European Radiology Experimental最新文献

Background: Asbestosis, a rare pneumoconiosis marked by diffuse pulmonary fibrosis, arises from prolonged asbestos exposure. Its diagnosis, guided by the Helsinki criteria, relies on exposure history, clinical findings, radiology, and lung function. However, interobserver variability complicates diagnoses and financial compensation. This study prospectively validated the sensitivity of an AI-driven assessment for asbestosis compensation in the Netherlands. Secondary objectives included evaluating specificity, accuracy, predictive values, area under the curve of the receiver operating characteristic (ROC-AUC), area under the precision-recall curve (PR-AUC), and interobserver variability.

Materials and methods: Between September 2020 and July 2022, 92 adult compensation applicants were assessed using both AI models and pulmonologists' reviews based on Dutch Health Council criteria. The AI model assigned an asbestosis probability score: negative (< 35), uncertain (35-66), or positive (≥ 66). Uncertain cases underwent additional reviews for a final determination.

Results: The AI assessment demonstrated sensitivity of 0.86 (95% confidence interval: 0.77-0.95), specificity of 0.85 (0.76-0.97), accuracy of 0.87 (0.79-0.93), ROC-AUC of 0.92 (0.84-0.97), and PR-AUC of 0.95 (0.89-0.99). Despite strong metrics, the sensitivity target of 98% was unmet. Pulmonologist reviews showed moderate to substantial interobserver variability.

Conclusion: The AI-driven approach demonstrated robust accuracy but insufficient sensitivity for validation. Addressing interobserver variability and incorporating objective fibrosis measurements could enhance future reliability in clinical and compensation settings.

Relevance statement: The AI-driven assessment for financial compensation of asbestosis showed adequate accuracy but did not meet the required sensitivity for validation.

Key points: We prospectively assessed the sensitivity of an AI-driven assessment procedure for financial compensation of asbestosis. The AI-driven asbestosis probability score underperformed across all metrics compared to internal testing. The AI-driven assessment procedure achieved a sensitivity of 0.86 (95% confidence interval: 0.77-0.95). It did not meet the predefined sensitivity target.

Background: Reducing contrast agent and radiation exposure is paramount for pediatric patients. Digital variance angiography (DVA) might address this need by increasing the contrast-to-noise ratio (CNR).

Materials and methods: A total of 132 raw iodinated contrast angiograms of 10 children (mean age: 12 years) who had endovascular procedures for arteriovenous malformations were retrospectively processed for DVA analysis. The CNR of the DVA and digital subtraction angiography (DSA) images was calculated. The visual image quality was assessed using a four-point Likert scale. Statistical analyses were based on the Wilcoxon signed-rank test and one-sample t-test.

Results: The CNR was determined and compared for 3,318 regions of interest in 132 image pairs in four anatomical regions (upper limb (UL), lower limb (LL), head and neck (HN), and chest (CH)). DVA outperformed DSA, with a median overall CNR_DVA/CNR_DSA ratio of 2.00 (UL, 1.83; LL, 1.71; HN, 2.06; CH, 2.23; all p < 0.001). The paired Likert scale scores were significantly different from zero in 50% of the comparisons (in all large vessel and small vessel groups, except in the UL region, and the tissue blush group in the LL and HN regions), indicating a superiority of DSA, but the difference was clinically negligible.

Conclusion: Although DVA improved CNR, it did not surpass DSA in subjective image quality, possibly due to motion artifacts and the high baseline quality of DSA images.

Relevance statement: The enhanced CNR seen with DVA indicates a potential quality reserve that could be exploited to safely reduce contrast agent dose and radiation risks in pediatric patients, who are more susceptible to the long-term effects of radiation.

Key points: In previous studies, DVA was superior to DSA due to a higher CNR and better image quality. However, no evidence was available regarding pediatric endovascular procedures. While DVA exhibited a marked advantage in terms of the CNR, it was unable to surpass DSA in terms of visual assessment. The enhanced CNR seen with DVA indicates a potential quality reserve that could be exploited to safely reduce contrast agent dose and radiation risks in pediatric patients.

Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension.

Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and "model for end-stage liver disease-sodium" (MELD-Na) score) and fibrosis/portal hypertension (Fibrosis-4 (FIB-4) Score, liver stiffness measurement (LSM), hepatic venous pressure gradient (HVPG), platelet count (PLT), and spleen volume.

Results: We included 197 subjects, aged 54.9 ± 13.8 years (mean ± standard deviation), 111 males (56.3%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) (p ≤ 0.001). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) (p ≤ 0.001), but showed no difference between CLD groups (p = 0.999). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume (ρ ranging from -0.27 to -0.40), and directly with PLT (ρ = 0.36). TVVR and PVVR showed similar but weaker correlations.

