Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.

Purpose: To explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients. Methods: A total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC). Results: Larger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively. Conclusions: Our study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation. Critical relevance statement: Both clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation. Key points: • Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation. Graphical

Physical exercise has been proved to reduce the risk of major depression in Subthreshold depression (StD) individuals effectively, yet little is known about the spontaneous brain activity changes associated with physical exercise. A total of 70 adult subjects, including 38 StD and 32 healthy control (HC) subjects, underwent a resting-state functional magnetic resonance imaging (rs-fMRI) before and after eight-week aerobic exercise respectively. Then, the amplitude of low-frequency fluctuation (ALFF) alterations between the two groups were quantitatively analyzed. Before exercise intervention, the rs-fMRI data showed increased ALFF of the right putamen in the StD group compared with HC group. After exercise intervention, there was no significant ALFF change observed between the StD and HC groups. The longitudinal ALFF differences from pre- to post- exercise intervention showed significantly decreased ALFF in the right middle and inferior occipital gyrus, right middle and inferior temporal gyrus, right fusiform gyrus (FG), while increased ALFF in the right middle cingulate, right superior parietal louble, right inferior parietal lobule (IPL) (inferior parietal gyrus and supramarginal gyrus), and bilateral precuneus in the StD group. As for HC group, the results showed that decreased ALFF in the right FG and right parahippocampus, while increased ALFF in the right precuneus, right middle cingulate, right supplementary motor area, right superior parietal lobule and right paracentral lobule in the HC group. No significant correlation between changes of ALFF and clinical scale scores in the StD group. The definitions of StD are varied in terms of different studies, the final sample size was relatively small, and the age range of the subjects in this study was narrow. Meanwhile, the exercise intervention trial was short-term. These results further support the standpoint that physical exercise has the potential to reshape the abnormal patterns of spontaneous brain activity in adults with StD.