Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
Biomarkers, Tumor ; Diagnosis, Computer-Assisted ; Genome ; Genomics ; Humans ; Magnetic Resonance Imaging ; Neoplasms/therapy* ; Phenotype ; Positron-Emission Tomography ; Precision Medicine/methods* ; Radiology/methods* ; Radiology, Interventional/methods* ; Tomography, X-Ray Computed ; Workflow

INTRODUCTIONPeak currents are the final arbiter of defibrillation in patients with ventricular fibrillation (VF). However, biphasic defibrillators continue to use energy in joules for electrical conversion in hopes that their impedance compensation properties will address transthoracic impedance (TTI), which must be overcome when a fixed amount of energy is delivered. However, optimal peak currents for conversion of VF remain unclear. We aimed to determine the role of peak current and optimal peak levels for conversion in collapsed VF patients.

METHODSAdult, non-pregnant patients presenting with non-traumatic VF were included in the study. All defibrillations that occurred were included. Impedance values during defibrillation were used to calculate peak current values. The endpoint was return of spontaneous circulation (ROSC).

RESULTSOf the 197 patients analysed, 105 had ROSC. Characteristics of patients with and without ROSC were comparable. Short duration of collapse < 10 minutes correlated positively with ROSC. Generally, patients with average or high TTI converted at lower peak currents. 25% of patients with high TTI converted at 13.3 ± 2.3 A, 22.7% with average TTI at 18.2 ± 2.5 A and 18.6% with low TTI at 27.0 ± 4.7 A (p = 0.729). Highest peak current conversions were at < 15 A and 15-20 A. Of the 44 patients who achieved first-shock ROSC, 33 (75.0%) received < 20 A peak current vs. > 20 A for the remaining 11 (25%) patients (p = 0.002).

CONCLUSIONFor best effect, priming biphasic defibrillators to deliver specific peak currents should be considered.