Influencing factors on the time to CT in suspected pulmonary embolism: an explorative investigation.

Pulmonary embolism is a potentially fatal condition with increased mortality if anticoagulation is delayed. This study aimed to find influencing factors on the duration from requesting a computed tomography (CT) pulmonary angiography (CTPA) to performing a CTPA in suspected acute pulmonary embolism. In 1849 cases, automatically generated time data were extracted from the radiological information system. The impact of the distance to the scanner, case-related features (sector of patient care, triage), and workload (demand for CTs, performed CTs, available staff, hospital occupancy) were investigated retrospectively using multiple regression. The time to CTPA was shorter in cases from the emergency room (ER) than in inpatients and outpatients at distances below 160 m and 240 m, respectively. While requests from the ER were also performed faster than cases from regular wards (< 180 m), no difference was found between the ER and intensive care units. Compared to "not urgent" cases, the workflow was shorter in "urgent" (- 17%) and "life-threatening" (- 67%) situations. The process was prolonged with increasing demand (+ 5%/10 CTs). The presented analysis identified relevant in-hospital influences on the CTPA workflow, including the distance to the CT together with the sector of patient care, the case triage, and the demand for imaging.

Prostate-specific membrane antigen radioguided surgery with negative histopathology: an in-depth analysis.

PURPOSE: To identify reasons for negative histopathology of specimens from prostate-specific membrane antigen (PSMA) radioguided surgery (PSMA-RGS) in recurrent prostate cancer (PCa) after prostatectomy. METHODS: Of 302 patients who underwent PSMA-RGS, 17 (5.6%) demonstrated a negative histopathology. Preoperative data, PSMA PET, PSMA SPECT, and follow-up information were analyzed retrospectively to differentiate true/false positive (TP/FP) from true/false negative (TN/FN) lesions. RESULTS: The median prostate-specific antigen at PET was 0.4 ng/ml (interquartile range [IQR] 0.3-1.2). Twenty-five index lesions (median short axis 7 mm, IQR 5-8; median long-axis 12 mm, IQR 8-17) had a median SUVmax of 4 (IQR 2.6-6; median PSMA expression score 1, IQR 1-1). Six lesions were TP, twelve were FP, one was TN, and six remained unclear. All TP lesions were in the prostatic fossa or adjacent to the internal iliac arteries. Three suspected local recurrences were FP. All FP lymph nodes were located at the distal external iliac arteries or outside the pelvis. A low PSMA-expressing TN node was identified next to a common iliac artery. Unclear lesions were located next to the external iliac arteries or outside the pelvis. CONCLUSION: In most cases with a negative histopathology from PSMA-RGS, lesions were FP on PSMA PET. Unspecific uptake should be considered in low PSMA-expressing lymph nodes at the distal external iliac arteries or outside the pelvis, especially if no PSMA-positive lymph nodes closer to the prostatic fossa are evident. Rarely, true positive metastases were missed by surgery or histopathology.

DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography.

Purpose: To develop, train, and validate a multiview deep convolutional neural network (DeePSC) for the automated diagnosis of primary sclerosing cholangitis (PSC) on two-dimensional MR cholangiopancreatography (MRCP) images. Materials and Methods: This retrospective study included two-dimensional MRCP datasets of 342 patients (45 years ± 14 [SD]; 207 male patients) with confirmed diagnosis of PSC and 264 controls (51 years ± 16; 150 male patients). MRCP images were separated into 3-T (n = 361) and 1.5-T (n = 398) datasets, of which 39 samples each were randomly chosen as unseen test sets. Additionally, 37 MRCP images obtained with a 3-T MRI scanner from a different manufacturer were included for external testing. A multiview convolutional neural network was developed, specialized in simultaneously processing the seven images taken at different rotational angles per MRCP examination. The final model, DeePSC, derived its classification per patient from the instance expressing the highest confidence in an ensemble of 20 individually trained multiview convolutional neural networks. Predictive performance on both test sets was compared with that of four licensed radiologists using the Welch t test. Results: DeePSC achieved an accuracy of 80.5% ± 1.3 (sensitivity, 80.0% ± 1.9; specificity, 81.1% ± 2.7) on the 3-T and 82.6% ± 3.0 (sensitivity, 83.6% ± 1.8; specificity, 80.0% ± 8.9) on the 1.5-T test set and scored even higher on the external test set (accuracy, 92.4% ± 1.1; sensitivity, 100.0% ± 0.0; specificity, 83.5% ± 2.4). DeePSC outperformed radiologists in average prediction accuracy by 5.5 (P = .34, 3 T) and 10.1 (P = .13, 1.5 T) percentage points. Conclusion: Automated classification of PSC-compatible findings based on two-dimensional MRCP was achievable and demonstrated high accuracy on internal and external test sets.Keywords: Neural Networks, Deep Learning, Liver Disease, MRI, Primary Sclerosing Cholangitis, MR Cholangiopancreatography Supplemental material is available for this article. © RSNA, 2023.

PSMA hybrid imaging in prostate cancer - current applications and perspectives. / PSMA-Hybridbildgebung in der Diagnostik des Prostatakarzinoms ­ aktuelle Anwendungen und Perspektiven.

