Christopher Kloth

Background: Structural and functional alterations resulting from radial access for cerebral diagnostic catheter angiography might contribute to impaired wrist function. This study aimed to evaluate the impact of the transradial approach for diagnostic cerebral procedures on wrist function, using prospective follow-up assessments. Methods: Wrist function was prospectively assessed by using the Patient-Rated Wrist Evaluation (PRWE) questionnaire at baseline (pre-procedural assessment), as well as at 1-month and 3-month follow-up assessments (PRWE: 0 to 100, with 0 indicating no functional impairment). Association analyses with demographic and clinical parameters were performed using univariate logistic regression models. Results: During the 12-month observation period, 35 patients were enrolled in the study. At baseline, 25 patients (71.4%) reported no wrist impairment, while 10 patients (28.6%) had PRWE scores of up to 51. At the 1-month assessment, seven participants (20%) experienced a worsening in wrist function, reflected by increased PRWE scores. Of these, five patients showed deterioration exceeding the minimum clinically important difference. Another eight participants (22.9%) showed an improvement. A worsening of wrist function between the baseline and 1-month follow-up was not significantly associated with age, sex, prior neurosurgical status, body mass index (BMI), total procedure duration, dose area product, or fluoroscopy time (p > 0.05). At the 3-month follow-up, none of the patients reported any wrist-related impairments. Conclusions: In this exploratory cohort, the use of the transradial approach for cerebral angiography resulted in no wrist-related impairment at the 3-month follow-up. Transient worsening occurred in 20%, including clinically relevant cases, underscoring the need for larger studies with objective outcome measures.

Background: With increasing demand for ambulatory catheter angiography, interest in the transradial approach for diagnostic cerebral procedures has grown markedly. This study aimed to evaluate the effect of the transradial approach for catheter-based diagnostic cerebral procedures on wrist function. Methods: Wrist function was quantified by the Patient-Rated Wrist Evaluation (PRWE) questionnaire. The PRWE score ranged from 0 to 100, with 0 indicating no functional impairment. Association analyses with demographic and clinical parameters were performed using univariate logistic regression models. Results: A total of 88 patients underwent ambulatory diagnostic cerebral angiography during the 15-month observation period; of these, 40 (45%) participated in a telephone interview. Overall, 47.5% (n = 19) of patients reported no wrist impairment (PRWE = 0) after the transradial approach. The remaining 52.5% (n= 21) showed a mean PRWE score of 21.3 ± 22.5 (standard deviation), with a median value of 11.0 and a range from 1.0 to 87.0. Interestingly, univariate logistic regression models revealed a trend towards association between the dichotomized PRWE score and body mass index (p = 0.051). No associations were found with age, sex, prior neurosurgical status, total procedure duration, dose area product, fluoroscopy time, or dominant hand (p > 0.05). Conclusions: Following transradial cerebral catheter angiography, 52.5% of patients reported some degree of wrist impairment at follow-up; whether this represents procedure-related deterioration cannot be determined without baseline values.

CT-based fractional flow reserve (CT-FFR) is a promising noninvasive method for the functional assessment of coronary stenosis. It expands the diagnostic capabilities of coronary CT angiography (cCTA) by providing hemodynamic information and potentially reducing unnecessary invasive coronary angiography examinations This review summarizes current technological developments, study results, and clinical applications of CT-FFR. It also discusses the advantages and disadvantages of various software solutions, including artificial intelligence (AI)-based on-site analyses, and their potential integration into the clinical routine. Studies show that CT-FFR improves diagnostic accuracy compared to cCTA and can optimize patient management. Advances in artificial intelligence and new imaging techniques such as photon-counting CT could further refine CT-FFR and expand its applicability. Despite promising results, further research is needed regarding long-term validation, standardized workflows, and economic feasibility. CT-FFR is a promising complementary tool for assessing the hemodynamic relevance of coronary stenoses. CT-FFR is particularly helpful in complex, long-segment, or consecutive stenosis, because a purely anatomical visual examination is not always sufficient. The combination of technical innovations and AI-assisted image analysis could have the potential to transform noninvasive coronary diagnostics.

