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

Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.

Pathology oncology research : POR - 1 Jan 2026

Toth Bertalan, Mesterházi Marcell, Szabo Istvan, Mersich Tamas, Toth Erika, Szoke Janos, Benczur Andras, Kerepesi Csaba, Tarnoki David Laszlo, Tarnoki Adam Domonkos

Abstract excerpt

Background: Prediction of pathologic features and outcome in patients with rectal cancer is challenging as a result of lack of a significant biomarker and heterogeneity between and within tumors. This study aims to evaluate the potential of Magnetic Resonance Imaging (MRI)-based radiomics in predicting key pathological features and long-term survival outcomes in patients. Methods: A retrospective study was...

Read the complete abstract on PubMed

Topics

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

Share to Follow-Up Studies
Share to Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Magnetic Resonance Imaging

Share to Prognosis

Share to Proto-Oncogene Proteins p21(ras)

Share to Radiomics

Share to Rectal Neoplasms

Share to Survival Rate

Source

Open in PubMed Open full text

Identifiers

PMID: 42180157
DOI: 10.3389/pore.2026.1612303
PMCID: PMC13189980

Genes

KRAS

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.PMID 42180157Read ↗

MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.Semantic proximity Read ↗

Diagnostic Accuracy of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer: A Systematic Review and Meta-analysisSemantic proximity Read ↗

Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer.Semantic proximity Read ↗

Prognostic Value of Multiparametric MRI-Based Radiomics model: Potential Role for Chemotherapeutic Benefits in Locally Advanced Rectal CancerSemantic proximity Read ↗

Comprehensive Overview of Molecular, Imaging, and Therapeutic Challenges in Rectal Mucinous AdenocarcinomaSemantic proximity Read ↗

Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning.Semantic proximity Read ↗

Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer.Semantic proximity Read ↗

Different MRI-based radiomics machine learning models to predict CD3+ Tumor-infiltrating Lymphocytes (TILs) in rectal cancerSemantic proximity Read ↗

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal CancerSemantic proximity Read ↗

Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy PredictionSemantic proximity Read ↗

Personalized Risk Stratification in Colon Cancer using Radiomic-Based Predictive ModelsSemantic proximity Read ↗

Integrated radiomics and machine learning approach for ras mutation status prediction in colorectal liver metastases.Semantic proximity Read ↗

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