Ultrasound Image Enhancement using CycleGAN and Perceptual Loss

Purpose: The objective of this work is to introduce an advanced framework designed to enhance ultrasound images, especially those captured by portable hand-held devices, which often produce lower quality images due to hardware constraints. Additionally, this framework is uniquely capable of effectively handling non-registered input ultrasound image pairs, addressing a common challenge in medical imaging. Materials and Methods: In this retrospective study, we utilized an enhanced generative adversarial network (CycleGAN) model for ultrasound image enhancement across five organ systems. Perceptual loss, derived from deep features of pretrained neural networks, is applied to ensure the human-perceptual quality of the enhanced images. These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems. Results: Preliminary validation of the framework reveals promising performance metrics. The model generates images that result in a Structural Similarity Index (SSI) score of 0.722, Locally Normalized Cross-Correlation (LNCC) score of 0.902 and 28.802 for the Peak Signal-to-Noise Ratio (PSNR) metric. Conclusion: This work presents a significant advancement in medical imaging through the development of a CycleGAN model enhanced with Perceptual Loss (PL), effectively bridging the quality gap between ultrasound images from varied devices. By training on paired images, the model not only improves image quality but also ensures the preservation of vital anatomic structural content. This approach may improve equity in access to healthcare by enhancing portable device capabilities, although further validation and optimizations are necessary for broader clinical application.Comment: 7 pages, 3 figure

Effect of lurbinectedin on the QTc interval in patients with advanced solid tumors: an exposure–response analysis

Cardiac repolarization; Lurbinectedin; Plasma concentrationRepolarización cardíaca; Lurbinectedina; Concentración plasmáticaRepolarització cardíaca; Lurbinectedina; Concentració plasmàticaPurpose This study assessed the effect of lurbinectedin, a highly selective inhibitor of oncogenic transcription, on the change from baseline in Fridericia’s corrected QT interval (∆QTcF) and electrocardiography (ECG) morphological patterns, and lurbinectedin concentration–∆QTcF (C-∆QTcF) relationship, in patients with advanced solid tumors. Methods Patients with QTcF ≤ 500 ms, QRS < 110 ms, PR < 200 ms, and normal cardiac conduction and function received lurbinectedin 3.2 mg/m2 as a 1-h intravenous infusion every 3 weeks. ECGs were collected in triplicate via 12-lead digital recorder in treatment cycle 1 and 2 and analyzed centrally. ECG collection time-matched blood samples were drawn to measure lurbinectedin plasma concentration. No effect on QTc interval was concluded if the upper bound (UB) of the least square (LS) mean two-sided 90% confidence intervals (CI) for ΔQTcF at each time point was < 20 ms. C-∆QTcF was explored using linear mixed-effects analysis. Results A total of 1707 ECGs were collected from 39 patients (females, 22; median age, 56 years). The largest UB of the 90% CI of ΔQTcF was 9.6 ms, thus lower than the more conservative 10 ms threshold established at the ICH E14 guideline for QT studies in healthy volunteers. C-∆QTcF was better fit by an effect compartment model, and the 90% CI of predicted ΔQTcF at Cmax was 7.81 ms, also below the 10 ms threshold of clinical concern. Conclusions ECG parameters and C-ΔQTcF modelling in this prospective study indicate that lurbinectedin was not associated with a clinically relevant effect on cardiac repolarization.This study was funded by Pharma Mar S.A. and partially funded by the Industrial and Technological Development Center–CDTI (IDI-20150006)

