European Radiology最新文献

Ovarian masses encompass various conditions, from benign to highly malignant, and imaging plays a vital role in their diagnosis and management. Ultrasound, particularly transvaginal ultrasound, is the foremost diagnostic method for adnexal masses. Magnetic Resonance Imaging (MRI) is advised for more precise characterisation if ultrasound results are inconclusive. The ovarian-adnexal reporting and data system (O-RADS) MRI lexicon and scoring system provides a standardised method for describing, assessing, and categorising the risk of each ovarian mass. Determining a histological differential diagnosis of the mass may influence treatment decision-making and treatment planning. When ultrasound or MRI suggests the possibility of cancer, computed tomography (CT) is the preferred imaging technique for staging. It is essential to outline the extent of the malignancy, guide treatment decisions, and evaluate the feasibility of cytoreductive surgery. This article provides a comprehensive overview of the key imaging processes in evaluating and managing ovarian masses, from initial diagnosis to initial treatment. It also includes pertinent recommendations for properly performing and interpreting various imaging modalities. KEY POINTS: MRI is the modality of choice for indeterminate ovarian masses at ultrasound, and the O-RADS MRI lexicon and score enable unequivocal communication with clinicians. CT is the recommended modality for suspected ovarian masses to tailor treatment and surgery. Multidisciplinary meetings integrate information and help decide the most appropriate treatment for each patient.

Purpose: This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration.

Materials and methods: We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class.

Results: Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios.

Conclusion: Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance.

Clinical relevance statement: This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes.

Key points: Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.

Objectives: The Node-RADS score was recently introduced to offer a standardized assessment of lymph node invasion (LNI). We tested its diagnostic performance in accurately predicting LNI in breast cancer (BC) patients with magnetic resonance imaging. The study also explores the consistency of the score across three readers.

Materials and methods: A retrospective study was conducted on BC patients who underwent preoperative breast contrast-enhanced magnetic resonance imaging and lymph node dissection between January 2020 and January 2023. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated for different Node-RADS cut-off values. Pathologic results were considered the gold standard. The overall diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). A logistic regression analysis was performed. Cohen's Kappa analysis was used for inter-reader agreement.

Results: The final population includes 192 patients and a total of 1134 lymph nodes analyzed (372 metastatic and 762 benign). Increasing the Node-RADS cut-off values, specificity and PPV rose from 71.4% to 100% and 76.7% to 100%, respectively, for Reader 1, 69.4% to 100% and 74.6% to 100% for Reader 2, and from 64.3% to 100% and 72% to 100% for Reader 3. Node-RADS > 2 could be considered the best cut-off value due to its balanced performance. Node-RADS exhibited a similar AUC for the three readers (0.97, 0.93, and 0.93). An excellent inter-reader agreement was found (Kappa values between 0.71 and 0.83).

Conclusions: The Node-RADS score demonstrated moderate-to-high overall accuracy in identifying LNI in patients with BC, suggesting that the scoring system can aid in the identification of suspicious lymph nodes and facilitate appropriate treatment decisions.

Clinical relevance statement: Node-RADS > 2 can be considered the best cut-off for discriminating malignant nodes, suggesting that the scoring system can effectively help identify suspicious lymph nodes by staging the disease and providing a global standardized language for clear communication.

Key points: Axillary lymphadenopathies in breast cancer are crucial for determining the disease stage. Node-RADS was introduced to provide a standardized evaluation of breast cancer lymph nodes. RADS > 2 can be considered the best cut-off for discriminating malignant nodes.

Objectives: To assess radiopalmar ganglion cysts' (RPG) prevalence, morphology, and clinical significance in consecutive patients.

Materials and methods: In this retrospective single-center study, two radiologists assessed the presence of RPG and morphologic features on wrist MRI. Radiopalmar complaints and scapholunate ligament (SLL) tears were evaluated.

