Insights into Imaging最新文献

Objectives: To propose a reliable and standard 3D assessment method to analyze the effect of weight-bearing (WB) status on the location of patella and clarify the diagnostic performance of 3D parameters for recurrent patellar dislocation (RPD) in WB and non-weight-bearing (NWB) conditions.

Methods: Sixty-five knees of RPD patients and 99 knees of controls were included. Eight landmarks, two lines and a coordinate system were defined on 3D bone models of knees based on weight-bearing CT and non-weight-bearing CT. The shift and tilt of patella in three orthogonal axes (X_shift, Y_shift, Z_shift, X_tilt, Y_tilt, Z_tilt) were evaluated.

Results: X_shift and Y_shift were significantly higher, Z_shift, X_tilt and Y_tilt were significantly lower in WB condition than NWB condition (p < 0.001, p < 0.001, p = 0.001, p = 0.002, p = 0.010). In both WB and NWB conditions, X_shift, Y_shift and Z_tilt were significantly higher, and X_tilt was significantly lower in the RPD group than the control group (WB/NWB: p < 0.001/p = 0.002, p < 0.001/p = 0.001, p < 0.001/p < 0.001, p < 0.001/p = 0.009). In WB condition, Z_shift and Y_tilt were significantly higher in the RPD group than the control group (p = 0.011, p < 0.001). Z_tilt had the best diagnostic performance for RPD in both WB and NWB conditions, with AUC of 0.887 (95% CI: 0.828, 0.946) and 0.885 (95% CI: 0.822, 0.947), respectively.

Conclusions: The 3D measurement method reliably and comprehensively reflected the relative spatial position relationship of the patellofemoral joint. It can be applied to the 3D preoperative planning of patellofemoral procedures. In addition, patellofemoral evaluation under the WB condition was essential to detect subtle underlying risk factors for RPD, with axial lateral patellar tilt being the best predictor.

Critical relevance statement: This 3D measurement method under weight-bearing conditions contributes to comprehensively describing the relative spatial position of the patellofemoral joint in a standardized way and can be applied to preoperative evaluation for recurrent patellar dislocation.

Key points: Patellofemoral alignment is a 3D problem, and the accuracy of 2D parameters has been questioned. 3D measurement was reliable and comprehensively reflected relative spatial relationships of the patellofemoral joint. 3D measurements under weight-bearing condition help preoperative evaluation for RPD.

Objective: To determine the value of preoperative CT perfusion (CTP) parameters for prediction of post-revascularization cerebral infarction (post-CI) in adults with moyamoya disease (MMD).

Methods: This retrospective study included 92 adults with MMD who underwent surgical revascularization. Preoperative quantitative CTP parameters, including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to drain (TTD), and transit time to maximum of the residue function (Tmax), along with clinical data, were compared between the groups with and without post-CI. Predictors of post-CI were identified and assessed using multivariable logistic regression and receiver-operating characteristic curve analyses.

Results: Post-CI occurred in 11 patients (12.0%). In univariate analysis, preoperative mean values for CBF, MTT, TTD, Tmax, initial presentation, infarction within the 2 months before surgery, surgical side, and modified Rankin Scale score on admission were associated with post-CI (all p < 0.05). Multivariable logistic regression revealed that the preoperative mean Tmax (OR 2.342, 95% CI: 1.267-4.330, p = 0.007) and infarction within the 2 months before surgery (OR 14.345, 95% CI: 2.108-97.638, p = 0.006) were independent predictors of post-CI. The preoperative mean Tmax produced the largest area under the curve (0.955, 95% CI: 0.914-0.997) with a cutoff of 3.590 s (sensitivity, 100%; specificity, 87.7%).

Conclusions: Adults with MMD are at risk of post-CI when the preoperative mean Tmax is > 3.590 s. Cerebral infarction during the 2 months before revascularization is also a risk factor for post-CI.

Critical relevance statement: Post-CI is a serious complication for adults with MMD following surgical revascularization. The risk of post-CI can be predicted using preoperative CTP parameters, which will assist neurosurgeons with surgical decisions and implementing individualized prophylactic strategies.

Key points: Predicting the risk of post-CI in MMD patients is beneficial to their prognosis. The preoperative mean Tmax was an excellent perfusion parameter for predicting post-CI. Preoperative CTP evaluation can help clinicians make cautious surgical decisions.

