Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma.

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.

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.

OBJECTIVE: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. METHODS: A total of 401 participants (311 patients and 90 age-matched volunteers) were recruited for this prospective study. The two-dimensional UNet segmentation model of the LV and classification model for identifying paradoxical pulsation were established using the DCNN model. Features of 2- and 3-chamber images were extracted with 2-dimensional (2D) and 3D ResNets with masks generated by a segmentation model. Next, the accuracy of the segmentation model was evaluated using the Dice score and classification model by receiver operating characteristic (ROC) curve and confusion matrix. The areas under the ROC curve (AUCs) of the physicians in training and DCNN models were compared using the DeLong method. RESULTS: The DCNN model showed that the AUCs for the detection of paradoxical pulsation were 0.97, 0.91, and 0.83 in the training, internal, and external testing cohorts, respectively (p < 0.001). The 2.5-dimensional model established using the end-systolic and end-diastolic images combined with 2-chamber and 3-chamber images was more efficient than the 3D model. The discrimination performance of the DCNN model was better than that of physicians in training (p < 0.05). CONCLUSIONS: Compared to the model trained by 2-chamber or 3-chamber images alone or 3D multiview, our 2.5D multiview model can combine the information of 2-chamber and 3-chamber more efficiently and obtain the highest diagnostic sensitivity. CLINICAL RELEVANCE STATEMENT: A deep convolutional neural network model that integrates 2-chamber and 3-chamber CMR images can identify LV paradoxical pulsation which correlates with LV thrombosis, heart failure, ventricular tachycardia after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. KEY POINTS: • The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.

[Relationship between Quantitative Monitoring of WT1 Gene and Prognosis in Patients with Acute Leukemia].

OBJECTIVE: To investigate the exprassion of WT1 gene in patients with adult acute myeloid leukemia (AML) and its clinical significance. METHODS: Sixty-three newly diagnosed patients with acute myeloid leukemia were selected. Quantitative RT-PCR was used to detect the expression of WT1 gene in the 63 AML patients and 20 non-AML controls. RESULTS: WT1 gene was highly expressed in AML patents and its expression in the low-risk group was significantly lower than that in middle-risk group and high-risk group (P<0.05), and no significant difference of WT1 gene expression between middle-risk and high-risk group was observed. In the patients of middle-risk and high-risk patients, the expression of WT1 gene in the remission group was significantly lower than that in the patients of non-remission after treatment (P<0.05). The non-remission patients after first treatment in middle-risk and high-risk group were treated with second induction therapy. After second induction therapy, the WT1 expression in remission patients was significantly decreased (P<0.05) in comparison with that in patients still in non-remission. There was a negative correlation between WT1 expression and the 2-year overall survival rate in the newly diagnosed middle and high-risk AML patients. CONCLUSION: The detection of WT1 gene expression can help to divide AML patients into low-/middle-/high-risk groups and to evaluate therapeutic response and clinical prognosis in middle and high-risk AML patients.

Physiological and metabolic differences between visceral and subcutaneous adipose tissues in Nile tilapia (Oreochromis niloticus).

Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SCAT) have different structures and metabolic functions and play different roles in the regulation of the mammal endocrine system. However, little is known about morphology and physiological and metabolic functions between VAT and SCAT in fish. We compared the morphological, physiological, and biochemical characteristics of VAT and SCAT in Nile tilapia and measured their functions in energy intake flux, lipolytic ability, and gene expression patterns. SCAT contained more large adipocytes and nonadipocytes than VAT in Nile tilapia. VAT had higher lipid content and was the primary site for lipid deposition. Conversely, SCAT had higher hormone-induced lipolytic activity. Furthermore, SCAT had a higher percentage of monounsaturated and lower polyunsaturated fatty acids than VAT. SCAT had higher mitochondrial DNA, gene expression for fatty acid ß-oxidation, adipogenesis, and brown adipose tissue characteristics, but it also had a lower gene expression for inflammation and adipocyte differentiation than VAT. SCAT and VAT have different morphological structures, as well as physiological and metabolic functions in fish. VAT is the preferable lipid deposition tissue, whereas SCAT exhibits higher lipid catabolic activity than VAT. The physiological functions of SCAT in fish are commonly overlooked. The present study indicates that SCAT has specific metabolic characteristics that differ from VAT. The differences between VAT and SCAT should be considered in future metabolism studies using fish as models, either in biomedical or aquaculture studies.