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

Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer.

Surgical oncology - 1 Dec 2023

Ricci Lara María Agustina, Esposito Marco Iván, Aineseder Martina, López Grove Roy, Cerini Matías Alejandro, Verzura María Alicia, Luna Daniel Roberto, Benítez Sonia Elizabeth, Spina Juan Carlos

Abstract excerpt

PURPOSE: Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images. METHODS: Clinical and follow-up information was collected from patients with metastatic...

Read the complete abstract on PubMed

Topics

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

Share to Colonic Neoplasms
Share to Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Proto-Oncogene Proteins p21(ras)

Share to Rectal Neoplasms

Source

Identifiers

PMID: 37729816
DOI: 10.1016/j.suronc.2023.101986

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer.PMID 37729816Read ↗

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.Semantic proximity Read ↗

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

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

CT-Based Radiomics to Predict the KRAS Mutation in CRC patients. A Retrospective StudySemantic proximity Read ↗

Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Semantic proximity Read ↗

Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.Semantic proximity Read ↗

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

Radiological and pathological integration predicts the efficacy of bevacizumab with chemotherapy in colorectal cancer liver metastasesSemantic proximity Read ↗

Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis.Semantic proximity Read ↗

Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic modelSemantic proximity Read ↗

Development and Validation of Radiomics Signatures to Predict KRAS Mutation Status Based on Triphasic Enhanced Computed Tomography in Patients with Colorectal CancerSemantic proximity Read ↗

First-line treatment efficacy and prognostic model in RAS-mutant metastatic colorectal cancer: a real-world study.Semantic proximity Read ↗

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