The road toll for heavy vehicles in the Czech Republic. Charts for calculating toll costs for trucks in the combined shipment and less-than-truckload sector in the Czech Republic

(no abstract available

Zur Effizienz von Schieneninfrastrukturbau-vorhaben am Beispiel des Brenner-Basistunnels.Die Zukunft der Schiene mit Milliardeninvestitionen verbaut?

Cost overrun has become a major issue regarding infrastructure projects. Especially in regard to railway infrastructure and the construction of tunnels, building costs are regularly exceeding the original estimates. This article discusses the reasons for this apparently common fact. In addition, reasons for and consequences of misassigned infrastructure investments are pointed out and a concept called "lifetime-based infrastructure controlling" to improve the efficiency of infrastructure projects is outlined. To highlight the importance of this field of research the serious consequences of wrong investments in infrastructure are shown by discussing the current "Brenner-Basistunnel"-project in Austria. (author's abstract

Die tschechische LKW-Maut. Gutachten und Kalkulations-Tabellen: Tabellen zur Berechnung der Kosten der tschechischen Lkw-Maut im Spediteursammelgut- und Teilladungsverkehr

(kein Abstract vorhanden

Addition of Radiotherapy to Immunotherapy: Effects on Outcome of Different Subgroups Using a Propensity Score Matching

Immune checkpoint inhibition (ICI) has been established as successful modality in cancer treatment. Combination concepts are used to optimize treatment outcome, but may also induce higher toxicity rates than monotherapy. Several rationales support the combination of radiotherapy (RT) with ICI as radioimmunotherapy (RIT), but it is still unknown in which clinical situation RIT would be most beneficial. Therefore, we have conducted a retrospective matched-pair analysis of 201 patients with advanced-stage cancers and formed two groups treated with programmed cell death protein 1 (PD-1) inhibitors only (PD1i) or in combination with local RT (RIT) at our center between 2013 and 2017. We collected baseline characteristics, programmed death ligand 1 (PD-L1) status, mutational status, PD-1 inhibitor and RT treatment details, and side effects according to the Common Terminology Criteria for Adverse Events (CTCAE) v.5.0. Patients received pembrolizumab (n = 93) or nivolumab (n = 108), 153 with additional RT. For overall survival (OS) and progression-free survival (PFS), there was no significant difference between both groups. After propensity score matching (PSM), we analyzed 96 patients, 67 with additional and 29 without RT. We matched for different covariates that could have a possible influence on the treatment outcome. The RIT group displayed a trend towards a longer OS until the PD1i group reached a survival plateau. PD-L1-positive patients, smokers, patients with a BMI <= 25, and patients without malignant melanoma showed a longer OS when treated with RIT. Our data show that some subgroups may benefit more from RIT than others. Suitable biomarkers as well as the optimal timing and dosage must be established in order to achieve the best effect on cancer treatment outcome

Treatment monitoring of immunotherapy and targeted therapy using FET PET in patients with melanoma and lung cancer brain metastases: Initial experiences.

Background: Due to the lack of specificity of contrast-enhanced (CE) MRI, the differentiation of progression from pseudoprogression (PsP) following immunotherapy using checkpoint inhibitors (IT) or targeted therapy (TT) may be challenging, especially when IT or TT is applied in combination with radiotherapy (RT). Similarly, for response assessment of RT plus IT or targeted therapy (TT), the use of CE MRI alone may also be difficult. For problem solving, the integration of advanced imaging methods may add valuable information. Here, we evaluated the value of amino acid PET using O-(2-[18F]fluoroethyl)-L-tyrosine (FET) in comparison to CE MRI for these important clinical situations in patients with brain metastases (BM) secondary to malignant melanoma (MM) and non-small cell lung cancer (NSCLC). Methods: From 2015-2018, we retrospectively identified 31 patients with 74 BM secondary to MM (n = 20 with 42 BM) and NSCLC (n = 11 with 32 BM) who underwent 52 FET PET scans during the course of disease. All patients had RT prior to IT or TT initiation (61%) or RT concurrent to IT or TT (39%). In 13 patients, FET PET was performed for treatment response assessment of IT or TT using baseline and follow-up scans (median time between scans, 4.2 months). In the remaining 18 patients, FET PET was used for the differentiation of progression from PsP related to RT plus IT or TT. In all BM, metabolic activity on FET PET was evaluated by calculation of tumor/brain ratios. FET PET imaging findings were compared to CE MRI and correlated to the clinical follow-up or neuropathological findings after neuroimaging. Results: In 4 of 13 patients (31%), FET PET provided additional information for treatment response evaluation beyond the information provided by CE MRI alone. Furthermore, responding patients on FET PET had a median stable clinical follow-up of 10 months. In 10 of 18 patients (56%) with CE MRI findings suggesting progression, FET PET detected PsP. In 9 of these 10 patients, PsP was confirmed by a median stable clinical follow-up of 11 months. Conclusions: FET PET may add valuable information for treatment monitoring in individual BM patients undergoing RT in combination with IT or TT

Treatment Monitoring of Immunotherapy and Targeted Therapy using 18 F-FET PET in Patients with Melanoma and Lung Cancer Brain Metastases: Initial Experiences

Purpose: We investigated the value of O-(2-[18F]fluoroethyl)-L-tyrosine (18F-FET) PET for treatment monitoring of immune checkpoint inhibition (ICI) or targeted therapy (TT) alone or in combination with radiotherapy in patients with brain metastases (BM) since contrast-enhanced MRI often remains inconclusive. Methods: We retrospectively identified 40 patients with 107 BM secondary to melanoma (n = 29 with 75 BM) or non-small cell lung cancer (n = 11 with 32 BM) treated with ICI or TT who had 18F-FET PET (n = 60 scans) for treatment monitoring from 2015-2019. The majority of patients (n = 37; 92.5%) had radiotherapy during the course of disease. In 27 patients, 18F-FET PET was used for the differentiation of treatment-related changes from BM relapse following ICI or TT. In 13 patients, 18F-FET PET was performed for response assessement to ICI or TT using baseline and follow-up scans (median time between scans, 4.2 months). In all lesions, static and dynamic 18F-FET PET parameters were obtained (i.e., mean tumor-to-brain ratios (TBR), time-to-peak values). Diagnostic accuracies of PET parameters were evaluated by receiver-operating-characteristic analyses using the clinical follow-up or neuropathological findings as reference. Results: A TBR threshold of 1.95 differentiated BM relapse from treatment-related changes with an accuracy of 85% (P = 0.003). Metabolic responders to ICI or TT on 18F-FET PET had a significantly longer stable follow-up (threshold of TBR reduction relative to baseline, ≥10%; accuracy, 82%; P = 0.004). Furthermore, at follow-up, time-to-peak values in metabolic responders increased significantly (P = 0.019). Conclusion: 18F-FET PET may add valuable information for treatment monitoring in BM patients treated with ICI or TT

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd