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Purpose: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. Methods: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. Results: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). Conclusion: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Swiss National Foundation The development of novel adjuvant angiogenic therapies to restore the low-perfused microvascular network upon myocardial infarction (MI) is crucial to avoid a possible end-stage heart failure. Of the current adult cell-based therapies, human adipose tissue-derived stromal vascular fraction cell (SVF) has vast reparative potential, principally due to: 1) its heterogeneous composition rich in mesenchymal stem cells (MSC), endothelial cells (EC), pericytes and hematopoietic cells, among others. In vitro engineering of SVF-based patches under unidirectional flow, applied by the help of a perfusion-based bioreactor, was found to increase certain cellular SVF subgroups such as pericytes, compared to static culture. In this study, we aimed at studying the potential of SVF-based engineered tissues in a model of chronic MI in nude rats. Human SVF cells were isolated upon liposuction and cultured on 3D collagen sponges (8 mm diameter, 3 mm thickness) either under constant unidirectional perfusion or in static condition for 5 days. Patches were characterized in terms of cellular composition prior to implantation. MI was induced by permanent ligation of the left anterior descending (LAD) coronary artery in male nude rats. Cardiac MRI was performed 4 weeks after MI; prior to the suture of patches and before sacrifice (4 weeks after implantation). Left ventricular ejection fraction (EF) was the surrogate marker and primary end point for cardiac pump function. Controls included untreated MI animals. Following perfusion culture, SVF cells were composed with a statistically superior percentage of pericytes, identified as CD45- CD34- CD146+ compared to static culture (28.06±10.03 and 3.37±2.50, respectively, p<0.0007). The presence of other cell subpopulations was similar in the patches generated in perfusion or static culture. While the percentage of EF at the time of sacrifice resulted to be not statistically different between static and perfusion-based patches, statically generated constructs showed a general trend of decrease in the % EF before and after treatment (rat 1: 61.96 vs 52.90; rat 2: 55.39 vs 53.00; rat 3: 52.34 vs 50.62, respectively). Perfusion-cultured patches, instead, rather improved the cardiac function, measured as % EF (rat 1: 51.82 vs 58.72; rat 2: 51.66 vs 60.45; rat 3: 53.50 vs 52. 36, respectively for 4 weeks following MI and 4 weeks following treatment). When comparing the ratio of the % EF 8 weeks and 4 weeks between static or perfusion-based patches and the untreated controls, rats treated with patches generated under perfusion resulted to show higher levels of % EF, with an almost statistically difference (p=0.0556), compared to the control group. The observed results showed the great potential of human SVF-based patches in the improvement of the heart pump function. [ABSTRACT FROM AUTHOR]

Our objective was to assess the ability of MR imaging in the detection of the normal appendix, and to describe the MR appearance of the normal appendix. There were 15 healthy volunteers (11 girls, 4 boys; mean age 12.3 years) who underwent MR imaging on a 1.0-T unit. The imaging protocol included axial and coronal T2-weighted ultra turbo spin-echo (UTSE)-weighted images, axial T1-weighted turbo spin-echo (TSE) and coronal short tau inversion recovery (STIR)/TSE sequences. Confidence regarding the detection was scored from 1 (high confidence) to 3 (low confidence). Thickness was measured and MR appearance described. Clinical control after 2 weeks revealed no signs or symptoms of acute appendicitis. The normal appendix was seen in 86% on T2/UTSE-weighted images and in 73% on T1/TSE-weighted images and in none on STIR/TSE images. On axial T2/UTSE-weighted images, normal appendix had a hyperintense center and a hypointense wall, and was mostly hypointense on T1/TSE-weighted images, with a mean thickness of 4.5 mm. Magnetic resonance imaging seems to be an accurate method for the assessment of the normal appendix in children; thus, MR imaging might be an alternative to CT if US examinations are inconclusive. [ABSTRACT FROM AUTHOR]