Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD

Xu, Xinxin; Zhu, Hong; Li, Ruokun; Lin, Huimin; Grimm, Robert; Fu, Caixia; Yan, Fuhua

doi:10.1007/s00330-020-07235-4

Cited by 10 publications

(5 citation statements)

References 36 publications

(8 reference statements)

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“…A combined model integrating promising imaging findings and texture features was also established. The goodness-of-fit of the logistic regression model was evaluated using the Hosmer–Lemeshow test ( 26 ). The diagnostic efficacy of these models based on image-based analysis, TA, and the combination of the two approaches was measured by the area under the curve (AUC) of ROC curves.…”

Section: Methodsmentioning

confidence: 99%

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Guo

Chen

et al. 2021

Front. Oncol.

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PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

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Section: Methodsmentioning

confidence: 99%

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Guo

Chen

et al. 2021

Front. Oncol.

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“…Intraclass correlation coe cient (ICC) was used to test the reliability and reproducibility of intra-observer and inter-observer data features [9]. Twenty patients' MRI data were selected randomly for ICC validation analysis.…”

Section: The Intraobserver and Interobserver Agreementmentioning

confidence: 99%

Value of dynamic enhanced magnetic resonance image-based model in predicting low expression of HER-2 in breast cancer

Sun

Meng

Chen

et al. 2023

Preprint

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Objective This study aimed to evaluate the feasibility of evaluating early low expression of HER-2 in patients with breast cancer by applying Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) based imaging features, which could potentially optimize treatment for patients. Method Clinical and pathology data of 294 female patients with invasive ductal carcinoma confirmed by puncture or surgical pathology were collected. Regions of interest (ROI) were mapped. Features were then extracted from the original Magnetic Resonance Imaging (MRI) image data. Relevant features were screened out by Mann-Whitney U test. Cross-validated LASSO regression was used for feature selection. Inner and outer 10-fold cross-validation (CV) models were used. The inner 10-fold CV was used to select the best model during the Linear SVC modeling in training set, and an outer 10-fold CV was used to validate the efficiency in validation set. Model performance was evaluated by using receiver operator curve (ROC) analysis. The average accuracy, sensitivity, and specificity were calculated. Results After model selection using the inner 10-fold CV in Linear SVC modeling and validation using the outer CV, the average accuracy, sensitivity, and specificity of the validation set were 79.6%, 73.7%, and 85.6%, respectively. The average area under curve (AUC) of ROC analysis was 0.87. The diagnostic efficiency of the replacement dataset after 1000 permutation tests was compared with the original dataset, and the average accuracy, sensitivity, and specificity were all less than 0.05. The differences were all statistically significant. The model established after cross-validation could classify patients as HER2 low expression or HER2 positive. The classification efficiency of the model was higher than the chance level. Conclusion DCE-MRI imaging model can help predict the low expression of HER2 receptor in breast cancer with a high predictive efficiency, which can provide a new method for clinical diagnosis of non-invasive HER2 status.

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“…The use of radiomics, a method that extracts a large number of features from medical images using data-characterization algorithms, may uncover patterns, texture, or characteristics that may serve as digital fingerprints of disease [ 11 ]. Such methods have shown promise in individuals with liver disease [ 12 ], kidney cancer [ 13 ], and kidney transplants [ 14 ]. Based on our recent preliminary experience with selected radiomic features [ 15 ], we now have extended the analysis to include many more radiomic features in order to provide image phenotypes of the ADC maps and to verify whether the phenotypes correspond to clinical classification(s).…”

Section: Introductionmentioning

confidence: 99%

Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy

Leidner

Wilt

et al. 2022

JCM

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Given the central role of interstitial fibrosis in disease progression in chronic kidney disease (CKD), a role for diffusion-weighted MRI has been pursued. We evaluated the feasibility and preliminary efficacy of using radiomic features to phenotype apparent diffusion coefficient (ADC) maps and hence to the clinical classification(s) of the participants. The study involved 40 individuals (10 healthy and 30 with CKD (eGFR < 60 mL/min/1.73 m2)). Machine learning methods, such as hierarchical clustering and logistic regression, were used. Clustering resulted in the identification of two clusters, one including all individuals with CKD (n = 17), while the second one included all the healthy volunteers (n = 10) and the remaining individuals with CKD (n = 13), resulting in 100% specificity. Logistic regression identified five radiomic features to classify participants as with CKD vs. healthy volunteers, with a sensitivity and specificity of 93% and 70%, respectively, and an AUC of 0.95. Similarly, four radiomic features were able to classify participants as rapid vs. non-rapid CKD progressors among the 30 individuals with CKD, with a sensitivity and specificity of 71% and 43%, respectively, and an AUC of 0.75. These promising preliminary data should support future studies with larger numbers of participants with varied disease severity and etiologies to improve performance.

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Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD

Cited by 10 publications

References 36 publications

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Value of dynamic enhanced magnetic resonance image-based model in predicting low expression of HER-2 in breast cancer

Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy

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