Insights into Imaging最新文献

Objectives: To elucidate the research training exposure of radiology residents across ESR country members.

Methods: A 30-question survey was constructed by the Radiology Trainee Forum and was distributed among residents and subspecialty fellows of countries members of the ESR. The survey examined the training environment, the status of research training and publications among trainees, the conditions under which research was conducted, and the exposure to activities such as grant proposal preparation and manuscript reviewing. Descriptive statistics and the chi-square test were used to assess the responses to survey questions and evaluate factors related to these responses.

Results: A total of 159 participants from 29 countries provided fully completed questionnaires. Only 12/159 trainees already had a PhD degree and nearly half had never published a PubMed-indexed manuscript (76/159, 47.8%). Among those who published their papers during radiology training, most did so in the first or second year of residency (n = 26 and n = 20 participants, respectively). Most participants (79%) did not receive further statistical training during residency, fifty-five out of 159 (34.59%) respondents never had any guidance/training on how to read a paper and 58 out of 159 (36.48%) had never been encouraged to participate in any research. Most of them had worked after hours to carry out research at least a few times (47/159, 29.56%) or always (82/159, 51.57%).

Conclusion: Analysis of research training among radiology trainees was performed. Areas for improvement were identified that can prompt changes in training curricula to prepare a highly competent European workforce.

Critical relevance statement: This survey has identified deficits in research training of radiology residents across countries members of ESR, pinpointing areas for improvement to fortify the future of radiology in Europe.

Key points: Research exposure and training of radiology residents varies across countries and members of ESR. Radiology residents largely lack systematic research training, dedicated research time, and guidance. Areas for improvement in research training of radiology residents have been identified, aiding the fortification of radiology research across Europe.

In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January-March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. CRITICAL RELEVANCE STATEMENT: This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. KEY POINTS: The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance.

Objectives: Repeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).

Methods: Nine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.

Results: First-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42-0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70-0.84 vs 0.14-0.64). Features with smaller bin widths had higher repeatability (median ICC of 8-128: 0.65-0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).

Conclusions: Applying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.

Critical relevance statement: The exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.

Key points: The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.

Objectives: To analyze the lung structure of small airway dysfunction (SAD) defined by spirometry and parametric response mapping (PRM) using high-resolution computed tomography (HRCT), and to analyze the predictive factors for SAD.

Methods: A prospective study was conducted with 388 participants undergoing pulmonary function test (PFT) and inspiratory-expiratory chest CT scans. The clinical data and HRCT assessments of SAD patients defined by both methods were compared. A prediction model for SAD was constructed based on logistic regression.

Results: SAD was defined in 122 individuals by spirometry and 158 by PRM. In HRCT visual assessment, emphysema, tree-in-bud sign, and bronchial wall thickening have higher incidence in SAD defined by each method. (p < 0.001). Quantitative CT showed that spirometry-SAD had thicker airway walls (p < 0.001), smaller lumens (p = 0.011), fewer bronchi (p < 0.001), while PRM-SAD had slender blood vessels. Predictive factors for spirometry-SAD were age, male gender, the volume percentage of emphysema in PRM (PRM^Emph), tree-in-bud sign, bronchial wall thickening, bronchial count; for PRM-SAD were age, male gender, BMI, tree-in-bud sign, emphysema, the percentage of blood vessel volume with a cross-sectional area less than 1 mm² (BV1/TBV). The area under curve (AUC) values for the fitted predictive models were 0.855 and 0.808 respectively.

Conclusions: Compared with PRM, SAD defined by spirometry is more closely related to airway morphology, while PRM is sensitive to early pulmonary dysfunction but may be interfered by pulmonary vessels. Models combining patient information and HRCT assessment have good predictive value for SAD.

Critical relevance statement: HRCT reveals lung structural differences in small airway dysfunction defined by spirometry and parametric response mapping. This insight aids in understanding methodological differences and developing radiological tools for small airways that align with pathophysiology.

