In the recent past, the Classifiers are based on genetic signatures in which many microarray studies are analyzed to predict medical results for cancer patients. However, the Signatures from different studies have been benefitted with low-intensity ratio during the classification of individual datasets has been considered as a significant point of research in the present scenario. Hence to overcome the above-discussed issue, this paper provides a Deep Learning Framework that combines an algorithm of necessary processing of Linear Discriminant Analysis (LDA) and Auto Encoder (AE) Neural Network to classify different features within the profile of gene expression. Hence, an advanced ensemble classification has been developed based on the Deep Learning (DL) algorithm to assess the clinical outcome of breast cancer. Furthermore, numerous independent breast cancer datasets and representations of the signature gene, including the primary method, have been evaluated for the optimization parameters. Finally, the experiment results show that the suggested deep learning frameworks achieve 98.27% accuracy than many other techniques such as genomic data and pathological images with multiple kernel learning (GPMKL), Multi-Layer Perception (MLP), Deep Learning Diagnosis (DLD), and Spatiotemporal Wavelet Kinetics (SWK).

Objectives: To explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP). Methods: A total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model 2: plaque characteristics model. Model 3: PCAT radiomics model. Model 4: clinical + radiomics model. Model 5: plaque characteristics + radiomics model. The evaluation of the models encompassed identification accuracy, calibration precision, and clinical applicability. Delong’ test was employed to compare the area under the curve (AUC) of different models. Results: Seven radiomic features, including two shape features, three first-order features, and two textural features, were selected to build the PCAT radiomics model. In contrast to the clinical model and plaque characteristics model, the PCAT radiomics model (AUC 0.85 for training, 0.84 for internal validation, and 0.81 for external validation; p < 0.05) achieved significantly higher diagnostic performance in predicting RPP. The separate combination of radiomics with clinical and plaque characteristics model did not further improve diagnostic efficacy statistically (p > 0.05). Conclusion: Radiomic feature analysis derived from PCAT significantly improves the prediction of RPP as compared to clinical and plaque characteristics. Radiomic analysis of PCAT may improve monitoring RPP over time. Critical relevance statement: Our findings demonstrate PCAT radiomics model exhibited good performance in the prediction of RPP, with potential clinical value. Key Points: Rapid plaque progression may be predictable with radiomics from pericoronary adipose tissue. Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression. Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression. Graphical

Background: This study aimed to explore the diagnostic accuracy of cardiac magnetic resonance tissue tracking (CMR-TT) technology in the quantitative evaluation of left myocardial infarction for differentiating between acute and chronic myocardial infarction. Methods: A total of 104 human subjects were enrolled in this prospective study. Among them, 64 healthy subjects and 40 patients with left ventricular myocardial infarction and 7 days and 6 months’ follow-up CMR studies, including steady-state free precession (SSFP) sequence and late gadolinium enhancement MR imaging, were enrolled. The strain parameters of the infarcted myocardium, its corresponding remote segments, and global right ventricular strain were analyzed using tissue tracking technology, and CMR-TT 3D strain parameters in radial, circumferential, and longitudinal directions were obtained. Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accuracy of the CMR-TT strain parameters for discriminating between acute and chronic myocardial infarction. Results: Peak radial strain (RS) of infarcted myocardium increased from 12.99±7.28 to 18.57±6.66 at 6 months (P<0.001), whereas peak circumferential strain (CS) increased from −8.82±4.71 to −12.78±3.55 (P<0.001). CS yielded the best areas under the ROC curve (AUC) of 0.751 in showing differentiation between acute and chronic myocardial infarction of all the strain parameters obtained. The highest significant differences between acute myocardial infarction and normal myocardium, both in the left and right ventricles, were also found in the RS (P<0.001) and CS (P<0.001). Conclusions: RS and CS obtained by CMR-TT have high sensitivity and specificity in the differential diagnosis of acute versus chronic myocardial infarction, and their use is thus worth popularizing in clinical application.

To evaluate both left and right ventricular (LV and RV) function in patients with pulmonary arterial hypertension (PAH) using cardiac magnetic resonance tissue-tracking (CMR-TT) technology and explore its clinical value. Methods: A total of 79 participants (including 47 patients with PAH and 32 healthy controls) underwent cardiac magnetic resonance imaging (CMRI) with a short-axis balanced steady-state free precession (SSFP) sequence. The biventricular cardiac function parameters and strain parameters were obtained by postprocessing with CVI42 software. A comparative analysis was performed between the LV and RV strain parameters in all PAH patients and in PAH patients with reduced or preserved cardiac function. Results: The results showed preferable repeatability of CMR-TT in analyzing the global radial strain (GRS), circumferential strain (GCS), and longitudinal strain (GLS) of the left and right ventricles in the PAH group. The GRS, GCS, and GLS of the left and right ventricles except for LV GRS (LVGRS) of PAH patients were significantly lower than those of healthy controls (p < 0.05 for all). The GRS and GCS of the left and right ventricles showed a moderate correlation in the PAH group (r = 0.323, p = 0.02; r = 0.301, p = 0.04, respectively). PAH patients with preserved RV function (n = 9) showed significantly decreased global and segmental RS, CS, and LS of the right ventricles than healthy controls (p < 0.05 for all), except for basal RVGCS (RVGCS-b, p = 0.996). Only the LVGLS was significantly different between the PAH patients with preserved LV function (n = 32) and the healthy controls (−14.23 ± 3.01% vs. −16.79 ± 2.86%, p < 0.01). Conclusions: As a nonradioactive and noninvasive technique, CMR-TT has preferable feasibility and repeatability in quantitatively evaluating LV and RV strain parameters in PAH patients and can be used to effectively detect early biventricular myocardial damage in patients with PAH.

Although many studies have examined the relationship between uric acid (UA) and coronary artery disease (CAD), whether UA is an independent risk factor contributing to progression of CAD is still controversial. Whether UA plays a different role in different sexes is also unclear.