European Radiology最新文献

Objectives: Tumor collagen is vital in chemotherapy resistance of pancreatic cancer (PC), but its non-invasive evaluation remains challenging. This study aims to investigate the association of variables derived from dual-energy CT with the collagen ratio (CR) of PC and to determine the prognostic value of CR in unresectable diseases.

Materials and methods: A total of 83 patients with resected PC and 71 patients with unresectable PC were enrolled. In the resected group, the correlation between the tumor CR and variables of dual-energy CT was analyzed. In the unresectable group, Cox regression analyses were conducted to investigate the prognostic value of dual-energy CT-predicted CR and other clinicoradiological indicators.

Results: The patients with resected PC were divided into low and high-CR sets with a threshold of 55%. In the resected group, the extracellular volume fraction calculated by the iodine concentration (ECV_IC) was the only predictor of tumor CR according to univariate and multivariate analysis (hazard ratio [HR] (95% confidence interval [CI]):1.19 [1.03-1.37]). The correlation coefficient r was 0.26 (p = 0.02) between ECV_IC and specific CR values. In the training set of unresectable PC group, ECV_IC (HR (95% CI): 0.94 (0.89-0.99), p = 0.03) and contrast-enhanced pattern (CEP) (HR (95% CI): 3.20 (1.41-7.27), p = 0.01) were independent prognostic factors for overall survival. The nomogram model was constructed and showed a good performance.

Conclusion: The ECV_IC is a non-invasive indicator of tumor CR in PC. The ECV_IC and CEP have the potential to predict the prognosis of unresectable PC.

Key points: Question Non-invasive evaluation of tumor collagen, a vital determinant of chemotherapy resistance of pancreatic cancer, remains challenging. Findings Tumor collagen ratio can be noninvasively predicted by extracellular volume fraction based on iodine concentration. Clinical relevance The nomogram model composed of extracellular volume fraction and contrast-enhanced pattern can serve as an effective and convenient tool for stratifying the prognosis of patients with unresectable pancreatic cancer.

Objectives: The aim is to assess the feasibility and accuracy of a novel quantitative ultrasound (US) method based on global speed-of-sound (g-SoS) measurement using conventional US machines, for breast density assessment in comparison to mammographic ACR (m-ACR) categories.

Materials and methods: In a prospective study, g-SoS was assessed in the upper-outer breast quadrant of 100 women, with 92 of them also having m-ACR assessed by two radiologists across the entire breast. For g-SoS, ultrasonic waves were transmitted from varying transducer locations and the image misalignments between these were then related analytically to breast SoS. To test reproducibility, two consecutive g-SoS acquisitions each were taken at two similar breast locations by the same operator.

Results: Measurements were found highly repeatable, with a mean absolute difference ± standard deviation of 3.16 ± 3.79 m/s. Multiple measurements were combined yielding a single g-SoS estimate per each patient, which strongly correlated to m-ACR categories (Spearman's = 0.773). The g-SoS values for categories A-D were 1459.6 ± 0.74, 1475.6 ± 15.92, 1515.6 ± 27.10, and 1545.7 ± 20.62, with all groups (except A-B) being significantly different from each other. Dense breasts (m-ACR C&D) were classified with 100% specificity at 78% sensitivity, with an area under the curve (AUC) of 0.931. Extremely dense breasts (m-ACR D) were classified with 100% sensitivity at 77.5% specificity (AUC = 0.906).

Conclusion: Quantitative g-SoS measurement of the breast was shown feasible and repeatable using conventional US machines, with values correlating strongly with m-ACR assessments.

Key points: Question Breast density is a strong predictor of risk for breast cancer, which frequently develops in dense tissue regions. Therefore, density assessment calls for refined non-ionizing methods. Findings Quantitative global speed-of-sound (g-SoS) measurement of the breast is shown to be feasible using conventional US machines, repeatable, and able to classify breast density with high accuracy. Clinical relevance Being effective in classifying dense breasts, where mammography has reduced sensitivity, g-SoS can help stratify patients for alternative modalities. Ideal day for mammography or MRI can be determined by monitoring g-SoS. Furthermore, g-SoS can be integrated into personalized risk assessment.