Conclusion: Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity.

Relevance statement: Hepatic vessel volumetry demonstrates differences between healthy liver and chronic liver disease stages, potentially serving as a non-invasive imaging biomarker.

Key points: Deep learning-based vessel analysis can provide automated quantification of hepatic vascular changes across healthy liver and chronic liver disease stages. Automated quantification of hepatic vasculature shows significantly reduced hepatic vascular volume in advanced chronic liver disease compared to non-advanced disease and healthy liver. Decreased hepatic vascular volume, particularly in the hepatic venous system, correlates with markers of liver dysfunction, fibrosis, and portal hypertension.

Background: Dual-energy computed tomography (DECT) detects monosodium urate (MSU) deposits in joints. However, the correlation between coronary atherosclerosis phenotypes and MSU-positive lesions in the cardiovascular system remains unclear. We investigated the correlation between coronary MSU-positive plaques on unenhanced DECT with the coronary atherosclerosis profile at coronary CT angiography.

Methods: One hundred fifty rheumatologic patients were prospectively enrolled. Sixty of them underwent unenhanced DECT and 128-row DECT coronary angiography. Analysis included CAD-RADS stenosis severity, high-risk plaque (HRP) phenotypes, and coronary artery calcium (CAC) score.

Results: Of 60 patients, with a mean age of 63.7 years, including 7 females (11.7%), 37 had gout (61.7%), 9 had hyperuricemia (15%), and 14 had other rheumatologic diseases (23.3%). At DECT, 11 (18.3%) had coronary MSU-positive lesions totaling 24 lesions (left anterior descending, 12; right coronary artery, 10; circumflex, 1; left main, 1). HRP phenotypes were identified in 14 of 60 patients (23.3%). The prevalence of HRP was higher in MSU-positive than MSU-negative patients (63.3% versus 14.2%; p = 0.003; odds ratio 9.91; 95% confidence interval [CI]: 2.30-48.41). CAD-RADS and CAC scores correlated with the number of MSU-positive lesions (ρ = 0.412; 95% CI: 0.167-0.609; p < 0.001) and ρ = 0.412; 95% CI: 0.169-0.609; p < 0.001). None of the major cardiovascular risk factors (smoking, hypertension, dyslipidemia, or diabetes) was associated with MSU-positive lesions.

Conclusion: We found an association between coronary MSU-positive lesions and HRP-phenotypes, as well as a correlation with stenosis severity and calcium burden. MSU-positive lesions may serve as an unenhanced DECT-derived biomarker of increased cardiovascular risk.

Relevance statement: The detection of coronary MSU-positive lesions by DECT could indicate an increased likelihood of HRP phenotypes. These findings suggest their potential as imaging biomarkers for cardiovascular risk, using unenhanced spectral DECT scans or photon-counting CT.

Key points: Identifying gout patients with increased cardiovascular risk remains challenging. Coronary MSU-positive lesions detected on unenhanced DECT may be associated with HRP features on coronary computed tomography angiography. MSU-positive lesions could serve as biomarkers for cardiovascular risk in gout patients.

Background: This study investigated the correlation between decreased internal iliac arterial blood pressure (IIABP) and blood perfusion volume within the vesical artery region during double-balloon-occluded arterial infusion chemotherapy (D-BOAI) for invasive bladder cancer, utilizing two-dimensional perfusion angiography (2D-PA).

Materials and methods: Sixteen patients were enrolled in this study. A double-balloon catheter was positioned into the contralateral internal iliac artery via the femoral artery approach. The catheter's side hole, located between the distal and proximal balloons, facilitated angiographic visualization of the contrast medium (CM) flow into the urinary bladder. Hemodynamic analysis of the CM in the pelvic arteries during D-BOAI was conducted using 2D-PA. Regions of interest (ROIs) were delineated at the side hole (A) as the outflow point for CM and in the vesical artery region (B). The ratio of the area under the curve (AUC) of CM at each ROI (C = B/A) was computed. The decrease in IIABP (D) following balloon occlusion was recorded at the catheter side hole. The relationship between C and D was analyzed using Pearson's product-moment correlation coefficient.

Results: A total of 32 sides from 16 patients were analyzed. The mean C value was 0.39, and the mean D value was 55.2 mmHg, while the mean IIABP post-occlusion measured 66.2 mmHg. A significant positive correlation between C and D was identified, with a correlation coefficient of 0.704 (p < 0.001).

