022-10323-wopenalex-extracted
Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images
inferredcites →https://doi.org/10.1016/j.bios.2019.03.003openalex-extracted
Specific and label-free immunosensing of protein-protein interactions with silicon-based immunoFETs
inferredcites →https://doi.org/10.1016/j.ebiom.2022.104364openalex-extracted
Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study
inferredcites →https://doi.org/10.1016/j.engappai.2023.107024openalex-extracted
Automatic borehole fracture detection and characterization with tailored Faster R-CNN and simplified Hough transform
inferredcites →https://doi.org/10.1016/j.optlastec.2023.109952openalex-extracted
Correction of spurious phase sign in single closed-fringe demodulation using transformer based Swin-ResUnet
inferredcites →https://doi.org/10.1016/j.patcog.2023.109432openalex-extracted
3D Medical image segmentation using parallel transformers
inferredcites →https://doi.org/10.1021/acsomega.1c00732openalex-extracted
Benefits of Ion Mobility Separation and Parallel Accumulation–Serial Fragmentation Technology on timsTOF Pro for the Needs of Fast Photochemical Oxidation of Protein Analysis
inferredcites →https://doi.org/10.1038/s41467-020-15027-zopenalex-extracted
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
inferredcites →https://doi.org/10.1038/s41467-020-15326-5openalex-extracted
Identification of cell surface markers and establishment of monolayer differentiation to retinal pigment epithelial cells
inferredcites →https://doi.org/10.1038/s41467-022-34703-wopenalex-extracted
Prognostic and predictive value of a pathomics signature in gastric cancer
inferredcites →https://doi.org/10.1148/radiol.2021203614openalex-extracted
Preoperative CT for Characterization of Aggressive Macrotrabecular-Massive Subtype and Vessels That Encapsulate Tumor Clusters Pattern in Hepatocellular Carcinoma
inferredcites →https://doi.org/10.1148/radiol.2021210902openalex-extracted
Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non–Small Cell Lung Cancer
inferredcites →https://doi.org/10.1148/radiol.222223openalex-extracted
An MRI Deep Learning Model Predicts Outcome in Rectal Cancer
inferredcites →https://doi.org/10.1159/000512239openalex-extracted
Efficacy and Safety of Lenvatinib Therapy for Unresectable Hepatocellular Carcinoma in a Real-World Practice in Korea
inferredcites →https://doi.org/10.1245/s10434-022-12000-6openalex-extracted
Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma
inferredcites →https://doi.org/10.1371/journal.pone.0206108openalex-extracted
Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC
inferredcites →https://doi.org/10.18240/ijo.2023.04.05openalex-extracted
4D label-free proteomic analysis of vitreous from patients with rhegmatogenous retinal detachment
inferredcites →https://doi.org/10.3389/fonc.2022.968202openalex-extracted
Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning
inferredcites →https://doi.org/10.3389/fonc.2022.986089openalex-extracted
Preoperative CT-based deep learning model for predicting overall survival in patients with high-grade serous ovarian cancer
inferredcites →https://doi.org/10.3390/cancers11060841openalex-extracted
Current Treatment Landscape for Advanced Hepatocellular Carcinoma: Patient Outcomes and the Impact on Quality of Life
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.