Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT

Homayounieh, Fatemeh; Saini, Sanjay; Mostafavi, Leila; Khera, Ruhani Doda; Sühling, Michael; Schmidt, Bernhard; Singh, Ramandeep; Flohr, Thomas; Kalra, Mannudeep K.

doi:10.1007/s11548-020-02212-0

Cited by 15 publications

(11 citation statements)

References 21 publications

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“…Since the feature subset of each decision tree is randomly selected, it has good robustness and can maintain accuracy even if data are missing. Homayounieh et al used random forest to differentiate diffuse liver diseases on non-contrast CT [45].…”

Section: Random Forestmentioning

confidence: 99%

Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients

et al. 2022

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Purpose Previously, the evaluation of xerostomia depended on subjective grading systems, rather than the accurate saliva amount reduction. Our aim was to quantify acute xerostomia with reduced saliva amount, and apply radiomics, dose-volume histogram (DVH) criteria and clinical features to predict saliva amount reduction by machine learning techniques. Material and methods Computed tomography (CT) of parotid glands, DVH, and clinical data of 52 patients were collected to extract radiomics, DVH criteria and clinical features, respectively. Firstly, radiomics, DVH criteria and clinical features were divided into 3 groups for feature selection, in order to alleviate the masking effect of the number of features in different groups. Secondly, the top features in the 3 groups composed integrated features, and features selection was performed again for integrated features. In this study, feature selection was used as a combination of eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to alleviate multicollinearity. Finally, 6 machine learning techniques were used for predicting saliva amount reduction. Meanwhile, top radiomics features were modeled using the same machine learning techniques for comparison. Result 17 integrated features (10 radiomics, 4 clinical, 3 DVH criteria) were selected to predict saliva amount reduction, with a mean square error (MSE) of 0.6994 and a R2 score of 0.9815. Top 17 and 10 selected radiomics features predicted saliva amount reduction, with MSE of 0.7376, 0.7519, and R2 score of 0.9805, 0.9801, respectively. Conclusion With the same number of features, integrated features (radiomics + DVH criteria + clinical) performed better than radiomics features alone. The important DVH criteria and clinical features mainly included, white blood cells (WBC), parotid_glands_Dmax, Age, parotid_glands_V15, hemoglobin (Hb), BMI and parotid_glands_V45.

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Section: Random Forestmentioning

confidence: 99%

Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients

et al. 2022

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“…The automatic segmentation and inclusion of the entire renal volume enabled us to apply radiomics beyond renal calculi to the entire renal volume and obtain a reliable and generalizable prediction on stone burden and need for invasive treatment procedures. Other studies beyond kidneys have also reported on organ radiomics for assessing pancreatic ductal adenocarcinoma from entire pancreas [17], diffuse liver diseases from whole liver [18], COVID-19 pneumonia from whole lung [19,20], and white matter hyperintensities from whole brain [21]. Due to the need for efficient image interpretation and the well-known complexity of radiomics (1690 features in our prototype), autosegmentation of entire organ-based regions of interest as well as estimation and analyses of radiomics is essential for bringing them into the clinical workflow.…”

Section: Discussionmentioning

confidence: 99%

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

et al. 2020

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Purpose To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi. Methods The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output. Results Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85-0.92). Higher-order radiomics (gray-level size zone matrix-GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89-0.92). Conclusion Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.

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“…Radiomics [16], which allows the extraction of numerous quantitative features from medical imaging, so then may possibly reflect histological characteristics. Although radiomics, based on magnetic resonance imaging (MRI) or computed tomography (CT), have already been applied for diagnosing and staging fibrosis in some chronic liver diseases [17,18], to our knowledge, there is no study on the radiomics based on 18 F-FDG PET in MAFLD. Although the routine 18 F-FDG PET has shown a potential role in the diagnosis and evaluation of fibrosis/cirrhosis in MAFLD, radiomics may help to scrutinize imaging data deeply to improve the performance.…”

Section: Ivyspringmentioning

confidence: 99%

Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study

et al. 2021

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Rationale: Since non-invasive tests for prediction of liver fibrosis have a poor diagnostic performance for detecting low levels of fibrosis, it is important to explore the diagnostic capabilities of other non-invasive tests to diagnose low levels of fibrosis. We aimed to evaluate the performance of radiomics based on 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) in predicting any liver fibrosis in individuals with biopsy-proven metabolic dysfunction-associated fatty liver disease (MAFLD). Methods: A total of 22 adults with biopsy-confirmed MAFLD, who underwent 18 F-FDG PET/CT, were enrolled in this study. Sixty radiomics features were extracted from whole liver region of interest in 18 F-FDG PET images. Subsequently, the minimum redundancy maximum relevance (mRMR) method was performed and a subset of two features mostly related to the output classes and low redundancy between them were selected according to an event per variable of 5. Logistic regression, Support Vector Machine, Naive Bayes, 5-Nearest Neighbor and linear discriminant analysis models were built based on selected features. The predictive performances were assessed by the receiver operator characteristic (ROC) curve analysis. Results: The mean (SD) age of the subjects was 38.5 (10.4) years and 17 subjects were men. 12 subjects had histological evidence of any liver fibrosis. The coarseness of neighborhood grey-level difference matrix (NGLDM) and long-run emphasis (LRE) of grey-level run length matrix (GLRLM) were selected to predict fibrosis. The logistic regression model performed best with an AUROC of 0.817 [95% confidence intervals, 0.595-0.947] for prediction of liver fibrosis. Conclusion: These preliminary data suggest that 18 F-FDG PET radiomics may have clinical utility in assessing early liver fibrosis in MAFLD.

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Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT

Cited by 15 publications

References 21 publications

Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients

Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study

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