Insights into Imaging最新文献

Objectives: This study aimed to assess the impact of the photon-counting detector (PCD)-CT-based virtual monoenergetic image (VMI) reconstruction keV levels on CT-based fractional flow reserve (CT-FFR), compared to the energy-integrating detector (EID)-CT.

Methods: Patients undergoing clinically indicated coronary CT angiography (CCTA) on an EID-CT were prospectively enrolled for a research CCTA on a PCD-CT within 30 days. PCD-CT datasets were reconstructed at VMI levels of 45, 55, 70, and 90 keV. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm by two readers. CT-FFR ≤ 0.80 was considered hemodynamically significant.

Results: A total of 20 patients (63.3 ± 8.8 years; 13 men (65%) were included. Median CT-FFR values in the per-vessel analysis for PCD-CT scans were 0.86 (0.81-0.92) for 45 keV, 0.87 (0.80-0.93) for 55 keV, 0.85 (0.79-0.92) for 70 keV and 0.82 (0.76-0.89) for 90 keV, and 0.86 (0.71-0.93) for EID-CT. Comparison among different VMIs showed significant differences only for 45 vs. 90 keV (p < 0.001), and 55 vs. 90 keV (p < 0.001). No significant differences were found in the pairwise comparison between any VMI and EID-CT (all p > 0.05). PCD-CT at 70 keV showed the highest correlation (r = 0.83, p < 0.001), agreement (ICC: 0.90 (0.84-0.94)), and the lowest bias (mean bias -0.01; limits of agreement, 0.84/0.94) when compared to EID-CT.

Conclusion: VMI reconstructions showed significant influence on CT-FFR values only at the extreme levels of the spectrum, while no significant differences were found in comparison with EID-CT. VMI at 70 keV demonstrates the highest correlation and agreement, with the lowest bias compared to EID-CT.

Critical relevance statement: Evidence on novel spectral photon-counting detector (PCD)-CT's impact on CT-fractional flow reserve (FFR) is limited; our results demonstrate the feasibility of CT-FFR using PCD-CT, showing no significant differences between various virtual monoenergetic images and energy-integrating detector (EID)-CT values KEY POINTS: The impact of spectral photon-counting detector (PCD)-CT on CT-derived fractional flow reserve (CT-FFR) is unclear. Spectral PCD-CT-based CT-FFR is feasible, differing only at extreme virtual monoenergetic image levels. CT-FFR from PCD-CT at 70 keV showed the strongest correlation with energy-integrating detector-CT.

Objectives: This study evaluates the perceived impact of European School of Radiology (ESOR) training programs on radiologists' professional development.

Methods: A cross-sectional survey targeted alumni who participated in ESOR fellowships from 2011 to 2023. The survey included questions on demographics, professional background, ESOR program details, and career impact. Data were collected via a web-based questionnaire and analyzed using descriptive statistics and thematic analysis.

Results: A total of 916 alumni were invited to the survey, with a response rate of 21% (190 participants). The median age was 31 years (range 29-33), and 54% were female. Most worked in public healthcare (62%) and were involved in academic activities (24%). Fellowship types included the visiting scholarship program (44%), Bracco fellowship (32%), and exchange program for fellowships (25%). The majority (59%) reported the fellowship helped them reach their current position, and 35% noted it upgraded their CV. Significant application of learned skills was reported by 69%. Ongoing cooperation with former tutors was maintained by 54%. Financial support was crucial, with 41% stating they could not have completed the training without it, 33% considering it very important, and 13% important. Participants rated the impact on clinical skills with a median score of 9/10. Other areas of impact included research skills (median 7/10), subspecialization (median 9/10), exposure to diverse practices (median 9/10), networking (median 10/10), and personal and professional growth (median 10/10).

Conclusion: ESOR training programs significantly enhance radiologists' professional development through comprehensive support, high-quality training, and substantial financial aid, ensuring participants are well-equipped for career advancement.

Critical relevance statement: This study evaluates the perceived impact of ESOR training programs on radiologists' professional development, highlighting significant enhancements in clinical skills, career advancement, and the critical role of financial support in facilitating access to high-quality education.

Key points: The ESOR offers various programs addressing both foundational and advanced training in radiology. Fifty-nine percent of participants reported that ESOR fellowships helped them achieve their current positions. Participants experienced a median improvement score of 9 out of 10 in clinical skills. Fifty-four percent of participants maintained ongoing cooperation with former tutors post-fellowship. ESOR financial support was perceived as crucial by many participants, ensuring access to high-quality education.