Prognostic precipitation with three liquid water classes in the ECHAM5-HAM GCM

A new parameterization with three prognostic liquid water classes was implemented into the general circulation model (GCM) ECHAM5 with the aerosol module HAM in order to improve the global representation of rain formation in marine stratiform clouds. The additionally introduced drizzle class improves the physical representation of the droplet spectrum and, more importantly, improves the microphysical processes relevant for precipitation formation compared to the standard parameterization. In order to avoid a mismatch of the liquid and ice phase, a prognostic treatment of snow has been introduced too. This has a significant effect on the amount and altitude of ice clouds, which in turn affects not only the in- and outgoing radiation but also the parameterized collection rates. With the introduction of a prognostic precipitation scheme, a more realistic representation of both liquid and ice phase large-scale precipitation is achieved compared to a diagnostic treatment. An encouraging finding is that with the prognostic treatment the increase of the liquid water path in response to anthropogenic aerosols is reduced by about 25 %. Although the total net radiative forcing is decreased from −1.3±0.3 to −1.6±0.3 W m−2 from the control to the prognostic model version, the difference is within the interannual variability. Altogether the results suggest that the treatment of precipitation in global circulation models has not only a significant influence on the phase of clouds and their conversion rates, but also hints towards uncertainties related to a prognostic precipitation scheme.ISSN:1680-7375ISSN:1680-736

Performance of a Triclass Parameterization for the Collision–Coalescence Process in Shallow Clouds

Abstract Focusing on the formation of precipitation in marine stratiform clouds, a two-moment bulk parameterization for three liquid water classes (cloud, drizzle, and rain) is proposed to describe the process of collision–coalescence. Based on the stochastic collection equation and making use of partial moments to improve the physical representation of the shape of the drop size distribution, new rate equations for both number and mass densities are derived using the modified gamma distribution and an adapted collection kernel. Based on observations and spectral model results, the free shape parameters of the modified gamma distribution of each class are determined closing the set of equations. Idealized simulations of the new parameterization compare well to other studies and prove that the closure assumptions are appropriate, especially as the rate equations are invariant under time-stretching transformations—a key property of the stochastic collection equation. The framework of the one-dimensional kinematic cloud model is used to compare the new bulk parameterization to existing formulations and a spectral model. These results show a good agreement, especially in the sensitivity to the aerosol background concentration and the general development for updraft velocities relevant for shallow clouds. Furthermore, as drizzle dominates the formed precipitation for stratocumulus it becomes a pure transition class for more convective type clouds. The analysis reveals a different quantitative behavior of the various parameterizations in the drizzle regime, which is of special importance for precipitating stratocumulus clouds.</jats:p

Key Advances in the Systemic Therapy for Soft Tissue Sarcomas: Current Status and Future Directions

Soft tissue sarcomas (STS) represent a heterogeneous group of diverse neoplasms of mesenchymal origin. Once relapsed from standard therapy, STS patients have limited treatment options especially those that present with advanced or metastatic disease. In this review article, we highlight recent clinical data that led to the US Food and Drug Administration (FDA) approval of pazopanib (Votrient®) for STS and regorafenib (Stivarga®, BAY 73-4506) in gastrointestinal stromal tumours. We also review ongoing safety/efficacy data for trabectedin (Yondelis®, ET-743), and data from clinical studies of ridaforolimus (AP23573; MK-8669) and palifosfamide (ZIO-201). We provide a list of some promising ongoing trials in soft tissue sarcomas including first line studies of TH-302 and trabectedin. Finally, our article delves into recent advances in our understanding of the molecular pathogenesis of STS and novel therapies that might be explored as treatment options for specific STS histologies.</jats:p

Key Advances in the Systemic Therapy for Soft tissue Sarcomas: Current Status and Future Directions

Soft tissue sarcomas (STS) represent a heterogeneous group of diverse neoplasms of mesenchymal origin. Once relapsed from standard therapy, STS patients have limited treatment options especially those that present with advanced or metastatic disease. In this review article, we highlight recent clinical data that led to the US Food and Drug Administration (FDA) approval of pazopanib (Votrient®) for STS and regorafenib (Stivarga®, BAY 73-4506) in gastrointestinal stromal tumours. We also review ongoing safety/efficacy data for trabectedin (Yondelis®, ET-743), and data from clinical studies of ridaforolimus (AP23573; MK-8669) and palifosfamide (ZIO-201). We provide a list of some promising ongoing trials in soft tissue sarcomas including first line studies of TH-302 and trabectedin. Finally, our article delves into recent advances in our understanding of the molecular pathogenesis of STS and novel therapies that might be explored as treatment options for specific STS histologies