Objective: This single-centre retrospective study aims to determine the incidence of therapy-induced surgical benefit in patients with non-metastatic gastrointestinal stromal tumour (GIST) treated with neoadjuvant tyrosine kinase inhibitors (TKI) and evaluate whether this can be predicted by radiological response criteria. Methods: Thirty-nine non-metastatic GIST patients were treated with neoadjuvant TKI treatment, followed by curative-intended surgery, and monitored using contrast-enhanced computed tomography (CE-CT). Surgical benefit was independently assessed by two surgical oncologists and was defined by de-escalation of surgical strategy or reduced surgical complexity. Radiological response between baseline and the last preoperative scan was determined through RECIST 1.1, Choi and volumetric criteria. Results: In this patient cohort, median neoadjuvant treatment interval was 8.3 (IQR, 3.9–10.6) months. Surgical benefit was gained in 22/39 patients. When comparing radiological criteria to findings on surgical benefit, accuracy, sensitivity, and specificity for RECIST 1.1 (90 %, 100.0 % and 82 %), Choi (64 %, 24 %, and 96 %) and volumetry (95 %, 100.0 %, and 91 %) were calculated. In 30/39 patients, temporal changes in tumour size over the course of treatment was assessed. Tumour volume reduced significantly in the surgical-benefit group compared to the non-benefit group (72 % vs. 25 %, p < 0.01) within three months. 14/19 surgical-benefit patients had an initial volume reduction above 66 %, after which volume reduced slightly with a median 3.1 % (IQR, 2.1–7.8 %) reduction. Conclusion: Surgical benefit after neoadjuvant treatment was achieved in 56 % of patients and was most accurately reflected by size-based response criteria. In patients with therapy-induced surgical benefit, nearly all treatment-induced volume reductions were achieved within three months.

Objective: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. Results: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. Conclusions: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. Key Points: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR).DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images.DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

In the critical setting of a trauma team activation, team composition is crucial information that should be accessible at a glance. This calls for a technological solution, which are widely available, that allows access to the whereabouts of personnel. This diversity presents decision makers and users with many choices and considerations. The aim of this review is to give a comprehensive overview of available real-time person identification techniques and their respective characteristics. A systematic literature review was performed to create an overview of identification techniques that have been tested in medical settings or already have been implemented in clinical practice. These techniques have been investigated on a total of seven characteristics: costs, usability, accuracy, response time, hygiene, privacy, and user safety. The search was performed on 11 May 2020 in PubMed and the Web of Science Core Collection. PubMed and Web of Science yielded a total n = 265 and n = 228 records, respectively. The review process resulted in n = 23 included records. A total of seven techniques were identified: (a) active and (b) passive Radio-Frequency Identification (RFID) based systems, (c) fingerprint, (d) iris, and (e) facial identification systems and infrared (IR) (f) and ultrasound (US) (g) based systems. Active RFID was largely documented in the included literature. Only a few could be found about the passive systems. Biometric (c, d, and e) technologies were described in a variety of applications. IR and US techniques appeared to be a niche, as they were only spoken of in few (n = 3) studies.

Background: The glymphatic system (GS) is a recently discovered waste clearance system in the brain. Purpose: To evaluate the most promising magnetic resonance imaging (MRI) sequence(s) and the most optimal sequence parameters for glymphatic MRI (gMRI) 4–24 h after administration of gadolinium-based contrast agent (GBCA). Material and Methods: Multiple literature databases were systematically searched for articles regarding gMRI or MRI of the perilymph in the inner ear until 11 May 2020. All relevant MRI sequence parameters were tabulated for qualitative analysis. Their potential was assessed based on detection of low dose GBCA, primarily measured as signal intensity (SI) ratio. Results: Thirty articles were included in the analysis. Three-dimensional fluid attenuated inversion recovery (3D-FLAIR), 3D Real Inversion Recovery (3D-Real IR), and multiple 3D T1-weighted gradient echo sequences were used. In perilymph, 3D-FLAIR with a TE of at least 400 ms yielded the highest SIRs. In the qualitative analysis of inner ear studies using 3D-FLAIR, TR was in the range of 4400–10,000 ms, TI 1500–2600 ms, refocusing flip angle (rFA) (range 120°–180°), and echo train length (ETL) 23–173. In the gMRI studies, quantitative analysis was not possible. In the qualitative analysis, 3D-FLAIR was used in the majority (8/12) of the studies, usually with TR 4800–9000 ms, TI 1650–2500 ms, TE 311–561 ms, rFA 90°–120°, and ETL 167–278. Conclusion: Long TE 3D-FLAIR is the most promising sequence for detection of low-dose GBCA in the GS. Clinical and/or phantom studies on other MRI parameters are needed for further optimization of gMRI.