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

Supplementary Material for: Does Physical Activity Improve Quality of Life in Cancer Patients Undergoing Chemotherapy?

<b><i>Background:</i></b> Improved cancer treatments have resulted in prolonged survival. Nevertheless, tumor symptoms and side effects still compromise physical activity and quality of life (QoL). <b><i>Patients and Methods:</i></b> We conducted an anonymous survey among cancer patients undergoing chemotherapy using standardized questionnaires: the ‘<i>Freiburger Fragebogen zur körperlichen Aktivität</i>' (Freiburg Questionnaire on Physical Activity) and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30. Two main questions were addressed: were there differences (1) in physical activity and QoL between patients who do not believe that sport could improve their QoL and those who believe it could (group A vs. B); and (2) in QoL between patients with a total activity (TA) < 18 metabolic equivalent of task (MET) h/week and those with a TA of ≥ 18 MET h/week (group C vs. D)? <b><i>Results:</i></b> 276 of 400 questionnaires were completed. Groups A and B were balanced in terms of baseline characteristics. Group A suffered significantly more from fatigue and pain; group B reported higher levels of global health status (GHS) and TA. Groups C and D differed in gender distribution, age, and educational background. Group D had significantly higher levels of GHS, group C suffered more from fatigue, pain, and appetite loss. <b><i>Conclusion:</i></b> Physical activity correlates with a better QoL of cancer patients undergoing chemotherapy