Back to search

Article

Application of CT Radiomics Combined with Machine Learning Methods in Predicting the Recurrence or Metastasis of Gastrointestinal Stromal Tumors

2021-12-27

Abstract excerpt

<h4>Background: </h4> The study aimed to evaluate the diagnostic performance of machine learning-based CT radiomics models for predicting the recurrence and metastasis of gastrointestinal stromal tumors (GISTs) preoperatively. <h4>Methods: </h4>: A total of 382 patients with histopathological confirmed GISTs were retrospectively included. According to postoperative follow-up, patients were classified into non-recu...

Topics

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

Identifiers and source

Literature Corpus work
034ead2f-a1a6-560c-9d45-ec1303914f40
DOI
10.21203/rs.3.rs-1124517/v2
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.
Application of CT Radiomics Combined with Machine Learning Methods in Predicting the Recurrence or Metastasis of Gastrointestinal Stromal TumorsDOI 10.21203/rs.3.rs-1124517/v2
Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center studySemantic proximity
Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study.Semantic proximity
Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.Semantic proximity
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
Study on the Combined Prediction Model of c-KIT and PDGFRa Gene Mutation Status and Clinicopathological Features in 241 Patients with Gastrointestinal Stromal TumorsSemantic proximity
Machine Learning Model Integrating Clinical Features, CT Radiomics, and Transfer Learning for Predicting Aggressive Recurrence of Hepatocellular CarcinomaSemantic proximity
Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study.Semantic 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
Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutationSemantic proximity
Personalized Risk Stratification in Colon Cancer using Radiomic-Based Predictive ModelsSemantic proximity
CT-based Radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinomaSemantic proximity
[Interpretation on 2013 updated Chinese Consensus on the Diagnosis and Treatment of Gastrointestinal Stromal Tumors].Semantic proximity
Select a neighboring publication to make it the new centre.