scholarly journals Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

2021 ◽  
Author(s):  
Bo Liu ◽  
◽  
Hao Liu ◽  
Lequan Zhang ◽  
Yancheng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumors ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Stromal Tumors ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Exon 11 ◽  
Center Study

scholarly journals Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Xijiao Liu ◽  
Yuan Yin ◽  
Xiaozhou Wang ◽  
Caiwei Yang ◽  
Shang Wan ◽  
...  
Keyword(s):  
Gene Mutation ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Stromal Tumors ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Exon 11

scholarly journals Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

2020 ◽  
Author(s):  
Bo Liu ◽  
Hexiang Wang ◽  
Shunli Liu ◽  
Shifeng Yang ◽  
Yancheng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Arterial Phase ◽  
Training Set ◽  
Clinical Model ◽  
Stromal Tumors ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Exon 11

Abstract Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

scholarly journals Machine Learning for Detecting Early Infarction in Acute Stroke with Non–Contrast-enhanced CT

Radiology ◽  
2020 ◽  
Vol 294 (3) ◽  
pp. 638-644 ◽  
Author(s):  
Wu Qiu ◽  
Hulin Kuang ◽  
Ericka Teleg ◽  
Johanna M. Ospel ◽  
Sung Il Sohn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Stroke ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Early Infarction

scholarly journals Delayed Adverse Reaction to Contrast-enhanced CT: A Prospective Single-Center Study Comparison to Control Group without Enhancement

Radiology ◽  
2010 ◽  
Vol 255 (3) ◽  
pp. 764-771 ◽  
Author(s):  
Shaun Loh ◽  
Sepideh Bagheri ◽  
Richard W. Katzberg ◽  
Maxwell A. Fung ◽  
Chin-Shang Li
Keyword(s):  
Adverse Reaction ◽  
Control Group ◽  
Single Center ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Single Center Study ◽  
Enhanced Ct ◽  
Center Study
Sign in / Sign up

Export Citation Format

Share Document