Conclusion: The findings demonstrate a significant positive correlation between blood perfusion volume in the vesical artery region and the reduction in IIABP following balloon occlusion.

Relevance statement: Our results suggest that decreased IIABP after balloon occlusion could result in high concentrations of anticancer drugs in the vesical artery region, and favorable local tumor control in bladder cancer.

Key points: D-BOAI chemotherapy can treat invasive bladder cancer without radical cystectomy. IIABP and flow persist to some extent even following double balloon occlusion. 2D-PA allowed quantitative evaluation of vesical arterial perfusion volume in D-BOAI.

Early accurate diagnosis of osseous and soft tissue injuries following acute knee trauma is crucial for guiding clinical management and preventing chronic instability. Radiography is the appropriate first imaging test applied to detect traumatic osseous injuries. CT is indicated based on clinical symptoms and radiographic concordance. In this acute phase, soft tissue injuries are often clinically overlooked due to swelling and restricted motion, which significantly limit comprehensive physical examination. Moreover, both x-ray and conventional CT imaging are insufficient for addressing this issue due to their limited soft tissue contrast resolution. If clinical suspicion of soft tissue injury persists, an MRI will be performed at a later stage. This may lead to undesirable delays in diagnosis and treatment, thereby potentially impacting patient outcomes. Photon-counting detector CT (PCD-CT) offers enhanced, integrated diagnostic possibilities. The use of spectral imaging data, including color-coded virtual non-calcium (VNCa) images, enables the detection of bone marrow edema (BME) and visualization of key stabilizing soft tissue structures, which may assist emergency department clinicians in determining initial treatment, follow-up, and the need for additional imaging. This technical note illustrates the integral use of ultra-high resolution spectral PCD-CT in a case of a knee injury following an alpine skiing accident. RELEVANCE STATEMENT: The integration of photon-counting detector computed tomography with spectral imaging in acute knee trauma enhances visualization of osseous and soft tissue structures, improving diagnostic accuracy. It may optimize early triage and guide initial treatment for soft tissue injuries. KEY POINTS: Photon-counting detector CT (PCD-CT) enables comprehensive fracture, edema, and soft tissue assessment. Case-based notable correlation between injuries suspected on color-coded spectral imaging and MRI. Photon-counting detector CT (PCD-CT) may enhance early clinical decision-making in knee trauma.

Background: Technical advances in magnetic resonance imaging (MRI) acquisition and reconstruction have improved the visualization of anatomical structures such as cranial nerves (CNs) and enabled subsequent three-dimensional (3D) models for use in clinical care. However, a comprehensive overview of indications and techniques is lacking. The current study aimed to comprehensively describe and assess the techniques and applications used in MRI-based 3D modeling of CNs.

Methods: We conducted a systematic review of articles published in Medline, Embase, and Scopus databases on clinically applied MRI-based 3D models of CNs up to December 2023. We modified the Quality Assessment Tool for Diagnostic Accuracy Studies to assess the risk of bias.

Results: We analyzed 37 studies presenting virtual 3D models of CNs II, III, and V-X in proximity to pathologies in the head and neck area and intracranial, including vestibular schwannoma, skull base tumors, cerebellopontine angle tumors, and neurovascular compression syndrome. Certain studies explored alternative visualization modalities, including printed and augmented reality models. The creation of these 3D models involved the utilization of several MRI sequences and segmentation tools. The models demonstrate potential benefits for preoperative planning, intraoperative decision-making, and patient counseling.

Conclusion: MRI-specific sequences and segmentation techniques render CNs in 3D models, helping before and during surgery.

Relevance statement: MRI-based 3D models of cranial nerves help surgeons before and during surgery and enhance patient understanding of the procedure and its risks. Wider clinical adoption requires an established workflow, technical expertise, and collaboration to ensure accessibility and knowledge sharing.

Key points: 3D modeling of cranial nerves is a promising tool for preoperative planning, surgery, and patient-doctor communication. Data heterogeneity and small sample sizes hinder definitive conclusions about the best MRI techniques and segmentation protocols for 3D visualization of cranial nerves. Adopting MRI-based 3D models widely needs a set workflow, technical skills, and team collaboration.

Background: Transarterial radioembolisation (TARE) is a treatment for liver malignancies, involving the injection of radioactive microspheres in the hepatic artery (HA). Tumour-to-nontumour uptake varies among patients, possibly influenced by patient-specific blood flow profiles. To examine the impact of HA blood flow rate and high microsphere dosages on microsphere distribution in normal liver parenchyma, ex vivo magnetic resonance imaging (MRI)-guided machine perfusion experiments were conducted in porcine livers.

