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

Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy.

International journal of radiation oncology, biology, physics - 1 Oct 2011

De Ruyck Kim, Sabbe Nick, Oberije Cary, Vandecasteele Katrien, Thas Olivier, De Ruysscher Dirk, Lambin Phillipe, Van Meerbeeck Jan, De Neve Wilfried, Thierens Hubert

Abstract excerpt

PURPOSE: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. PATIENTS AND METHODS: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes,...

Read the complete abstract on PubMed

Topics

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

Share to Area Under Curve
Share to CD18 Antigens

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Combined Modality Therapy

Share to Endoglin

Share to Esophagus

Share to False Negative Reactions

Share to Radiotherapy Dosage

Share to ROC Curve

Source

Identifiers

PMID: 21605946
DOI: 10.1016/j.ijrobp.2011.03.012

Genes

EGFR
ENG
ITGB2
TRAF3

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy.PMID 21605946Read ↗

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.Semantic proximity Read ↗

Development of a machine-learning model for therapeutic efficacy prediction of preoperative treatment for esophageal cancer using single nucleotide variants of autophagy-related genesSemantic proximity Read ↗

Advanced Ensemble Machine Learning Framework for Lung Cancer Prediction: A Comprehensive Multi-Algorithmic Approach with Sophisticated Feature Engineering and Clinical Decision Support IntegrationSemantic proximity Read ↗

A Machine Learning–Based Prediction Model for Dysphagia After Radiotherapy in Nasopharyngeal Carcinoma SurvivorsSemantic proximity Read ↗

Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicitySemantic proximity Read ↗

Development and validation of a machine learning-based model incorporating systemic inflammatory, nutritional, and coagulation parameters for predicting metastasis in lung cancerSemantic proximity Read ↗

A database for lung cancer prediction models - a Danish cohort study of nearly 40,000 patientsSemantic proximity Read ↗

Development and Validation of a Survival Prediction Model for Lung Squamous Cell CarcinomaSemantic proximity Read ↗

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signaturesSemantic proximity Read ↗

A novel nomogram and risk classification system for estimating survival in tracheal cancer patients treated with surgical interventionSemantic proximity Read ↗

Radiogenomics in lung cancer: Where are we?Semantic proximity Read ↗

Prognostic risk factors of T2, T3, and T4 thoracic esophageal cancer and their relationship with improved survival after radiotherapy: A Retrospective Cohort StudySemantic proximity Read ↗

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