Back to search

Article

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

2022-08-19

Abstract excerpt

<h4>Purpose: </h4> To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. <h4>Methods:</h4> 204 ESCC patients were randomly divided into training cohort (n=143) and validation cohort (n=61) according to the ratio of 7:3. Two radiomics models were constructed by features which were selected by LASSO Cox m...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
328ff132-4c9e-515a-8c4d-2e9c89bd00b2
DOI
10.21203/rs.3.rs-1964056/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signaturesDOI 10.21203/rs.3.rs-1964056/v1
CT-based Radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinomaSemantic proximity
Pretreatment PET/CT Radiomics Combined with Clinicopathologic Features for Recurrence-Free Survival Risk Stratification After Neoadjuvant Immunochemotherapy in Esophageal Squamous Cell Carcinoma: A Multicenter StudySemantic proximity
CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR PathwaySemantic proximity
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutationSemantic proximity
Integrated CT-derived radiomics for predicting ctDNA status and prognosis in resectable non-small cell lung cancerSemantic proximity
Application of CT Radiomics Combined with Machine Learning Methods in Predicting the Recurrence or Metastasis of Gastrointestinal Stromal TumorsSemantic proximity
Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival AnalysisSemantic proximity
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation.Semantic proximity
The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung AdenocarcinomaSemantic proximity
Prognostic risk factors of T2, T3, and T4 thoracic esophageal cancer and their relationship with improved survival after radiotherapy: A Retrospective Cohort StudySemantic proximity
Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.Semantic proximity
A CT-based Machine Learning Radiomics Model for the Prediction of Gastric Cancer Differentiation and Mechanism ExplorationSemantic proximity
Select a neighboring publication to make it the new centre.