Background: Software-guided semi-quantitative analysis of coronavirus disease 2019 (COVID-19) pneumonia in lung computed tomography (CT) datasets for severity assessment. Further to correlate imaging findings with the need of intensive care medicine and clinical parameters. Methods: This single-center retrospective study analyzed 66 consecutive patients (31 females, mean age 64.6±16.2 years) with lung CT datasets from 12/2020 to 05/2021 and confirmed COVID-19 pneumonia. Lung CT datasets were evaluated using a semi-quantitative software for segmentation and quantification. Correlation with underlying diseases, laboratory parameters and further course were assessed, including intubation and need for intensive care. Results: Total lung volume was 3,903.65±1,185.67 mL, mean volume of opacities was 866.52±829.29 mL, reflecting 23.54%±21.92% of total lung volume. Volume of high opacities was 186.88±208.15 mL reflecting 0.06%±0.07% of total lung volume. Overall, 12 patients died (18.2%), 10 patients (15.2%) required intubation and in 27 cases (40.9%) intensive care was necessary. In patients who died volume of opacities and high opacities were significantly higher (PConclusions: COVID-19 pneumonia may be semi-quantified using an artificial intelligence (AI)-based software approach. Quantitative methods could provide precise information on the volume of opacities and may allow detecting connections to patient therapy, including the need for intensive care.

Purpose: To investigate lumbar vertebral volumetric bone mineral density (vBMD) from ex vivo opportunistic multi-detector computed tomography (MDCT) scans using different protocols, and compare it to dedicated quantitative CT (QCT) values from the same specimens. Methods: Cadavers from two female donors (ages 62 and 68 years) were scanned (L1–L5) using six different MDCT protocols and one dedicated QCT scan. Opportunistic vBMD was extracted using an artificial intelligence-based algorithm. The vBMD measurements from the six MDCT protocols, which varied in peak tube voltage (80–140 kVp), tube load (72–200 mAs), slice thickness (0.75–1 mm), and/or slice increment (0.5–0.75 mm), were compared to those obtained from dedicated QCT. Results: A strong positive correlation was observed between vBMD from opportunistic MDCT and reference QCT (ρ = 0.869, p < 0.01). Agreement between vBMD measurements from MDCT protocols and the QCT reference standard according to the intraclass correlation coefficient (ICC) was 0.992 (95% confidence interval [CI]: 0.982–0.998). Bland-Altman analysis showed biases ranging from − 12.66 to 8.00 mg/cm³ across the six MDCT protocols, with all data points falling within the respective limits of agreement (LOA) for both cadavers. Conclusion: Opportunistic vBMD measurements of lumbar vertebrae demonstrated reliable consistency ex vivo across various scan parameters when compared to dedicated QCT.

Background: The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting. Methods: Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected. Exams were read according to the PI-RADSv2.1 protocol and then assessed by an AI algorithm. Diagnostic accuracy for PCa of both human and AI readings were calculated using MR-guided ultrasound fusion biopsy as the gold standard. Results: Analysis of 91 patients resulted in 138 target lesions. Median patient age was 67 years (range: 49–82), median PSA at the time of the MRI exam was 8.4 ng/mL (range: 1.47–73.7). Sensitivity and specificity for clinically significant prostate cancer (csPCa, defined as ISUP ≥ 2) were 92%/64% for radiologists vs. 91%/57% for AI detection on patient level and 90%/70% vs. 81%/78% on lesion level, respectively (cut-off PI-RADS ≥ 4). Two cases of csPCa were missed by the AI on patient-level, resulting in a negative predictive value (NPV) of 0.88 at a cut-off of PI-RADS ≥ 3. Conclusions: AI-augmented lesion detection and scoring proved to be a robust tool in a multi-center setting with sensitivity comparable to the radiologists, even outperforming human reader specificity on both patient and lesion levels at a threshold of PI-RADS ≥3 and a threshold of PI-RADS ≥ 4 on lesion level. In anticipation of refinements of the algorithm and upon further validation, AI-detection could be implemented in the clinical workflow prior to human reading to exclude PCa, thereby drastically improving reading efficiency.

