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Reduction in Child Surgery's Crisis Division Appointments During COVID-19 Outbreak in the Tertiary School General Medical center throughout Portugal.
The presence of MYC amplification seems to increase the aggressiveness of the reported disease course, so even a small clone with this change should be indicated in cytogenetic result.Background Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS). Methods Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score. Results 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69. Conclusions Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.Objective To assess the potential of spectral photon-counting (PC) radiography (SPCR) for the detection and characterization of monosodium urate (MSU) and calcium hydroxyapatite (HA) crystals, based on effective atomic number (Zeff) values derived from specific X-ray attenuation characteristics at different energy levels. Methods Suspensions of either pure agar, synthetic MSU (200 mg/ml) or HA (100 and 150 mg/ml) crystals in agar were sealed in industry-standard polystyrene vials and supported on a 2.5-mm-thick plastic table. Samples were scanned using a vendor microfocus X-ray tube and a spectral PC detector prototype with four energy thresholds per acquisition (15, 25, 30, and 35 keV). Material decomposition calibration was performed using polymethyl methacrylate (PMMA) and polyvinylchloride (PVC) slabs. Using a custom post-processing software based on polynomial material decomposition, Zeff of the respective samples were computed. All samples were additionally scanned using dual-energy CT (DECT, 80 kV and tin-filtered 150 kV) and analyzed with a proprietary post-processing algorithm for gout. Results MSU crystal suspension attenuated significantly less than both HA samples. MSU and HA suspensions differed significantly in Zeff (mean ± SD 7.74 ± 0.28 vs. 9.43 ± 0.41, p less then .001). Zeff values from SPCR were comparable to DECT-based reference values (p = 0.16) and were independent of the radiation dose level (0.18 - 18 mAs, p = 1). Discussion This in vitro feasibility study demonstrates the potential of SPCR for discriminating MSU from HA crystal suspensions based on Zeff differences. Further studies have to corroborate these initial findings ex vivo and in vivo, and to compare the diagnostic performance of SPCR with DECT in imaging of crystal-associated arthropathies.Purpose To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). selleck chemicals llc The goal was to improve the currently applied size-based differentiation criterion in PI-RADS. Methods and materials MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade. Results 309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group ≥ 2) prostate cancer (PCa) for the PZ (AUC = 0.73) compared to the TZ (AUC = 0.63). The calculated Youden index resulted in size cut-offs of 14 mm for PZ and 21 mm for TZ tumors. Conclusion Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account.Purpose Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test negative for CAC could significantly reduce the workload of radiologists. We propose an algorithm that both excludes negative scans and segments the CAC. Method The training and internal validation data were collected from the ROBINSCA study. The external validation data were collected from the ImaLife study. Both contain annotated low-dose non-contrast cardiac CT scans. 60 scans of participants were used for training and 2 sets of 50 CT scans of participants without CAC and 50 CT scans of participants with an Agatston score between 10 and 20 were collected for both internal and external validation. The effect of dilated convolutional layers was tested by using 2 CNN architectures. We used the patient-level accuracy as metric for assessing the accuracy of our pipeline for detection of CAC and the Dice coefficient score as metric for the segmentation of CAC. Results Of the 50 negative cases in the internal and external validation set, 62 % and 86 % were classified correctly, respectively. There were no false negative predictions. For the segmentation task, Dice Coefficient scores of 0.63 and 0.84 were achieved for the internal and external validation datasets, respectively. Conclusions Our algorithm excluded 86 % of all scans without CAC. Radiologists might need to spend less time on participants without CAC and could spend more time on participants that need their attention.
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