Synthesis and automated fluorine-18 radiolabeling of new PSMA-617 derivatives with a CuAAC radiosynthetic approach

In the last decade, the development of new radiopharmaceuticals for the imaging and therapy of prostate cancer has been a highly active and important area of research, especially focusing on the prostate-specific membrane antigen (PSMA), an antigen which is upregulated in prostate, as well as in other tumor cells. A large variety of PSMA ligands have been radiolabeled, to date. Among the various derivatives, PSMA-617 resulted to be one of the most interesting in terms of interaction with the antigen and clinical properties, and its lutetium-177 labeled version has recently been approved by regulatory agencies for therapeutic purposes. For this reasons, the radiolabeling with fluorine-18 of a PSMA-617 derivative might be of interest. Beside other methodologies to radiolabel macromolecules with fluorine-18, the “click-chemistry” approach resulted to be very useful, and the copper-catalyzed azide-alkyne cycloaddition (CuAAC) is considered one of most efficient and reliable. This paper proposes the synthesis of a suitable precursor for the radiolabeling with fluorine-18 of a new PSMA-617 derivative. The whole radiosynthetic procedure has been fully automated, and the final product, which proved to be stable in plasma, has been obtained with radiochemical yield and purity suitable for subsequent preclinical studies.     

Accuracy of MRI-guided Versus Systematic Prostate Biopsy in Patients Under Active Surveillance: A Systematic Review and Meta-analysis

This meta-analysis focuses on the accuracy of upgrading to clinically significant prostate cancer (PCa) by multiparametric magnetic resonance imaging-targeted biopsy (MRI-TB) versus systematic biopsy (SB). We searched the Medline, Embase, Cochrane Central Register of Controlled Trials, Web of Science, Scopus, and Literatura Latino Americana em Ciências da Saúde databases through January 2020 for comparative, retrospective/prospective, paired-cohort, and randomized clinical trials with paired comparisons. The population consisted of patients with low-risk PCa in active surveillance with at least 1 index lesion on imaging. We evaluated the quality of evidence by using the Quality Assessment of Diagnostic Accuracy Studies-2 score. Group comparisons considered the differences between the area under the curve summary receiver operating characteristic curve in a 2-tailed method. We also compared the positive predictive value of the best single method (MRI-TB or SB) and the referral study test (combined biopsy, a combination of MRI-TB and SB). The meta-analysis included 6 studies enrolling 741 patients. The pooled sensitivity for the 2 groups was 0.79 (95% confidence interval, 0.74-0.83; I² = 75%) and 0.67 (95% confidence interval, 0.63-0.74; I² = 55.4%), respectively. The area under the curve for the MRI-TB and SB groups were 0.99 and 0.92 (P < .001), respectively. The positive predictive value for the MRI-TB and combined biopsy groups were similar. The accumulated evidence suggests better results for MRI-TB compared with SB. Therefore, use of MRI-TB alone may be preferable in patients in active surveillance harboring low-risk PCa.     

Elevated serum uric acid is a predictor of contrast associated acute kidney injury in patient with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention

BackgroundContrast associated-acute kidney injury (CA-AKI) has been associated with adverse outcomes after ST-segment elevation myocardial infarction (STEMI). However, early markers of CA-AKI are still needed to improve risk stratification. We investigated the association between elevated serum uric acid (eSUA) and CA-AKI in patients with STEMI treated with primary percutaneous coronary intervention (pPCI).Methods and resultsSerum creatinine (Scr) was measured at admission and 24, 48 and 72 h after pPCI. CA-AKI was defined as an increase of 25% (CA-AKI 25%) or 0.5 mg/dl (CA-AKI 0.5) of Scr level above the baseline after 48 h following contrast administration. Multivariable analyses to investigate CA-AKI predictors were performed by binary logistic regression and multivariable backward logistic regression model.In the 3023 patients considered, CA-AKI was more frequent among patients with eSUA as compared with patients with normal SUA levels, considering both CA-AKI definitions (CA-AKI25%: 20.8% vs 16.2%, p < 0.012; CA-AKI 0.5: 10.1% vs 5.8%, p < 0.001). The association between eSUA and CA-AKI was confirmed at multivariable analyses (CA-AKI 25%: odd ratio 1.32, 95% CI 1.03–1.69, p = 0.027; CA-AKI 0.5: odd ratio 1.76, 95% CI 1.11–2.79, p = 0.016).ConclusionElevated serum uric acid is associated with CA-AKI after reperfusion in patients with STEMI treated with pPCI.     

Three-dimensional architecture of nephrons in the normal and cystic kidney

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Does ATTRACT change our DVT management practice?

In 2017, the long awaited results of the ATTRACT trial were published in the New England Journal of Medicine leaving the scientific community with disappointment as the study did not show the expected results. Producing not the expected outcome is not uncommon in science – furthermore, it is important to disapprove common beliefs. But has the ATTRACT trial really the power to change our practice? Are the results correct in terms of evidence based on the methods used?

In 2017, the long awaited results of the ATTRACT trial were published in the New England Journal of Medicine^1,2 leaving the scientific community with disappointment as the study did not show the expected results. Producing not the expected outcome is not uncommon in science – furthermore, it is important to disapprove common beliefs. But has the ATTRACT trial really the power to change our practice? Are the results correct in terms of evidence based on the methods used? To understand the results, it is important to analyze the methods of the trial and to discuss them in a broader view.     

Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68–0.78 vs 0.56–0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.