Materials and methods: Porcine livers were subjected to oxygenated normothermic machine perfusion at three HA flow rates (0.02, 0.15, and 0.22 mL/min/g liver tissue; n = 3 per condition). Five fractions of 250 mg nonradioactive ¹⁶⁵Ho-loaded microspheres were administered to n = 9 livers, and four additional fractions of 1,000 mg to n = 6 livers. Dynamic contrast-enhanced and Ho-sensitive T2*-weighed MR scans were acquired to extract perfusion rates, fictive dose maps, and homogeneity indices (HI).

Results: Microsphere distribution correlated moderately with perfusion rate at low HA flow rate (r = 0.611), and very strongly at higher HA flow rates (r = 0.977 and 0.951 for 0.15 and 0.22 mL/min/g, respectively). Homogeneity increased with increasing flow rates, with HIs ranging from 3.68-4.72 at low, to 2.01-2.66 at medium, and 1.60-2.36 at high HA flow rate. HI decreased with higher microsphere concentrations, though distribution patterns remained unchanged.

Conclusion: In our ex vivo model, higher HA flow rates resulted in more homogeneous microsphere distributions. The impact on tumourous tissue needs further investigation to determine whether pre-TARE HA blood flow measurements could improve microsphere distribution predictions.

Relevance statement: Mapping of the hepatic arterial blood flow rate before transarterial radioembolisation and adjusting the treatment accordingly may help to improve outcomes for patients with liver cancer.

Key points: Parameters influencing microsphere distribution were studied in MRI-perfused healthy porcine livers. Higher hepatic arterial blood flow rates led to more homogeneous microsphere distributions. Administering large numbers of microspheres did not alter microsphere distribution patterns. Impact on tumour tissue should be further investigated.

Background: Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-weighted magnetic resonance imaging (T2W-MRI) for automated tumor segmentation and differentiation between SIP and SIP-SCC.

Methods: We conducted a retrospective analysis of 174 patients diagnosed with SIPs, who were divided into a training cohort (n = 121) and a testing cohort (n = 53). Three DL architectures were utilized to train automated segmentation models for endoscopic and T2W-MRI images. DL scores predicting SIP-SCC were generated using DenseNet121 from both modalities and combined to create a dual-modality DL nomogram. The diagnostic performance of the DL models was assessed alongside two radiologists, evaluated through the area under the receiver operating characteristic curve (AUROC), with comparisons made using the Delong method.

Results: In the testing cohort, the FCN_ResNet101 and VNet exhibited superior performance in automated segmentation, achieving mean dice similarity coefficients of 0.95 ± 0.03 for endoscopy and 0.93 ± 0.02 for T2W-MRI, respectively. The dual-modality DL nomogram based on automated segmentation demonstrated the highest predictive performance for SIP-SCC (AUROC 0.865), outperforming the radiology resident (AUROC 0.672, p = 0.071) and the attending radiologist (AUROC 0.707, p = 0.066), with a trend toward significance. Notably, both radiologists improved their diagnostic performance with the assistance of the DL nomogram (AUROCs 0.734 and 0.834).

Conclusion: The DL framework integrating endoscopy and T2W-MRI offers a fully automated predictive tool for SIP-SCC.

Relevance statement: The integration of endoscopy and T2W-MRI within a well-established DL framework enables fully automated prediction of SIP-SSC, potentially improving decision-making for patients with suspicious SIP.

Key points: Detecting the transformation of SIP into SIP-SCC before surgery is both critical and challenging. Endoscopy and T2W-MRI were integrated using DL for predicting SIP-SCC. The dual-modality DL nomogram outperformed two radiologists. The nomogram may improve decision-making for patients with suspicious SIP.

Severity scores, which often refer to the likelihood or probability of a pathology, are commonly provided by artificial intelligence (AI) tools in radiology. However, little attention has been given to the use of these AI scores, and there is a lack of transparency into how they are generated. In this comment, we draw on key principles from psychological science and statistics to elucidate six human factors limitations of AI scores that undermine their utility: (1) variability across AI systems; (2) variability within AI systems; (3) variability between radiologists; (4) variability within radiologists; (5) unknown distribution of AI scores; and (6) perceptual challenges. We hypothesize that these limitations can be mitigated by providing the false discovery rate and false omission rate for each score as a threshold. We discuss how this hypothesis could be empirically tested. KEY POINTS: The radiologist-AI interaction has not been given sufficient attention. The utility of AI scores is limited by six key human factors limitations. We propose a hypothesis for how to mitigate these limitations by using false discovery rate and false omission rate.