Results: A total of 1053 wrists in 909 patients (mean age 43.4 ± 15.5 years, 602 females) were evaluated. All 308 RPG (Group 1; 308 patients, 29.2%) originated from the palmar capsule; 49 were unilocular, 95 oligolocular, and 164 multilocular; 745 wrists had no RPG (Group 2; 601 patients). One hundred and twenty-six RPG showed internal debris. The mean diameter was 8.5 ± 5.6 mm (cranio-caudal) (1.0-32.9 mm), 8.0 ± 4.1 mm (medio-lateral) (1.0-31.9 mm), and 3.7 ± 2.3 mm (dorso-palmar) (0.4-16.0 mm). 168 RPG showed direct contact with the radial vascular bundle, 24 with the flexor carpi radialis tendon, and 123 with the flexor pollicis longus tendon. In Group 1, significantly more patients showed partial (82/308) [group 2: 45/745, p < 0.001] or complete SLL tears (22/308) [group 2: 20/745, p < 0.001]. Of the patients with RPG, 15.3% presented with radiopalmar complaints. Only the dorso-palmar RPG diameter was positively correlated with radiopalmar complaints (for readers 1 and 2: r_s = 0.66/0.61, p < 0.001, respectively), and the best dorso-palmar diameter cut-off value for the probability of having radiopalmar complaints was defined at 3 mm (area under the curve (AUC) 0.74). Other morphologic features were not eligible to discriminate symptomatic patients (AUC range 0.53-0.61).

Conclusion: This study found RPG in 29% of patients, most of them asymptomatic. However, a dorso-palmar cyst diameter > 3 mm may be clinically significant.

Clinical relevance statement: Radiopalmar ganglion cysts, observed in 29% of wrist MR examinations, are mostly asymptomatic, but those with a larger dorso-palmar diameter may be associated with radiopalmar complaints.

Key points: Radiopalmar ganglion cysts are found in 29% of patients undergoing wrist MRI. Most patients with evidence of radiopalmar ganglion cysts do not show radiopalmar symptoms (85%). A dorso-palmar cyst diameter > 3 mm may be associated with radiopalmar complaints.

Objectives: To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.

Methods: Fifty consecutive patients with bone marrow cancer underwent WB-MRI. Two experts compared axial b900 s/mm² and the corresponding maximum intensity projections (MIP) of deep resolve boost (DRB) accelerated diffusion-weighted imaging (DWI) sequences (time of acquisition: 6:42 min) against conventional sequences (time of acquisition: 14 min). Readers assessed paired images for noise, artefacts, signal fat suppression, and lesion conspicuity using Likert scales, also expressing their overall subjective preference. Signal-to-noise and contrast-to-noise ratios (SNR and CNR) and the apparent diffusion coefficient (ADC) values of normal tissues and cancer lesions were statistically compared.

Results: Overall, radiologists preferred either axial DRB b900 and/or corresponding MIP images in almost 80% of the patients, particularly in patients with a high body-mass index (BMI > 25 kg/m²). In qualitative assessments, axial DRB images were preferred (preferred/strongly preferred) in 56-100% of cases, whereas DRB MIP images were favoured in 52-96% of cases. DRB-SNR/CNR was higher in all normal tissues (p < 0.05). For cancer lesions, the DRB-SNR was higher (p < 0.001), but the CNR was not different. DRB-ADC values were significantly higher for the brain and psoas muscles, but not for cancer lesions (mean difference: + 53 µm²/s). Inter-class correlation coefficient analysis showed good to excellent agreement (95% CI 0.75-0.93).

Conclusion: DRB sequences produce higher-quality axial DWI, resulting in improved MIPs and significantly reduced acquisition times. However, differences in the ADC values of normal tissues need to be considered.

Clinical relevance statement: Deep learning accelerated diffusion sequences produce high-quality axial images and MIP at reduced acquisition times. This advancement could enable the increased adoption of Whole Body-MRI for the evaluation of patients with bone marrow cancer.

Key points: Deep learning reconstruction enables a more than 50% reduction in acquisition time for WB diffusion sequences. DRB images were preferred by radiologists in almost 80% of cases due to fewer artefacts, improved background signal suppression, higher signal-to-noise ratio, and increased lesion conspicuity in patients with higher body mass index. Cancer lesion diffusivity from DRB images was not different from conventional sequences.

Purpose: To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT).

Methods: Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images.

Results: Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%).