Bladder cancer is the 10th most common and 13th most deadly cancer worldwide, with urothelial carcinomas being the most common type. Distinguishing between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is essential due to significant differences in management and prognosis. MRI may play an important diagnostic role in this setting. The Vesical Imaging Reporting and Data System (VI-RADS), a multiparametric MRI (mpMRI)-based consensus reporting platform, allows for standardized preoperative muscle invasion assessment in BCa with proven diagnostic accuracy. However, post-treatment assessment using VI-RADS is challenging because of anatomical changes, especially in the interpretation of the muscle layer. MRI techniques that provide tumor tissue physiological information, including diffusion-weighted (DW)- and dynamic contrast-enhanced (DCE)-MRI, combined with derived quantitative imaging biomarkers (QIBs), may potentially overcome the limitations of BCa evaluation when predominantly focusing on anatomic changes at MRI, particularly in the therapy response setting. Delta-radiomics, which encompasses the assessment of changes (Δ) in image features extracted from mpMRI data, has the potential to monitor treatment response. In comparison to the current Response Evaluation Criteria in Solid Tumors (RECIST), QIBs and mpMRI-based radiomics, in combination with artificial intelligence (AI)-based image analysis, may potentially allow for earlier identification of therapy-induced tumor changes. This review provides an update on the potential of QIBs and mpMRI-based radiomics and discusses the future applications of AI in BCa management, particularly in assessing treatment response. CRITICAL RELEVANCE STATEMENT: Incorporating mpMRI-based quantitative imaging biomarkers, radiomics, and artificial intelligence into bladder cancer management has the potential to enhance treatment response assessment and prognosis prediction. KEY POINTS: Quantitative imaging biomarkers (QIBs) from mpMRI and radiomics can outperform RECIST for bladder cancer treatments. AI improves mpMRI segmentation and enhances radiomics feature extraction effectively. Predictive models integrate imaging biomarkers and clinical data using AI tools. Multicenter studies with strict criteria validate radiomics and QIBs clinically. Consistent mpMRI and AI applications need reliable validation in clinical practice.

Background: To investigate the efficacy of three-compartment restriction spectrum imaging (RSI), diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in the assessment of lymph node metastases (LNM) in rectal cancer.

Methods: A total of 77 patients with rectal cancer who underwent pelvic MRI were enrolled. RSI-derived parameters (f₁, f₂, and f₃), DKI-derived parameters (D_app and K_app), and the DWI-derived parameter (ADC) were calculated and compared using a Mann-Whitney U test or independent samples t-test. Logistic regression (LR) analysis was used to identify independent predictors of LNM status. Area under the receiver operating characteristic curve (AUC) and Delong analysis were performed to assess the diagnostic performance of each parameter.

Results: The LNM-positive group exhibited significantly higher f₁ and K_app levels and significantly lower f₃, D_app, and ADC levels compared to the LNM-negative group (p < 0.05). There was no difference in f₂ levels between the two groups (p = 0.783). LR analysis showed that D_app and K_app were independent predictors of a positive LNM status. AUC and Delong analysis showed that DKI (D_app + K_app) exhibited significantly higher diagnostic efficacy (AUC = 0.908; sensitivity = 87.10%; specificity = 86.96%) than RSI (f₁ + f₃) and DWI (ADC), with AUCs were 0.842 and 0.771 (Z = 2.113, 3.453; p = 0.035, < 0.001, respectively). The AUC performance between RSI and DWI was also statistically significant (Z = 1.972, p = 0.049).

Conclusion: The RSI model is superior to conventional DWI but inferior to DKI in differentiation between LNM-positive and LNM-negative rectal cancers. Further study is needed before it could serve as a promising biomarker for guiding effective treatment strategies.

Critical relevance statement: The three-compartment restriction spectrum imaging was able to differentiate between LNM-positive and LNM-negative rectal cancers with high accuracy, which has the potential to serve as a promising biomarker that could guide treatment strategies.

Key points: Three-compartment restriction spectrum imaging could differentiate lymph node metastases in rectal cancer. Diffusion kurtosis imaging and diffusion-weighted were associated with lymph node metastases in rectal cancer. The combination of different parameters has the potential to serve as a promising biomarker.

The Patient Advisory Group (PAG) of the European Society of Radiology, in collaboration with the European Federation of Radiographer Societies (EFRS), aims to highlight, in this short paper, the important role that communication plays when trying to meet patients' expectations throughout their imaging journey in a radiology department. The interactions with radiography professionals carrying out diagnostic or interventional procedures are critical in supporting high-quality patient care and patients' expectations. The key areas of consideration have been summarised in an easy-to-remember mnemonic: COMMUNICATION. There are different healthcare systems and medical imaging services across Europe, and healthcare providers should be mindful, when setting up new operational procedures, of the need for processes and systems to support the delivery of patient-centred care. At times when new or improved technology is being introduced, such as artificial intelligence applications, telemedicine, robotisation of interventional procedures, and digitised records, the impact on patient-radiographer communication and interactions should be considered. CRITICAL RELEVANCE STATEMENT: Effective communication helps radiographers meet patients' expectations by ensuring clear explanations, reducing anxiety, fostering trust, and improving cooperation during procedures. This enhances patient satisfaction, safety, and the overall quality of care, aligning with professional standards and patient-centred healthcare. KEY POINTS: Patient-centred imaging services are key to meeting patients' demands. Radiography professionals in radiology departments and medical imaging services should always communicate effectively with patients. This ESR-Patient Advisory Group publication attempts to summarise the key areas that should be embedded in patient communication. The 'COMMUNICATION' statement can be used as a reminder to all radiography professionals to work to improve patient-radiographer interactions and provide patient-focused services.

