2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study

Meng, Lingwei; Dong, Di; Chen, Xin; Fang, Mengjie; Wang, Rongpin; Li, Jing; Liu, Zaiyi; Tian, Jie

doi:10.1109/jbhi.2020.3002805

Cited by 81 publications

(63 citation statements)

References 35 publications

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“…The LASSO is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients (Meng et al., 2020). The optimization objective for LASSO is:

{normalmin}_{β} (\frac{1}{n} \sum_{i = 1}^{n} {()(y_{i} ‐ x_{i} β^{T})}^{2} + λ {false‖ β false‖}_{1})

where x i is the i ‐th patient's feature vector,

y_{i}

is the classification variable,

β

is the weight vector of the linear model, and

λ > 0

is a penalty term, which controls the value of shrinkage.…”

Section: Methodsmentioning

confidence: 99%

Radiomics‐based classification models for HBV‐related cirrhotic patients with covert hepatic encephalopathy

et al. 2020

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Introduction The significant abnormalities of precuneus (PC), which are associated with brain dysfunction, have been identified in cirrhotic patients with covert hepatic encephalopathy (CHE). The present study aimed to apply radiomics analysis to identify the significant radiomic features in PC and their subregions, combine with clinical risk factors, then build and evaluate the classification models for CHE diagnosis. Methods 106 HBV‐related cirrhotic patients (54 had current CHE and 52 had non‐CHE) underwent the three‐dimensional T1‐weighted imaging. For each participant, PC and their subregions were segmented and extracted a large number of radiomic features and then identified the features with significant discriminative power as the radiomics signature. The logistic regression analysis was employed to develop and evaluate the classification models, which are constructed using the radiomics signature and clinical risk factors. Results The classification model (R‐C model) achieved best diagnostic performance, which incorporated radiomics signature (4 radiomic features from right PC), venous blood ammonia, and the Child‐Pugh stage. And the area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, and accuracy values were 0.926, 1.000, 0.765, and 0.848, in the testing set. Application of the radiomics nomogram in the testing set still showed a good predictive accuracy. Conclusions This study presented the radiomic features of the right PC, as a potential image marker of CHE. The radiomics nomogram that incorporates the radiomics signature and clinical risk factors may facilitate the individualized prediction of CHE.

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{normalmin}_{β} (\frac{1}{n} \sum_{i = 1}^{n} {()(y_{i} ‐ x_{i} β^{T})}^{2} + λ {false‖ β false‖}_{1})

where x i is the i ‐th patient's feature vector,

y_{i}

is the classification variable,

β

is the weight vector of the linear model, and

λ > 0

is a penalty term, which controls the value of shrinkage.…”

Section: Methodsmentioning

confidence: 99%

Radiomics‐based classification models for HBV‐related cirrhotic patients with covert hepatic encephalopathy

et al. 2020

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“…Since annotations of the ROIs are not created as a routine work in the clinical practice, we considered our algorithm should work with minimal labor. Aside from that, several past studies reported that two-dimensional radiomic features based on the CT images with maximum solid tumor areas showed comparable performance to the three-dimensional features in characterization of tumors [26,27]. Therefore, the two-dimensional feature extraction algorithms were applied in the present study.…”

Section: Plos Onementioning

confidence: 97%

“…Anisotropic CT images and ROIs were transformed into isotropic images with an isovoxel size of 0.77 mm (mode pixel size in the training dataset), using cubic and shape-based interpolations [25], respectively. In the present study, an axial plane of each CT image, which contained the maximum axial plane area of the ROIs, was selected for the calculation of radiomic features [26,27]. Since annotations of the ROIs are not created as a routine work in the clinical practice, we considered our algorithm should work with minimal labor.…”

Section: Plos Onementioning

confidence: 99%

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

et al. 2021

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Objectives To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). Materials and methods Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. Results The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). Conclusion The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

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“…All CT images were then resized to 40 × 243 × 243. Next, we utilized lung volume corresponding binary volume masks to extract radiomic features, then center-cropped the lung volume to a size of 20 × 243 × 243 to focus on the central slice of lung volume [19]. The cropped lung volume was then inputted into our 3D DL model.…”

Section: B Automated Ct Image Segmentationmentioning

confidence: 99%

Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics

Dong

et al. 2020

IEEE J. Biomed. Health Inform.

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The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. Methods: We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n = 174) and a test cohort (n = 43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network

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2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study

Cited by 81 publications

References 35 publications

Radiomics‐based classification models for HBV‐related cirrhotic patients with covert hepatic encephalopathy

Radiomics‐based classification models for HBV‐related cirrhotic patients with covert hepatic encephalopathy

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics

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