Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.

Scientific Reports
Hairui ChuRongchun Xu

Abstract

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013-2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient's enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images...Continue Reading

References

Nov 19, 2015·Radiology·Robert J GilliesHedvig Hricak
Dec 12, 2018·Gastric Cancer : Official Journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association·Ming LiuErhu Jin
Nov 28, 2019·Cancer Metastasis Reviews·Pushpa NeppalaJason K Sicklick
Jan 15, 2020·Cancer Imaging : the Official Publication of the International Cancer Imaging Society·Caiyue RenShengjian Zhang
Mar 21, 2020·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Hyo Jung ParkSeung Soo Lee
Jun 18, 2020·Cancer Management and Research·Fangxing Peng, Yao Liu
Jul 4, 2020·Translational Oncology·Haixin Zhang, Qi Liu
Jul 31, 2020·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Ji Eun ParkHo Sung Kim

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Software Mentioned

- SNAP
SPSS
GLCMEnergy
[UNK] SNAP
R
LASSO
ITK
AK
Artificial Intelligence Kit ( AI - Kit

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