European Radiology最新文献

Tinnitus is common, with approximately 1/4 of the elderly population experiencing chronic tinnitus. While non-pulsatile tinnitus usually has no structural cause, pulsatile tinnitus is more likely to have an identifiable aetiology, and imaging plays a key role in the search for treatable and life-threatening causes. Since the characteristics of the tinnitus guide the diagnostic strategy, a detailed clinical assessment should always be performed before imaging is considered. In the setting of non-pulsatile tinnitus, imaging with MRI should only be performed if it is unilateral or asymmetric, or when it is associated with focal neurologic abnormalities or asymmetric hearing loss. In contrast, imaging investigation is always required in the presence of pulsatile tinnitus. Whilst there are specific clinical features in which temporal bone CT will be the initial imaging strategy for pulsatile tinnitus (e.g., retrotympanic mass or conductive hearing loss), most patients will require either CT or MRI with arterial and venous imaging. The clinical categorisation of pulsatile tinnitus as "arterial" or "venous" may guide the radiological search and help understand the significance of certain imaging findings (e.g., venous variants). Significant pathology (e.g., dural arteriovenous malformation) must be excluded in the context of objective pulsatile tinnitus and may require additional cross-sectional imaging; conventional angiography is now rarely indicated. KEY POINTS: In patients with unilateral, non-pulsatile tinnitus, MRI should be performed to rule out retrocochlear disease. All patients with pulsatile tinnitus should be imaged and the clinical assessment guides the selection of the most appropriate imaging technique. If the first imaging study does not reveal the suspected cause of objective pulsatile tinnitus, additional imaging investigations should be performed to exclude alternative diagnoses.

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics. KEY POINTS: Radiomics' inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making. Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor. Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.

Objectives: To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether the sample size meets the estimates obtained by using established criteria.

Methods: We identified radiomics studies that were published from 01 January 2023 to 31 December 2023 in seven leading peer-reviewed radiological journals. We reviewed the sample size justification methods, and actual sample size used. We calculated and compared the actual sample size used to the estimates obtained by using three established criteria proposed by Riley et al. We investigated which characteristics factors were associated with the sufficient sample size that meets the estimates obtained by using established criteria proposed by Riley et al. RESULTS: We included 116 studies. Eleven out of one hundred sixteen studies justified the sample size, in which 6/11 performed a priori sample size calculation. The median (first and third quartile, Q1, Q3) of the total sample size is 223 (130, 463), and those of sample size for training are 150 (90, 288). The median (Q1, Q3) difference between total sample size and minimum sample size according to established criteria are -100 (-216, 183), and those differences between total sample size and a more restrictive approach based on established criteria are -268 (-427, -157). The presence of external testing and the specialty of the topic were associated with sufficient sample size.

Conclusion: Radiomics studies are often designed without sample size justification, whose sample size may be too small to avoid overfitting. Sample size justification is encouraged when developing a radiomics model.

Key points: Question Sample size justification is critical to help minimize overfitting in developing a radiomics model, but is overlooked and underpowered in radiomics research. Findings Few of the radiomics models justified, calculated, or reported their sample size, and most of them did not meet the recent formal sample size criteria. Clinical relevance Radiomics models are often designed without sample size justification. Consequently, many models are too small to avoid overfitting. It should be encouraged to justify, perform, and report the considerations on sample size when developing radiomics models.

Purpose: To investigate the test-retest repeatability of radiomic features in myocardial native T1 and T2 mapping.

Methods: In this prospective study, 50 healthy volunteers (29 women and 21 men, mean age 39.4 ± 13.7 years) underwent two identical cardiac magnetic resonance imaging (MRI) examinations at 1.5 T. The protocol included native T1 and T2 mapping in both short-axis and long-axis orientation. For T1 mapping, we investigated standard (1.9 × 1.9 mm) and high (1.4 × 1.4 mm) spatial resolution. After manual segmentation of the left ventricular myocardium, 100 radiomic features from seven feature classes were extracted and analyzed. Test-retest repeatability of radiomic features was assessed using the intraclass correlation coefficient (ICC) and classified as poor (ICC < 0.50), moderate (0.50-0.75), good (0.75-0.90), and excellent (> 0.90).

Results: For T1 maps acquired in short-axis orientation at standard resolution, repeatability was excellent for 6 features, good for 29 features, moderate for 19 features, and poor for 46 features. We identified 15 features from 6 classes which showed good to excellent reproducibility for T1 mapping in all resolutions and all orientations. For short-axis T2 maps, repeatability was excellent for 6 features, good for 25 features, moderate for 23 features, and poor for 46 features. 12 features from 5 classes were found to have good to excellent repeatability in T2 mapping independent of slice orientation.

Conclusion: We have identified a subset of features with good to excellent repeatability independent of slice orientation and spatial resolution. We recommend using these features for further radiomics research in myocardial T1 and T2 mapping.

Key points: Question The study addresses the need for reliable radiomic features for quantitative analysis of the myocardium to ensure diagnostic consistency in cardiac MRI. Findings We have identified a subset of radiomic features demonstrating good to excellent repeatability in native T1 and T2 mapping independent of slice orientation and resolution. Clinical relevanceRadiomic features have been proposed as diagnostic and prognostic biomarkers in various heart diseases. By identifying a subset of particularly reproducible radiomic features our study serves to inform the selection of radiomic features in future research and clinical applications.

Objectives: To investigate baseline patient characteristics associated with the risk of computed tomography (CT)-based sarcopenia and assess whether sarcopenia and other morphometric parameters influence survival outcomes in patients with liver metastases and cholangiocarcinoma after Yttrium-90 radioembolization.