Key points: Spirometry-SAD shows thickened airway walls, narrowed lumen, and reduced branch count, which are closely related to airway morphology. PRM shows good sensitivity to early pulmonary dysfunction, although its assessment of SAD based on gas trapping may be affected by the density of pulmonary vessels and other lung structures. Combining patient information and HRCT features, the fitted model has good predictive performance for SAD defined by both spirometry and PRM (AUC values are 0.855 and 0.808, respectively).

Objectives: The study aimed to investigate the relationship between the radiomic features of perivascular adipose tissue (PVAT) and abdominal aortic aneurysm (AAA) growth after endovascular aneurysm repair (EVAR).

Methods: Patients with sub-renal AAA who underwent regular follow-up after EVAR between March 2014 and March 2024 were retrospectively collected. Two radiologists segmented aneurysms and PVAT. Patients were categorised into growing and non-growing groups based on volumetric changes observed in two follow-up computed tomography examinations. One hundred seven radiomic features were automatically extracted from the PVAT region. Univariable and multivariable logistic regression was performed to analyse radiomic features and clinical characteristics. Furthermore, the performance of the integrated clinico-radiological model was compared with models using only radiomic features or clinical characteristics separately.

Results: A total of 79 patients (68 ± 9 years, 89% men) were enroled in this study, 19 of whom had a growing aneurysm. Compared to the non-growing group, PVAT of growing AAA showed a higher surface area to volume ratio (non-growing vs growing, 0.63 vs 0.70, p = 0.04), and a trend of low dependence and high dispersion manifested by texture features (p < 0.05). The area under the curve of the integrated clinico-radiological model was 0.78 (95% confidence intervals 0.65-0.91), with a specificity of 87%. The integrated model outperformed models using only radiomic or clinical features separately (0.78 vs 0.69 vs 0.69).

Conclusions: Higher surface area to volume ratio and more heterogeneous texture presentation of PVAT were associated with aneurysm dilation after EVAR. Radiomic features of PVAT have the potential to predict AAA progression.

Clinical relevance statement: Radiomic features of PVAT are associated with AAA progression and can be an independent risk factor for aneurysm dilatation to assist clinicians in postoperative patient surveillance and management.

Key points: After EVAR for AAA, patients require monitoring for progression. PVAT surrounding growing AAA after EVAR exhibits a more heterogeneous texture. Integrating PVAT-related features and clinical features results in better predictive performance.

Objectives: To assess the role of 4D flow MRI in the assessment of gastro-oesophageal varices and in the prediction of high-risk varices in patients with chronic liver disease.

Methods: Thirty-eight patients diagnosed with either oesophageal or gastric varices were included in this single-centre prospective study. 4D flow MRI was used to calculate peak flow, average flow and peak velocity at the portal vein confluence (PV1) and hilum (PV2), splenic vein hilum (SV1) and confluence (SV2), and superior mesenteric vein (SMV). PV and SV fractional flow changes were also measured.

Results: ROC analysis revealed that both PV2 average flow and PV fractional average flow change had 100% sensitivity to predict high-risk patients with the PV fractional peak flow change having the widest area under the curve (AUC) and the highest specificity (92.3%). SV1 average flow, SV2 average flow, SV2 peak flow, and SV2 peak velocity increased significantly in patients with oesophageal compared to gastric varices included (p = 0.022, < 0.001, < 0.001 and 0.001, respectively).

Conclusion: Based on certain porto-mesenteric blood flow, velocity, and fractional flow change parameters, 4D flow MRI showed excellent performance in identifying high-risk patients and giving an idea about the grade and location of varices.

Critical relevance statement: Variceal bleeding is a major consequence of unidentified risky upper GI varices. Thus, by identifying and locating high-risk varices early, either oesophageal or gastric, using a non-invasive method like MRI, adverse events might be avoided.

Key points: 4D flow MRI can be used as a potential alternative for endoscopy to predict patients with high-risk varices. Based on portal vein fractional flow change, splenic flow and velocity, 4D MRI can predict and locate high-risk varices. Earlier identification of high-risk varices can allow for interventions to prevent adverse events.

