European Radiology最新文献

Objective: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.

Materials and methods: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.

Results: Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.

Conclusions: This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.

Clinical relevance statement: These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.

Key points: • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Objectives: To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms.

Materials and methods: PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears.

Results: Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I² = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80-0.91) and 0.89 (95% CI 0.83-0.93) for meniscus tear identification and 0.88 (95% CI 0.82-0.91) and 0.84 (95% CI 0.81-0.85) for locating the tears.

Conclusions: AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately.

Clinical relevance statement: Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists.

Key points: • Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. • The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). • AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.

Objective: Our study aimed to explore with cardiovascular magnetic resonance (CMR) the impact of left atrial (LA) and left ventricular (LV) myocardial strain in patients with acute pericarditis and to investigate their possible prognostic significance in adverse outcomes.

Method: This retrospective study performed CMR scans in 36 consecutive patients with acute pericarditis (24 males, age 52 [23-52]). The primary endpoint was the combination of recurrent pericarditis, constrictive pericarditis, and surgery for pericardial diseases defined as pericardial events. Atrial and ventricular strain function were performed on conventional cine SSFP sequences.

Results: After a median follow-up time of 16 months (interquartile range [13-24]), 12 patients with acute pericarditis reached the primary endpoint. In multivariable Cox regression analysis, LA reservoir and LA conduit strain parameters were all independent determinants of adverse pericardial diseases. Conversely, LV myocardial strain parameters did not remain an independent predictor of outcome. With receiving operating characteristics curve analysis, LA conduit and reservoir strain showed excellent predictive performance (area under the curve of 0.914 and 0.895, respectively) for outcome prediction at 12 months.

Conclusion: LA reservoir and conduit mechanisms on CMR are independently associated with a higher risk of adverse pericardial events. Including atrial strain parameters in the management of acute pericarditis may improve risk stratification.

Clinical relevance statement: Atrial strain could be a suitable non-invasive and non-contrast cardiovascular magnetic resonance parameter for predicting adverse pericardial complications in patients with acute pericarditis.

Key points: • Myocardial strain is a well-validated CMR parameter for risk stratification in cardiovascular diseases. • LA reservoir and conduit functions are significantly associated with adverse pericardial events. • Atrial strain may serve as an additional non-contrast CMR parameter for stratifying patients with acute pericarditis.

Objectives: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.

Methods: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference.

Results: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01).

Conclusion: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis.

Clinical relevance statement: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other.

Key points: • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

Objectives: We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients.

Methods: The DMV was divided into six segments according to the regional anatomy. The total DMV score (0-18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed.

Results: We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2-6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3-9)) was significantly higher than other groups (p < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV (R = 0.67, p < 0.01), rather than centrum semivole PVS (CS-PVS) (R = 0.17, p = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87-345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively.

Conclusion: The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients.

Clinical relevance statement: The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications.

Key points: • The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

Objectives: To evaluate the technical success and outcomes of renal biopsies performed under magnetic resonance imaging (MRI) using a closed-bore, 1.5-Tesla MRI unit.

Materials and methods: We retrospectively reviewed our institutional biopsy database and included 150 consecutive MRI-guided biopsies for renal masses between November 2007 and March 2020. We recorded age, sex, BMI, tumor characteristics, RENAL nephrometry score, MRI scan sequence, biopsy technique, complications, diagnostic yield, pathologic outcome, and follow-up imaging. Univariate logistic regression was used to assess the association between different parameters and the development of complications. McNemar's test was used to assess the association between paired diagnostic yield measurements for fine-needle aspiration and core samples.

Results: A total of 150 biopsies for 150 lesions were performed in 150 patients. The median tumor size was 2.7 cm. The median BMI was 28.3. The lesions were solid, partially necrotic/cystic, and predominantly cystic in 137, eight, and five patients, respectively. Image guidance using fat saturation steady-state free precession sequence was recorded in 95% of the biopsy procedures. Samples were obtained using both fine-needle aspiration (FNA) and cores in 99 patients (66%), cores only in 40 (26%), and FNA only in three (2%). Tissue sampling was diagnostic in 144 (96%) lesions. No major complication developed following any of the biopsy procedures. The median follow-up imaging duration was 8 years and none of the patients developed biopsy-related long-term complication or tumor seeding.

Conclusions: MRI-guided renal biopsy is safe and effective, with high diagnostic yield and no major complications.

Clinical relevance statement: Image-guided renal biopsy is safe and effective, and should be included in the management algorithm of patients with renal masses. Core biopsy is recommended.

Key points: • MRI-guided biopsy is a safe and effective technique for sampling of renal lesions. • MRI-guided biopsy has high diagnostic yield with no major complications. • Percutaneous image-guided biopsy plays a key role in the management of patients with renal masses.

Colorectal cancer (CRC) is a significant global health concern. Diagnostic imaging, using different modalities, has a pivotal role in CRC, from early detection (i.e., screening) to follow-up. The role of imaging in CRC screening depends on each country's approach: if an organized screening program is in place, the role of CT colonography (CTC) is limited to the study of either individuals with a positive stool test unwilling/unable to undergo colonoscopy (CC) or in patients with incomplete CC. Although CC is the most common modality to diagnose CRC, CRC can be also incidentally detected during a routine abdominal imaging examination or at the emergency room in patients presenting with intestinal occlusion/subocclusion or perforation. Staging is a crucial aspect of CRC management, guiding treatment decisions and providing valuable prognostic information. An accurate local staging is mandatory in both rectal and colon cancer to drive the appropriate therapeutic workflow. Important limitations of US, CT, and MR in N-staging can be partially solved by FDG PET/CT. Distant staging is usually managed by CT, with MR and FDG PET/CT which can be used as problem-solving techniques. Follow-up is performed according to the general recommendations of the oncological societies. CLINICAL RELEVANCE STATEMENT: It is essential to summarize each phase of colorectal cancer workup, differentiating the management for colon and rectal cancer supported by the main international guidelines and literature data, with the aim to inform the community on the best practice imaging in colorectal cancer. KEY POINTS: • Colorectal cancer is a prevalent disease that lends itself to imaging at each stage of detection and management. • Various imaging modalities can be used as adjuncts to, or in place of, direct visualization methods of screening and are necessary for evaluating metastatic disease. • Reevaluation of follow-up strategies should be considered depending on patients' individual risk of recurrence.