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Biliary tract cancer (BTC) is a rare malignancy with a long disease course and an overall poor prognosis. Despite multiple chemotherapy agents, there is no defined second-line treatment opportunity for advanced BTCs. In the era of precision oncology, NGS plays an important role in identifying mutations that may predict the molecular pathomechanism and manage the BTC therapy. The peripheral blood liquid biopsy (LB) of cancer patients represents variable amounts of cell-free DNA (cfDNA) released from tumor foci of any anatomical location. Our study aimed to identify somatic mutations and tumor variant burden (TVB) in cell-free and matched tumor DNA. We found a positive correlation between the estimated tumor volume and cfDNA yield (r = 0.9326, p < 0.0001). Comparing tissue and LB results, similar TVB was observed. SNVs were proven in 84% of the cases, while in two cases, only the LB sample was informative for molecular analysis. The most important aberrations in BTCs, such as FGFR2, IDH1, IDH2, KRAS, and TP53, could be detected in matched LB samples. Our prospective study demonstrates a minimally invasive testing approach to identify molecular genetic alterations in cholangiocarcinoma and gallbladder cancers. Clinical applications of cfDNA reflect by capturing the outstanding spatial tumor heterogeneity and guarantee novel aspects for the precision oncology treatment.

Purpose Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. Methods We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. Results In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Conclusion Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

Background/Aim: Previous studies have already shown that 68 Gallium( 68 Ga)-labeled NGR-based radiopharmaceuticals specifically bind to the neoangiogenic molecule Aminopeptidase N (APN/CD13). The aim of this study was to evaluate the applicability of 68 Ga-NOTAc(NGR) in the in vivo detection of the temporal changes of APN/CD13 expression in the diabetic retinopathy rat model using positron emission tomography (PET). Materials and Methods: Ischemia/reperfusion injury was initiated by surgical ligation of the left bulbus oculi of rats. In vivo PET imaging studies were performed after the surgery using 68 Ga-NOTA-c(NGR). Results: Significantly higher 68 Ga-NOTA-c(NGR) uptake was observed in the surgically-ligated left bulbus, compared to the bulbus of the non-surgical group at each investigated time point. The western blot and histological analysis confirmed the increased expression of the neo-angiogenic marker APN/CD13. Conclusion: 68 Ga-NOTA-c(NGR) is a suitable radiotracer for the detection of the temporal changes of the ischemia/reperfusion-mediated expression of APN/CD13 in the surgically induced diabetic retinopathy rat model.Diabetes mellitus (DM) belongs to the cluster of metabolic disorders, characterized by hyperglycemia over a prolonged interlude. Symptoms of hyperglycemia include polyuria, polydipsia, and polyphagia (1, 2). DM may cause many acute or severe complications (3), such as cardiovascular disease, stroke, chronic kidney failure, neuropathy and diabetic retinopathy (DR) (2, 4). DR is one of the most common microvascular complications of diabetes. In 2015, there were more than 414 million people affected by some form of diabetes. According to certain predictions, the number of diabetic patients will have extended to 641 million in 2040 (5). Diabetes can generally be divided into three types: i) insulin-dependent, ii) insulin-independent and iii) gestational, although, all patients commonly experience hyperglycemia. According to the current scientific evidence, approximately 33% of diabetic patients have signs of DR and approximately 10% of them even develop visionthreatening retinopathy (6). Clinically, DR-induced 657 This article is freely accessible online.

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