Mullerian duct anomalies (MDA) are a group of uncommon but treatable causes of infertility and pregnancy complications. This review describes the embryology, American Society of Reproductive Medicine (ASRM) classification 2021, and corresponding imaging features of MDA. The three phases of embryological development of Mullerian duct structures are described. The main emphasis is on the ASRM 2021 classification of MDA into nine descriptive categories, while the European Society of Human Reproduction and Embryology and the European Society for Gynecologic Endoscopy (ESHRE/ESGE) classification is also briefly described where necessary. MRI imaging features of MDA along with the acquisition techniques are discussed in detail, as MRI is the ideal imaging modality for MDA diagnosis. In addition, the current role of imaging modalities such as hysterosalpingography and ultrasound including 3D transvaginal ultrasound is also elucidated. The review aims to revisit the MRI imaging features of Mullerian anomalies and reiterates that an accurate description of each anomaly and precise communication with clinicians is the priority rather than rigidly fitting the anomaly into one particular category. CRITICAL RELEVANCE STATEMENT: The ASRM 2021 classification of Mullerian anomalies has re-defined the criteria for an arcuate uterus. Radiologists must know of the new classification and imaging features and try to describe each anomaly accurately rather than forcefully fitting an anomaly into a definite category. KEY POINTS: MDA has an important role in infertility and pregnancy complications. Knowledge of imaging features helps radiologists aid patient management; MRI is the preferred imaging modality for MDAs. An accurate MRI-based description of Mullerian anomalies is crucial, avoiding the pitfalls of rigid categorization.

Objectives: This study assesses radiologists' knowledge, attitudes, and practices regarding healthcare sustainability. With radiology's substantial environmental impact, sustainable practices are crucial to reduce energy use, waste, and resource depletion. The study evaluates radiologists' awareness, engagement, and perceived barriers to sustainable practices in the UAE, identifying areas for improvement and intervention.

Methods: A cross-sectional survey was conducted among UAE radiologists in hospitals, clinics, and medical centers from August to October 2024. Developed and piloted by the research team, the survey addressed demographic details, sustainability knowledge, attitudes, current practices, and implementation barriers. Convenience sampling yielded 111 responses, analyzed using descriptive and inferential statistics to identify trends and associations.

Results: The findings indicate moderate knowledge levels, with 31.8% of radiologists agreeing to understand sustainability concepts. While 36.4% strongly agreed on its importance, only 20% felt it was prioritized at their workplace. Key barriers included lack of training (40.5% agreed, 29.7% strongly agreed) and insufficient financial support (37.8% agreed, 25.2% strongly agreed). Digital documentation and waste-reduction practices were observed but varied in consistency.

Conclusion: Radiologists display a positive attitude toward sustainability but face significant implementation barriers, primarily in institutional support and resources. Addressing training gaps and increasing leadership commitment are essential to advancing sustainable practices. Future initiatives should emphasize policy support, education, and resource allocation to foster a sustainable radiology sector.

Critical relevance statement: This article critically examines radiologists' knowledge, attitudes, and barriers to sustainable practices, highlighting the need for institutional support and targeted training to advance environmental responsibility and sustainable practices within clinical radiology.

Key points: Radiologists support sustainability but lack knowledge of specific practices. Key challenges include limited training, support, and funding. Commitment, training, and resources are essential for sustainable radiology.

Objectives: To evaluate the ability of sphericity in glioblastomas (GBMs) for predicting overall survival (OS) and the survival benefit from gross tumour resection (GTR).

Methods: Preoperative MRI scans were retrospectively analysed in IDH-wildtype GBM patients from two datasets. After MRI preprocessing and tumour segmentation, tumour sphericity was calculated based on the tumour core region. The prognostic value of tumour surface regularity was evaluated via Kaplan-Meier (K-M) plots, univariate and multivariate Cox proportional hazards analyses. In different surface regularity subgroups, the OS benefit from GTR was evaluated via K-M plots and the restricted mean survival time (RMST).

Results: This study included 367 patients (median age, 62.0 years [IQR, 54.5-70.5 years]) in the discovery cohort and 475 patients (median age, 63.6 years [IQR, 56.2-71.3 years]) in the validation cohort. Sphericity was an independent predictor of OS in the discovery (p = 0.022, hazard ratio (HR) = 1.45, 95% confidence interval (CI) 1.06-1.99) and validation groups (p = 0.007, HR = 1.38, 95% CI: 1.09-1.74) according to multivariate analysis. Age, extent of resection, and surface regularity composed a prognostic model that separated patients into subgroups with distinct prognoses. Patients in the surface-irregular subgroup benefited from GTR, but patients in the surface-regular subgroup did not in the discovery (p < 0.001 vs. p = 0.056) and validation datasets (p < 0.001 vs. p = 0.11).

