Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Technology in cancer research & treatment - 1 Jan 2000

Li Xiangyun, Yang Xiaoqun, Yang Xianwei, Xie Xin, Rui Wenbin, He Hongchao

Abstract excerpt

Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Carcinoma, Renal Cell

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Gene Ontology

Share to Kaplan-Meier Estimate

Share to Kidney Neoplasms

Share to Machine Learning

Share to Proportional Hazards Models

Source

Open in PubMed Open full text

Identifiers

PMID: 39703069
DOI: 10.1177/15330338241307686
PMCID: PMC11662324

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.PMID 39703069Read ↗

Clinical value and in vitro validation of gene and molecular subtype identification in renal clear cell carcinomaSemantic proximity Read ↗

A novel prognostic model based on immunogenomics for clear cell renal cell carcinoma.Semantic proximity Read ↗

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma PatientsSemantic proximity Read ↗

Identification of G protein-coupled receptor gene and immune infiltration analysis to construct a prognostic model for renal clear cell carcinoma patientsSemantic proximity Read ↗

Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinomaSemantic proximity Read ↗

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.Semantic proximity Read ↗

NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencingSemantic proximity Read ↗

Multi-omics features-based machine learning method improve immunotherapy response in clear cell renal cell carcinomaSemantic proximity Read ↗

Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinomaSemantic proximity Read ↗

Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinomaSemantic proximity Read ↗

Combining Single-Cell and Bulk RNA Sequencing Data to Create a Reliable Prognostic Model for Predicting Clear-cell Renal Cell Carcinoma ProgressionSemantic proximity Read ↗

Identification of a 5-Gene Signature Predicting Progression and Prognosis of Clear Cell Renal Cell CarcinomaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.