BACKGROUND: Prostate cancer (PCa) is the most common malignancy in men and the second most common tumor-associated cause of death in the male population in Germany. Prostate-specific membrane antigen (PSMA)-targeted hybrid imaging using positron emission tomography (PET) in combination with CT or MRI represents a comparably new method that gained increasing importance in the diagnostic process of PCa in recent years. METHOD: Current applications of PSMA hybrid imaging were summarized according to the German and European guidelines on PCa. New developments were elaborated based on a literature review of PubMed conducted in 10/22. RESULTS: PSMA-PET/CT demonstrated higher detection rates for metastases in high-risk PCa and recurrent PCa after primary therapy than established imaging methods (CT, MRI, and bone scan). Despite promising results from prospective trials in both scenarios and substantial influence on clinical decision making, data regarding the influence of PSMA-PET on PCa-specific and overall survival are still lacking. Hence, PSMA PET/CT is recommended with a "weak" strength rating in most situations. However, its importance in new treatment options like metastasis-directed therapy or PSMA-radioligand therapy expands the scope of PSMA-PET in the clinical routine. CONCLUSION: PSMA-targeting hybrid imaging represents the most sensitive diagnostic test in several stages of PCa and allows the development of new treatment strategies. Prospective studies are needed to evaluate the influence of PSMA-PET on patient survival. KEY POINTS: · PSMA-PET/CT is superior to conventional imaging in the primary staging of high-risk prostate cancer.. · PSMA hybrid imaging can detect metastases in patients with biochemical recurrence at low PSA values.. · Clinical decision making is frequently influenced by results of PSMA-PET/CT.. CITATION FORMAT: · Koehler D, Berliner C, Shenas F et al. PSMA hybrid imaging in prostate cancer - current applications and perspectives. Fortschr Röntgenstr 2023; 195: 1001 - 1008.

Infectious complications of radiologically placed upper arm ports: A single center analysis.

OBJECTIVES: Infections are common complications in venous access ports. The presented analysis aimed to investigate the incidence, microbiological spectrum, and acquired resistances of pathogens in upper arm port associated infections to provide a decision aid in the choice of therapy. MATERIALS AND METHODS: In total, 2667 implantations and 608 explantations were performed at a high-volume tertiary medical center between 2015 and 2019. In cases with infectious complications (n = 131, 4.9%), procedural conditions and results of microbiological testing were reviewed retrospectively. RESULTS: Of 131 port associated infections (median dwell time 103 days, interquartile range 41-260), 49 (37.4%) were port pocket infections (PPI) and 82 (62.6%) were catheter infections (CI). Infectious complications occurred more often after implantation in inpatients compared to outpatients (P < 0.01). PPI were mainly caused by Staphylococcus aureus (S. aureus, 48.3%) and coagulase-negative staphylococci (CoNS, 31.0%). Other gram-positive and gram-negative species were encountered in 13.8% and 6.9%, respectively. CI were caused less frequently by S. aureus (8.6%) than CoNS (39.7%). Other gram-positive and gram-negative strains were isolated in 8.6% and 31.0%, respectively. Candida species were seen in 12.1% of CI. An acquired antibiotic resistance was detected in 36.0% of all significant isolates, occurring especially in CoNS (68.3%) and gram-negative species (24.0%). CONCLUSIONS: Staphylococci comprised the largest group of pathogens in upper arm port associated infections. However, gram-negative strains and Candida species should also be considered as a cause of infection in CI. Due to the frequent detection of potential biofilm-forming pathogens, port explantation is an important therapeutic measure, especially in severely ill patients. Acquired resistances must be anticipated when choosing an empiric antibiotic treatment.

Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1.

BACKGROUND: Patients with neurofibromatosis type 1 (NF1) develop benign (BPNST), premalignant atypical (ANF), and malignant (MPNST) peripheral nerve sheath tumors. Radiological differentiation of these entities is challenging. Therefore, we aimed to evaluate the value of a magnetic resonance imaging (MRI)-based radiomics machine-learning (ML) classifier for differentiation of these three entities of internal peripheral nerve sheath tumors in NF1 patients. METHODS: MRI was performed at 3T in 36 NF1 patients (20 male; age: 31 ± 11 years). Segmentation of 117 BPNSTs, 17 MPNSTs, and 8 ANFs was manually performed using T2w spectral attenuated inversion recovery sequences. One hundred seven features per lesion were extracted using PyRadiomics and applied for BPNST versus MPNST differentiation. A 5-feature radiomics signature was defined based on the most important features and tested for signature-based BPNST versus MPNST classification (random forest [RF] classification, leave-one-patient-out evaluation). In a second step, signature feature expressions for BPNSTs, ANFs, and MPNSTs were evaluated for radiomics-based classification for these three entities. RESULTS: The mean area under the receiver operator characteristic curve (AUC) for the radiomics-based BPNST versus MPNST differentiation was 0.94, corresponding to correct classification of on average 16/17 MPNSTs and 114/117 BPNSTs (sensitivity: 94%, specificity: 97%). Exploratory analysis with the eight ANFs revealed intermediate radiomic feature characteristics in-between BPNST and MPNST tumor feature expression. CONCLUSION: In this proof-of-principle study, ML using MRI-based radiomics characteristics allows sensitive and specific differentiation of BPNSTs and MPNSTs in NF1 patients. Feature expression of premalignant atypical tumors was distributed in-between benign and malignant tumor feature expressions, which illustrates biological plausibility of the considered radiomics characteristics.