Conclusions: The high surface regularity of IDH-wildtype GBM is significantly correlated with better OS and does not benefit substantially from GTR.

Critical relevance statement: The proposed imaging marker has the potential to increase the survival prediction efficacy for IDH-wildtype glioblastomas (GBMs), offering a valuable indicator for clinical decision-making.

Key points: Sphericity is an independent prognostic factor in IDH-wildtype glioblastomas (GBMs). High sphericity in IDH-wildtype GBM is significantly correlated with better survival. GBM patients with low sphericity could receive survival benefits from gross tumour resection.

Objectives: To explore the quantitative analysis of high b value (2000 s/mm²) diffusion-weighted imaging (DWI) for the differentiation of ileocecal inflammatory conditions and tumors, compared with conventional b value (800 s/mm²) DWI.

Methods: Sixty-six patients with 30 tumors and 36 inflammatory conditions underwent MR enterography with conventional and high b values DWI. Quantitative apparent diffusion coefficient (ADC) values and signal intensity ratios (SIRs) of lesions of the psoas muscle were measured from the two b value DWIs. The receiver operating characteristic (ROC) curve was applied to determine the diagnostic value of ADC and SIR for differentiating tumors from inflammatory conditions.

Results: The ADC values of tumors were significantly lower than those of inflammatory conditions in 800 s/mm² (p = 0.001) and 2000 s/mm² (p < 0.001) DWI. In addition, tumors exhibited significantly higher SIR values compared to inflammatory conditions (p < 0.001 in 800 s/mm² and 2000 s/mm² DWI). Areas under the curve (AUC) of ADC and SIR derived from high b value (0.828 for ADC, 0.947 for SIR) were superior to those from conventional b value DWI (0.731 and 0.849, respectively). Compared to ADC, SIR values achieved better AUCs in both two b values DWI.

Conclusions: Quantitative ADC values and SIR could be used as non-invasive tools to distinguish ileocecal tumors from inflammatory conditions. The use of high b value DWI would improve this ability. Furthermore, SIR obtained from high b value DWI was the most promising quantitative parameter.

Critical relevance statement: This study indicated that quantitative DWI parameters might be applied as non-invasive imaging biomarkers for distinguishing bowel tumors from inflammatory conditions. The SIR from high b value DWI could improve the differentiation, providing invaluable information for establishing appropriate therapeutic strategies.

Key points: Differentiation between bowel inflammatory conditions and tumors is still a dilemma. Quantitative DWI contributed to distinguishing ileocecal tumors from inflammatory conditions. SIR from DWI is a promising parameter for differentiating these pathologies.

Purpose: To develop a machine learning (ML) model combining preoperative multiparametric diffusion-weighted imaging (DWI) and clinical features to better predict overall survival (OS) and recurrence-free survival (RFS) following radical surgery for pancreatic ductal adenocarcinoma (PDAC).

Materials and methods: A retrospective analysis was conducted on 234 PDAC patients who underwent radical resection at two centers. Among 101 ML models tested for predicting postoperative OS and RFS, the best-performing model was identified based on comprehensive evaluation metrics, including C-index, Brier scores, AUC curves, clinical decision curves, and calibration curves. This model's risk stratification capability was further validated using Kaplan-Meier survival analysis.

Results: The random survival forest model achieved the highest C-index (0.828/0.723 for OS and 0.781/0.747 for RFS in training/validation cohorts). Incorporating nine key factors-D value, T-stage, ADC-value, postoperative 7th day CA19-9 level, AJCC stage, tumor differentiation, type of operation, tumor location, and age-optimized the model's predictive accuracy. The model had integrated Brier score below 0.13 and C/D AUC values above 0.85 for both OS and RFS predictions. It also outperformed traditional models in predictive ability and clinical benefit, as shown by clinical decision curves. Calibration curves confirmed good predictive consistency. Using cut-off scores of 16.73/29.05 for OS/RFS, Kaplan-Meier analysis revealed significant prognostic differences between risk groups (p < 0.0001), highlighting the model's robust risk prediction and stratification capabilities.

Conclusion: The random survival forest model, combining DWI and clinical features, accurately predicts survival and recurrence risk after radical resection of PDAC and effectively stratifies risk to guide clinical treatment.

