What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging

Not all beta-blockers are equal in the management of long QT syndrome types 1 and 2: higher recurrence of events under metoprolol.

Objectives: The purpose of this study was to compare the efficacy of beta-blockers in congenital long QT syndrome (LQTS). Background: Beta-blockers are the mainstay in managing LQTS. Studies comparing the efficacy of commonly used beta-blockers are lacking, and clinicians generally assume they are equally effective. Methods: Electrocardiographic and clinical parameters of 382 LQT1/LQT2 patients initiated on propranolol (n = 134), metoprolol (n = 147), and nadolol (n = 101) were analyzed, excluding patients <1 year of age at beta-blocker initiation. Symptoms before therapy and the first breakthrough cardiac events (BCEs) were documented. Results: Patients (56% female, 27% symptomatic, heart rate 76 ± 16 beats/min, QTc 472 ± 46 ms) were started on beta-blocker therapy at a median age of 14 years (interquartile range: 8 to 32 years). The QTc shortening with propranolol was significantly greater than with other beta-blockers in the total cohort and in the subset with QTc >480 ms. None of the asymptomatic patients had BCEs. Among symptomatic patients (n = 101), 15 had BCEs (all syncopes). The QTc shortening was significantly less pronounced among patients with BCEs. There was a greater risk of BCEs for symptomatic patients initiated on metoprolol compared to users of the other 2 beta-blockers combined, after adjustment for genotype (odds ratio: 3.95, 95% confidence interval: 1.2 to 13.1, p = 0.025). Kaplan-Meier analysis showed a significantly lower event-free survival for symptomatic patients receiving metoprolol compared to propranolol/nadolol. Conclusions: Propranolol has a significantly better QTc shortening effect compared to metoprolol and nadolol, especially in patients with prolonged QTc. Propranolol and nadolol are equally effective, whereas symptomatic patients started on metoprolol are at a significantly higher risk for BCEs. Metoprolol should not be used for symptomatic LQT1 and LQT2 patients