Myokardiale Fett-Foci (MFF) sind gutartige Fettablagerungen im Myokardgewebe und treten typischerweise bei Patienten mit tuberöser Sklerose auf. Diese können sowohl im interventrikulären Septum, in den Papillarmuskeln oder der linken bzw. rechten Ventrikelwand auftreten. In der Literatur korrelieren diese myokardialen Fettherde positiv in der Anzahl mit dem Grad der extrakardialen Beteiligung der Grunderkrankung [Adriaensen ME et al. Radiology. 2009;253: 359–63. ; Wildgruber M. et al., Fortschr Röntgenstr 2012; 184: 465–467]. Als wichtigste Differenzialdiagnose muss das Rhabdomyom abgegrenzt werden, welches der häufigere kardiale Tumor bei Patienten mit tuberöser Sklerose ist. Received: 08 July 2024 Accepted after revision: 14 October 2024 Article published online: 14 November 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Background: Before surgical or transcatheter aortic valve implantation (TAVI), coronary status evaluation is required. The role of combined computed coronary tomography angiography (cCTA) and TAVI planning CT in this context is not yet well elucidated. This study assessed whether relevant proximal coronary disease requiring coronary revascularization can be safely detected by combined cCTA and TAVI planning CT, including CT-derived fractional flow reserve (FFR) calculation in patients with severe aortic stenosis. Methods: This study analyzed patients with successful cCTA combined with TAVI planning CT using a 128-slice dual-source scanner. The detection via cCTA of relevant left main stem stenosis (>50%) or proximal coronary artery stenosis (>70%) was compared to invasive coronary angiography (ICA). Results: This study comprised 101 consecutive TAVI patients with a median age of 83 [77–86] years, a median STS score of 3.7 [2.4–6.1] and 54% of whom had known coronary artery disease. Of 15 patients with relevant coronary stenoses, 14 (93.3%) were detected with cCTA, while false positive results were found in 25 patients. Only in patients with previous percutaneous coronary stent implantation (PCI) were false positive rates (11/29) increased. In the subgroup without previous PCI, an improved classification performance of 87.5%, being mainly due to 11.1% false positive classifications, led to a negative predictive value of 98.5%. Conclusions: Combined cCTA and CT-FFR with TAVI planning CT via state-of-the-art scanners and protocols as a one-stop shop can replace routine ICA in patients prior to TAVI due to its safe detection of relevant coronary artery stenosis, although diagnostic performance of cCTA is only reduced in patients with coronary stents.

The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in other domains. This is mainly due to the black box characteristic of most optimized models and the undetermined generalization ability of the trained architectures. The current work tackles both issues in the radiology domain by focusing on developing an effective and interpretable cardiomegaly detection architecture based on segmentation models. The architecture consists of two distinct neural networks performing the segmentation of both cardiac and thoracic areas of a radiograph. The respective segmentation outputs are subsequently used to estimate the cardiothoracic ratio, and the corresponding radiograph is classified as a case of cardiomegaly based on a given threshold. Due to the scarcity of pixel-level labeled chest radiographs, both segmentation models are optimized in a semi-supervised manner. This results in a significant reduction in the costs of manual annotation. The resulting segmentation outputs significantly improve the interpretability of the architecture’s final classification results. The generalization ability of the architecture is assessed in a cross-domain setting. The assessment shows the effectiveness of the semi-supervised optimization of the segmentation models and the robustness of the ensuing classification architecture.