Critical relevance statement: The construction of 101 ML models based on multiparametric quantitative DWI combined with clinical variables has enhanced the prediction performance for survival and recurrence risks in patients undergoing radical resection for PDAC.

Key points: This study first develops DWI-based radiological-clinical ML models predicting PDAC prognosis. Among 101 models, RFS is the best and outperforms other traditional models. Multiparametric DWI is the key prognostic predictor, with model interpretations through SurvSHAP.

Objective: This scoping review explores the potential role of cancer-staging chest CT scans in assessing cardiovascular (CV) risk in cancer patients. It aims to evaluate: (1) the correlation between non-gated chest CT and the conventional Agatston score from cardiac CT; (2) the association between coronary calcium scores from non-gated chest CT and CV risk in non-oncological patients; (3) the link between coronary calcium assessed by non-gated chest CT and CV events or endothelial damage in cancer patients.

Methods: Three different searches were performed on PubMed, according to the three steps described above. Both original articles and systematic reviews were included.

Results: Many studies in the literature have found a strong correlation between coronary calcium scores from non-gated chest CTs and the conventional Agatston scores from gated cardiac CTs. Various methodologies, including Agatston scoring, ordinal scoring, and the "extent" and "length" methods, have been successfully adapted for use with non-gated chest CTs. Studies show that non-gated scans, even those using iodinated contrast, can accurately assess coronary calcification and predict CV risk, with correlations as high as r = 0.94 when compared to cardiac CTs. In oncological settings, studies demonstrated a significant link between coronary calcium levels on non-gated chest CTs and higher CV risk, including MACE and overall mortality.

Conclusions: Radiological assessment of coronary calcium on non-gated CT scans shows potential for improving CV risk prediction.

Critical relevance statement: Non-gated chest CT scans can detect endothelial damage in cancer patients, highlighting the need for standardized radiological practices to assess CV risks during routine oncological follow-up, thereby enhancing radiology's role in comprehensive cancer care.

Key points: Cancer therapies improve outcomes but increase cardiovascular risk, requiring balanced management. Coronary calcification on non-gated CT correlates with Agatston scores, predicting cardiovascular risk. Routinely performed CTs predict cardiovascular risk, optimizing the management of cancer patients.

Objective: To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRI_DL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

Materials and methods: This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRI_DL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2_MESE). The inter-reader agreement on qualitative evaluation of SyMRI_DL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.

Results: DLR significantly narrowed the quantitative differences between T2-SyMRI_DL and T2_MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRI_DL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRI_DL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRI_DL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.

Conclusion: DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.

Critical relevance statement: One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.

Key points: Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

Objectives: Radiographers use advanced medical imaging and radiotherapy (MIRT) equipment. They are also a digitally mature and digitally resilient workforce in healthcare. Artificial intelligence is already changing their clinical practice and roles in data acquisition, post-processing, and workflow management. It is therefore vital to understand the impact of AI on the careers, roles and professional identity of radiographers, as key stakeholders of the digital transformation of healthcare within the medical imaging ecosystem.

Methods: A European radiographer survey, endorsed by the European Federation of Radiographer Societies (EFRS), was distributed online. It was piloted with twelve radiographers and translated into eight languages. Although this study included both qualitative and quantitative results, this paper emphasises the quantitative aspect.

Results: A total of 2206 European radiographers have responded from 37 different countries. Despite some concerns around workforce deskilling, future professional identity, and job prospects, participants showed overall optimistic views about the use of AI in healthcare. This was particularly strong for those with prior AI education (mean: 2.15 vs. 1.89; p-value: < 0.001), hands-on experience with AI (correlation: 0.047; p-value: 0.038), from countries with higher digital literacy (mean: 2.00 vs.1.93; p-value: 0.027) and a higher academic level of radiography education (mean: 3.28 vs. 3.15; p-value: 0.002). Men appeared slightly more enthused about the development of technological skills and women about the honing of patient-centred care skills. Finally, interprofessional collaboration was seen as essential not only for the seamless clinical integration of AI but also for supporting patient benefit.

Conclusion: While AI implementation advances, AI education needs to keep at pace to ensure acceptability, trust, and safe use of this technology by healthcare professionals, minimising their concerns around professional role changes and enabling them to see the opportunities of service transformation.

Critical relevance statement: This paper aims to map out the perceived impact of AI on the professional identity and careers of European radiographers.

Key points: AI is impacting radiographers' clinical practice and changing their professional identity. Despite increasing AI awareness, AI education is still lacking across Europe. AI education is key for AI acceptability and trust by radiographers, which facilitates AI implementation and service transformation.