Objectives: Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.

Methods: Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3D CNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (Δz). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics.

Results: Slice extraction using RL was slightly more precise (Δz: RL 1.8 mm (95% CI: [1.6, 2.0]), 2D CNN 2.0 mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1 cm² (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])).

Conclusions: We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine.

Critical relevance statement: Since inflammatory changes in epicardial adipose tissue (EAT) are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings.

Key points: Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis.

Objectives: To develop and externally validate a fully automated diagnostic convolutional neural network (CNN) model for cirrhosis based on liver MRI and serum biomarkers.

Methods: This multicenter retrospective study included consecutive patients receiving pathological evaluation of liver fibrosis stage and contrast-enhanced liver MRI between March 2010 and January 2024. On the training dataset, an MRI-based CNN model was constructed for cirrhosis against pathology, and then a combined model was developed integrating the CNN model and serum biomarkers. On the testing datasets, the area under the receiver operating characteristic curve (AUC) was computed to compare the diagnostic performance of the combined model with that of aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and radiologists. The influence of potential confounders on the diagnostic performance was evaluated by subgroup analyses.

Results: A total of 1315 patients (median age, 54 years; 1065 men; training, n = 840) were included, 855 (65%) with pathological cirrhosis. The CNN model was constructed on pre-contrast T1- and T2-weighted imaging, and the combined model was developed integrating the CNN model, age, and eight serum biomarkers. On the external testing dataset, the combined model achieved an AUC of 0.86, which outperformed FIB-4, APRI and two radiologists (AUC: 0.67 to 0.73, all p < 0.05). Subgroup analyses revealed comparable diagnostic performances of the combined model in patients with different sizes of focal liver lesions.

Conclusion: Based on pre-contrast T1- and T2-weighted imaging, age, and serum biomarkers, the combined model allowed diagnosis of cirrhosis with moderate accuracy, independent of the size of focal liver lesions.

Critical relevance statement: The fully automated convolutional neural network model utilizing pre-contrast MR imaging, age and serum biomarkers demonstrated moderate accuracy, outperforming FIB-4, APRI, and radiologists, independent of size of focal liver lesions, potentially facilitating noninvasive diagnosis of cirrhosis pending further validation.

Key points: This fully automated convolutional neural network (CNN) model, using pre-contrast MRI, age, and serum biomarkers, diagnoses cirrhosis. The CNN model demonstrated an external testing dataset AUC of 0.86, independent of the size of focal liver lesions. The CNN model outperformed aminotransferase-to-platelet ratio index, fibrosis-4 index, and radiologists, potentially facilitating noninvasive diagnosis of cirrhosis.

Hemato-oncologic children form a heterogeneous group with a wide spectrum of ages, malignancy types, and immunosuppression grades during the different phases of their treatment. Immunosuppression is caused by multiple factors, including the malignancy itself, bone marrow suppression secondary to therapy, and wide use of steroids and antibiotics, among others. At the same time, the risk of infections in these patients remains high because of prolonged hospitalizations or the need for long-timing implanted devices between other features. In this context, a pulmonary fungal infection can rapidly turn into a life-threatening condition that requires early diagnosis and appropriate management. This pictorial essay illustrates the main imaging findings detected in chest computed tomography examinations performed in pediatric hemato-oncologic patients with proven pulmonary invasive fungal infections caused by Candida, Aspergillus, or Mucor. In addition, it describes useful clues for limiting differential diagnoses, reviews the literature on pediatric patients, and compares imaging findings in adults and children. CRITICAL RELEVANCE STATEMENT: The main fungal pathogens causing invasive fungal infections (IFI) in hemato-oncologic children are Candida, Aspergillus, and Mucor. This review describes the most frequently affected organs and the most common imaging findings detected in chest CT exams in children with pulmonary IFI. KEY POINTS: To review the main computed tomography imaging findings suggesting pulmonary invasive fungal infection (IFI) in hemato-oncologic children. To describe differences between pediatric and adult patients with proven pulmonary IFI. To provide useful clues for limiting the differential diagnosis of pulmonary IFI in pediatric patients.