Conclusion: PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses.

Clinical relevance statement: The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice.

Key points: The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

Objectives: Our study comprised a single-center retrospective in vitro correlation between spectral properties, namely ρ/Z values, derived from scanning blood samples using dual-energy computed tomography (DECT) with the corresponding laboratory hemoglobin/hematocrit (Hb/Hct) levels and assessed the potential in anemia-detection.

Methods: DECT of 813 patient blood samples from 465 women and 348 men was conducted using a standardized scan protocol. Electron density relative to water (ρ or rho), effective atomic number (Z_eff), and CT attenuation (Hounsfield unit) were measured.

Results: Positive correlation with the Hb/Hct was shown for ρ (r-values 0.37-0.49) and attenuation (r-values 0.59-0.83) while no correlation was observed for Z_eff (r-values -0.04 to 0.08). Significant differences in attenuation and ρ values were detected for blood samples with and without anemia in both genders (p value < 0.001) with area under the curve ranging from 0.7 to 0.95. Depending on the respective CT parameters, various cutoff values for CT-based anemia detection could be determined.

Conclusion: In summary, our study investigated the correlation between DECT measurements and Hb/Hct levels, emphasizing novel aspects of ρ and Z_eff values. Assuming that quantitative changes in the number of hemoglobin proteins might alter the mean Z_eff values, the results of our study show that there is no measurable correlation on the atomic level using DECT. We established a positive in vitro correlation between Hb/Hct values and ρ. Nevertheless, attenuation emerged as the most strongly correlated parameter with identifiable cutoff values, highlighting its preference for CT-based anemia detection.

Clinical relevance statement: By scanning multiple blood samples with dual-energy CT scans and comparing the measurements with standard laboratory blood tests, we were able to underscore the potential of CT-based anemia detection and its advantages in clinical practice.

Key points: Prior in vivo studies have found a correlation between aortic blood pool and measured hemoglobin and hematocrit. Hemoglobin and hematocrit correlated with electron density relative to water and attenuation but not Z_eff. Dual-energy CT has the potential for additional clinical benefits, such as CT-based anemia detection.

Objectives: Artificial intelligence (AI) tools are becoming more available in modern healthcare, particularly in radiology, although less attention has been paid to applications for children and young people. In the development of these, it is critical their views are heard.

Materials and methods: A national, online survey was publicised to UK schools, universities and charity partners encouraging any child or young adult to participate. The survey was "live" for one year (June 2022 to 2023). Questions about views of AI in general, and in specific circumstances (e.g. bone fractures) were asked.

Results: One hundred and seventy-one eligible responses were received, with a mean age of 19 years (6-23 years) with representation across all 4 UK nations. Most respondents agreed or strongly agreed they wanted to know the accuracy of an AI tool that was being used (122/171, 71.3%), that accuracy was more important than speed (113/171, 66.1%), and that AI should be used with human oversight (110/171, 64.3%). Many respondents (73/171, 42.7%) felt AI would be more accurate at finding problems on bone X-rays than humans, with almost all respondents who had sustained a missed fracture strongly agreeing with that sentiment (12/14, 85.7%).

Conclusions: Children and young people in our survey had positive views regarding AI, and felt it should be integrated into modern healthcare, but expressed a preference for a "medical professional in the loop" and accuracy of findings over speed. Key themes regarding information on AI performance and governance were raised and should be considered prior to future AI implementation for paediatric healthcare.

Clinical relevance statement: Artificial intelligence (AI) integration into clinical practice must consider all stakeholders, especially paediatric patients who have largely been ignored. Children and young people favour AI involvement with human oversight, seek assurances for safety, accuracy, and clear accountability in case of failures.

Key points: Paediatric patient's needs and voices are often overlooked in AI tool design and deployment. Children and young people approved of AI, if paired with human oversight and reliability. Children and young people are stakeholders for developing and deploying AI tools in paediatrics.

Objectives: Hyper- or isointensity in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI has high specificity for focal nodular hyperplasia (FNH) but may be present in hepatocellular adenoma and carcinoma (HCA/HCC). This study aimed to identify imaging characteristics differentiating FNH and HCA/HCC.

