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The radiomics algorithms and deep learning (DL) models have demonstrated significant potential in medical image analysis [20]. Radiomics is a quantitative medical imaging analysis approach that aims to transform high-dimensional image features (such as texture heterogeneity, spatial topological relationships, and intensity distribution) into quantifiable digital biomarkers, thereby providing objective evidence to guide clinical decision-making.
J Med Internet Res 2026;28:e77092
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Current research primarily focuses on using radiomics to diagnose KOA and predict its radiological progression [11-14]. However, studies on radiomics for predicting the pain progression of KOA are still relatively scarce. A recent study that used magnetic resonance imaging (MRI) radiomics to predict pain progression in KOA demonstrated that the constructed radiomics model achieved an area under the receiver operating characteristic curve (AUC) of 0.79 to 0.86 for KOA pain progression prediction [15].
JMIR Med Inform 2026;14:e78338
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Recently, radiomics has advanced the development and application of prediction models by converting images into repeatable quantitative data. Prediction models based on radiomic features have demonstrated significant clinical value in diagnosing and treating HCC. Studies have shown that radiomic features are effective in predicting HCC microvascular invasion [5], early recurrence [55], and Ki-67 and cytokeratin 19 expression [7].
J Med Internet Res 2026;28:e82839
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Radiomics, a rapidly advancing field, uses advanced image analysis to extract quantitative features that extend beyond what is visible to the human eye [8]. By applying mathematical approaches, radiomics enables the quantification of visual attributes, offering deeper insights into imaging data. There has been growing interest in deploying machine learning in clinical practice in recent years.
JMIR Form Res 2025;9:e81038
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This meta-analysis included studies that used radiomics-based ML models to predict CRC recurrence risk. The Radiomics Quality Score (RQS) was used to determine the reporting completeness and methodological robustness of the included articles [29].
JMIR Med Inform 2025;13:e78644
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Radiomics aids in analyzing trabecular bone microstructure [12], assessing BMD [13], differentiating acute from chronic OVFs [14], and predicting residual back pain in these patients [15]. Deep learning radiomics (DLR) uses network architectures such as Res Net, pretrained on Image Net, to extract deep imaging features from images, a widely adopted approach.
JMIR Med Inform 2025;13:e75665
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Furthermore, as our study incorporated a substantial amount of radiomics research, the radiomics quality score (RQS) was used for quality assessment. The RQS consists of 16 criteria, with a maximum score of 36 points.
J Med Internet Res 2025;27:e73528
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Radiomics feature selection using the least absolute shrinkage and selection operator regression model. (A) Least absolute shrinkage and selection operator method was used to confirm the optimal adjustment parameter λ for arterial phase, (C) portal venous phase, and (E) fusion features. (B) Least absolute shrinkage and selection operator coefficient profiles of the selected radiomics features of arterial phase, (D) portal venous phase, and (F) fusion features.
JMIR Cancer 2025;11:e67379
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ML approaches leveraging radiomics help to integrate large datasets and optimize the use of CCTA data while minimizing interpretation bias due to interobserver variability. Furthermore, radiomics-based ML analysis improves the discriminatory power of CCTA in detecting advanced atherosclerotic lesions [6].
J Med Internet Res 2025;27:e68872
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Based on the selected radiomics features, a radiomics model was constructed by using the support vector machine classifier. As a supervised learning method that was very effective in linear or nonlinear classification tasks, the support vector machine classifier has been widely used in radiomics analysis.
J Med Internet Res 2024;26:e56851
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