Materials and methods: We retrospectively analyzed 120 cancer patients (mean age, 62 ± 13.3 years, 61 men) who underwent preprocedural CT. Skeletal muscle index (SMI) was measured at the L3 vertebral level to identify sarcopenia. The Cox proportional hazard model was performed to assess the prognostic value of the variables, and Kaplan-Meier analysis with log-rank text was used for overall survival (OS) assessment.

Results: Sarcopenia was diagnosed in 70 patients (58.3%). The multivariate regression analysis demonstrated that male sex, body mass index (BMI), visceral fat radiation attenuation (VFRA), skeletal muscle radiation attenuation (SMRA), and subcutaneous fat radiation attenuation (SFRA) were associated with the incidence of sarcopenia with the odds ratio of 8.81 (95% CI, 2.09-37.1, p = 0.003), 0.64 (95% CI, 0.48-0.85, p = 0.002), 1.23 (95% CI, 1.06-1.42, p = 0.006), 0.79 (95% CI, 0.69-0.91, p = 0.001) and 0.84 (95% CI, 0.76-0.93, p = 0.001), respectively. Age, skeletal muscle index, and tumor subtypes were independent prognostic factors for OS with the hazard ratio of 1.03 (95% CI, 1.01-1.05, p = 0.01), 0.92 (95% CI, 0.86-0.99, p = 0.021) and 2.09 (95% CI, 1.31-3.33 p = 0.002), respectively. In patients with intrahepatic cholangiocarcinoma, median OS was significantly longer in the non-sarcopenic group than in the sarcopenic patient (25.9 versus 6.5 months, p = 0.029).

Conclusion: Male sex, BMI, VFRA, SMRA, and SFRA were associated with the incidence of sarcopenia. SMI value could be used as a biomarker for OS in patients treated with Yttrium-90 radioembolization.

Key points: Question The prognostic significance of CT-based sarcopenia and other morphometric parameters in patients with liver metastases and cholangiocarcinoma undergoing Yttrium-90 radioembolization remains unclear. Findings A high skeletal muscle index has been identified as an independent protective factor for overall survival in cancer patients treated with Yttrium-90 radioembolization. Clinical relevance The negative impact of CT-based sarcopenia has been confirmed in the context of Yttrium-90 radioembolization. Clinicians should strive to prevent the progression of sarcopenia or maintain skeletal muscle index in perioperative management.

Objectives: The environmental footprint of iodinated contrast agents (ICAs) and gadolinium-based contrast agents (GBCAs) is noteworthy. This study assesses: (1) patients' "green sensitivity" as measured by their acceptance in a sustainability study and (2) the resulting potential reduction of contrast residuals in wastewater.

Materials and methods: After ethical approval, participants scheduled for administration of ICAs or GBCAs for diagnostic purposes were enrolled in this prospective observational study from July 2022 to October 2023. They were asked to prolong their hospital stay by up to 60 min to collect their first urine in dedicated canisters, thereby measuring the recovery rates of ICAs and GBCAs as found/theoretical ratio of concentrations. Mann-Whitney U, χ² tests, and multivariable regression analysis were used.

Results: Patients scheduled for contrast-enhanced CT or MRI (n = 455) were screened; 422 (92.7%) accepted to participate. We enrolled 212 patients administered with ICAs and 210 administered with GBCAs. The median recovery rate was 51.2% (interquartile range 29.2-77.9%) for ICAs and 12.9% (9.0-19.3%) for GBCAs. At multivariable analysis, a significant effect of patient age (ICAs, p = 0.001; GBCAs, p = 0.014), urine volume (p < 0.001 for both), and time interval from contrast administration to urine collection (p < 0.001 for both) on recovery rates was found for both contrast agents; injected contrast volume (p = 0.046) and saline flushing usage (p = 0.008) showed a significant effect only for ICAs.

Conclusion: The high patient enrollment compliance (93%) and potential recovery rates of 51% (ICAs) and 13% (GBCAs) play in favor of sustainable practices in reducing the environmental footprint of contrast agents.

Key points: Question How many patients are willing to extend their stay in radiology by up to 60 min to help reduce the environmental impact of contrast agents? Findings Over 90% of screened patients agreed to extend their stay by up to 60 min and collect their urine in dedicated containers. Clinical relevance Patients demonstrated a high willingness to cooperate in reducing the environmental impact of contrast agents, allowing for a potential recovery of approximately 51% for iodinated and 13% for gadolinium-based contrast agents.

Objective: To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC).

Materials and methods: This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring. K-means clustering (K = 4-6) was applied to obtain multiple sub-regions (n = 3-5), and corresponding histogram features (including mean, standard deviation, minimum, maximum, and 10th, 25th, 50th, 75th, and 90th quantile) of APT and apparent diffusion coefficient (ADC) maps were extracted and filtered using stepwise regression.

Results: When K = 5, the K-means clustering is four sub-regions, showing the best prediction for TB grade compared to K = 4 or 6. When K = 5, there were significantly higher histogram features of the APT map in sub-regions 3 and 4 in the high TB grade group compared to the low-intermediate TB grade group. Receiver operating characteristic (ROC) curve and internal validation suggested that the predictive efficiency of the model was highest when K = 5, with AUC, sensitivity, specificity, accuracy, and kappa values of 0.92, 93%, 71%, 87%, and 0.65, respectively. There were no significant differences in the histogram features of each sub-region in the ADC map (p > 0.05).

Conclusion: The sub-regional histogram features of APTw images can help to distinguish the heterogeneous regions of RC, which can be used to predict the TB grade of RC.

Key points: Question Can the sub-regional histogram features of APTw MRI predict the tumor budding (TB) grade of rectal cancer (RC)? Findings Differences exist in histogram features of APT map subregions between high and low-intermediate TB grade groups; subregions of the APT map have different predictive abilities. Clinical relevance APT-weighted imaging might outperform DWI in predicting TB grade in RC.