Materials and methods: This multicenter retrospective cohort study included patients with pathology-proven FNH or HCA/HCC, hyper-/isointense in the HBP of gadoxetic acid-enhanced MRI between 2010 and 2020. Diagnostic performance of imaging characteristics for the differentiation between FNH and HCA/HCC were reported. Univariable analyses, multivariable logistic regression analyses, and classification and regression tree (CART) analyses were conducted. Sensitivity analyses evaluated imaging characteristics of B-catenin-activated HCA.

Results: In total, 124 patients (mean age 40 years, standard deviation 10 years, 108 female) with 128 hyper-/isointense lesions were included. Pathology diagnoses were FNH and HCA/HCC in 64 lesions (50%) and HCA/HCC in 64 lesions (50%). Imaging characteristics observed exclusively in HCA/HCC were raster and atoll fingerprint patterns in the HBP, sinusoidal dilatation on T2-w, hemosiderin, T1-w in-phase hyperintensity, venous washout, and nodule-in-nodule partification in the HBP and T2-w. Multivariable logistic regression and CART additionally found a T2-w scar indicating FNH, less than 50% fat, and a spherical contour indicating HCA/HCC. In our selected cohort, 14/48 (29%) of HCA were B-catenin activated, most (13/14) showed extensive hyper-/isointensity, and some had a T2-w scar (4/14, 29%).

Conclusion: If the aforementioned characteristics typical for HCA/HCC are encountered in lesions extensively hyper- to isointense, further investigation may be warranted to exclude B-catenin-activated HCA.

Clinical relevance: Hyper- or isointensity in the HBP of gadoxetic acid-enhanced MRI is specific for FNH, but HCA/HCC can also exhibit this feature. Therefore, we described imaging patterns to differentiate these entities.

Key points: FNH and HCA/HCC have similar HBP intensities but have different malignant potentials. Six imaging patterns exclusive to HCA/HCC were identified in this lesion population. These features in liver lesions hyper- to isointense in the HBP warrant further evaluation.

Objectives: To establish normative values and identify potential factors influencing pancreatic iodine uptake using dual-energy CT (DECT).

Materials and methods: This retrospective study included participants without pancreatic diseases undergoing DECT at two institutions with different platforms. Their protocols both included arterial phase (AP), portal venous phase (PP), and equilibrium phase (EP), defined as 35 s-40 s, 60 s-70 s, and 150 s-180 s after injection of contrast agent, respectively. Both iodine concentration (IC) and normalised IC (NIC) were measured. Demographic features, local measurements of the pancreas and visceral fat area (VFA) were considered as potential factors influencing iodine uptake using multivariate linear regression analyses.

Results: A total of 562 participants (median age 58 years [interquartile range: 47-67], with 282 men) were evaluated. The mean IC differed significantly between two institutions (all p < 0.001) across three contrast-enhanced phases, while the mean NIC showed no significant differences (all p > 0.05). The mean values of NIC were 0.22 at AP, 0.43 at PP and 0.45 at EP. NIC_AP was independently affected by VFA (β = 0.362, p < 0.001), smoking (β = -0.240, p = 0.001), and type-II diabetes (β = -0.449, p < 0.001); NIC_PP by VFA (β = -0.301, p = 0.017) and smoking (β = -0.291, p < 0.001); and NIC_EP by smoking (β = -0.154, p = 0.10) and alcohol consumption (β = -0.350, p < 0.001) with statistical power values over 0.81.

Conclusion: NIC values were consistent across institutions. Abdominal obesity, smoking, alcohol consumption, and diabetes are independent factors influencing pancreatic iodine uptake.

Clinical relevance statement: This study has provided reference normative values, influential factors and effective normalisation methods of pancreatic iodine uptake in multiphase dual-energy CT for future studies in this area as a new biological marker.

Key points: Evaluation of pancreatic iodine uptake measured by dual-energy CT is a promising method for future studies. Abdominal obesity, smoking, alcohol consumption, diabetes, and sex are independent factors influencing pancreatic iodine uptake. Utility of normalised iodine concentration is necessary to ensure the consistency across different institutions.