Objectives: Creeping fat (CF) is associated with stricture formation in Crohn's disease (CD). This study evaluated the feasibility of intestinal ultrasound (IUS) for semiquantitative analysis of CF and compared the agreement between IUS and computed tomography enterography (CTE).

Methods: In this retrospective study, we recruited consecutive CD patients who underwent IUS and CTE. CF wrapping angle was analyzed on the most affected bowel segment and was independently evaluated by IUS and CTE. We evaluated the wrapping angle of CF in the cross- and vertical sections of the diseased bowel. CF wrapping angle was divided into < 180° and ≥ 180°. IUS performance was assessed using CTE as a reference standard, and IUS interobserver consistency was evaluated.

Results: We enrolled 96 patients. CTE showed that CF wrapping angle was < 180° in 35 patients and ≥ 180° in 61 patients. We excluded three cases in which the observation positions were inconsistent between the IUS and CTE. Excellent agreement was shown between US and CTE (82/93, 88.2%). The eleven remaining cases showed inconsistencies mostly in the terminal ileum (n = 5) and small intestine (n = 4). Total agreement between IUS observers was 89.6% (86/96, κ = 0.839, p = 0.000), with perfect agreement for the ileocecal and colonic segments (35/37, 94.6% and 20/21, 95.2%, respectively) and moderate agreement for small intestinal segments (16/21, 76.2%).

Conclusions: IUS could be of value and complementary to CTE for assessing CF, particularly in patients with affected terminal ileum and colon. IUS is a non-invasive technique for monitoring CD patients.

Critical relevance statement: In our study, excellent agreement was shown between intestinal US observers as well as between US and CT enterography (CTE) for assessing creeping fat (CF), which showed that ultrasound could be of value and complementary to CTE.

Key points: Creeping fat (CF) is a potential therapeutic target in Crohn's disease. Excellent agreement was shown between US and CT Enterography (CTE) for assessing CF. Ultrasound could be complementary to CTE for assessing CF.

Objectives: To establish superb microvascular imaging (SMI) based thyroid imaging reporting and data system (SMI TI-RADS) for risk stratification of malignancy in thyroid nodules.

Methods: In total, 471 patients, comprising 643 thyroid nodules, who received conventional ultrasound (US), SMI, and a final diagnosis were extensively analyzed. A qualitative assessment of US features of the nodules was performed followed by univariable and multivariable logistic regression analyses, leading to the construction of the SMI TI-RADS, which was further verified using internal and external validation cohorts.

Results: Among the stand-alone US, predictive factors were the shape and margins of the nodules, echogenicity and echogenic foci, vascularity, extrathyroidal extension, ring-SMI patterns, penetrating vascularity, flow-signal enlarged, and vascularity area ratio. SMI TI-RADS depicted an enhanced area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI: 0.92, 0.96; p < 0.001 relative to other stratification systems), a 79% biopsy yield of malignancy (BYM, 189/240 nodules), and a 21% unnecessary biopsy rate (UBR, 51/240 nodules). In the verification cohorts, we demonstrated AUCs, malignancy biopsy yields, and unnecessary biopsy rates of 0.88 (95% CI: 0.83, 0.94), 79% (59/75 nodules), and 21% (16/75 nodules) for the internal cohort, respectively, and 0.91 (95% CI: 0.85, 0.96), 72% (31/43 nodules), and 28% (12/43 nodules) for the external cohort, respectively.

Conclusion: SMI TI-RADS was found to be superior in diagnostic sensitivity, specificity, and efficiency than existing TI-RADSs, showing better stratification of the malignancy risk, and thus decreasing the rate of unnecessary needle biopsy.

Critical relevance statement: To develop an imaging and data system based on conventional US and SMI features for stratifying the malignancy risk in thyroid nodules.

Key points: SMI features could improve thyroid nodule risk stratification. SMI TI-RADS showed superior diagnostic efficiency and accuracy for biopsy guidance. SMI TI-RADS can provide better guidance for clinical diagnosis and treatment of thyroid nodules.