Purpose: To train and validate machine learning-derived clinical decision algorithm (_MLCDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning.

Methods: This retrospective study included 458 consecutive primary hyperparathyroidism (PHPT) patients who underwent combined 4D-CT and sestamibi SPECT/CT (MIBI) with subsequent parathyroidectomy from February 2013 to September 2016. The study cohort was divided into training (first 400 patients) and validation sets (remaining 58 patients). Sixteen clinical, laboratory, and imaging variables were evaluated. A random forest algorithm selected the best predictor variables and generated a clinical decision algorithm with the highest performance (_MLCDA). The _MLCDA was trained to predict the probability of a hyperfunctioning vs normal gland for each of the four parathyroid glands in a patient. The reference standard was a four-quadrant location on operative reports and pathology. The accuracy of _MLCDA was prospectively validated.

Results: Of 16 variables, the algorithm selected 3 variables for optimal prediction: combined 4D-CT and MIBI using (1) sensitive reading, (2) specific reading, and (3) cross-product of serum calcium and parathyroid hormone levels and outputted an _MLCDA using five probability categories for hyperfunctioning glands. The _MLCDA demonstrated excellent accuracy for correct classification in the training (4D-CT + MIBI: 0.91 [95% CI: 0.89-0.92]) and validation sets (4D-CT + MIBI: 0.90 [95% CI: 0.86-0.94].

Conclusion: Machine learning generated a clinical decision algorithm that accurately diagnosed hyperfunctioning parathyroid glands through classification into probability categories, which can be implemented for improved preoperative planning and convey diagnostic certainty.

Key points: Question Can an _MLCDA use preoperative variables for the diagnosis of hyperfunctioning parathyroid glands to facilitate surgical planning? Findings The developed _MLCDA demonstrated excellent accuracy for correct classification in the training (0.91 [95% CI: 0.89-0.92]) and validation sets (0.90 [95% CI: 0.86-0.94]). Clinical relevance Using standard preoperative variables, an _MLCDA for diagnosing hyperfunctioning parathyroid glands can be implemented to improve preoperative parathyroid localization and included in radiology reports for surgical planning.

Objective: To investigate imaging biomarkers of tumour response by describing changes in imaging and pathology findings after neoadjuvant radiotherapy (nRT) and exploring their correlations.

Materials and methods: Tumour diameter, volume, and tumour-to-muscle signal intensity (SI) ratio were collected before and after radiotherapy in a cohort of 107 patients with intermediate/high-grade STS and were correlated with post-radiotherapy pathology findings (percentage of necrosis, viable cells, and fibrosis) using Spearman Rank test. Pathological complete response (pCR) was defined as no residual viable cells present, whereas the presence of < 10% viable cells was defined as near-complete pathologic response (near-pCR).

Results: Median amount of necrosis, viable cells, and fibrosis after nRT were 10%, 30%, and 25%, respectively. 7% of patients achieved pCR and 22% near-pCR. No changes in tumour volume were found except for subtypes myxoid liposarcoma (mLPS) -Δ54.47%, undifferentiated pleomorphic sarcoma (UPS) +Δ24.22% and dedifferentiated liposarcoma (dLPS) +Δ35.91%. The median change of tumour-to-muscle SI ratio was -19.7% for the entire population, whereas it was -19.55% and -36.26% for UPS and mLPS, respectively. Correlations (positive and negative) were found between change in volume and the presence of necrosis or fibrosis (r_s = 0.44; r_s = -0.44), as well as between tumour-to-muscle SI ratio and viable cells (r_s = 0.33) or fibrosis (r_s = -0.28).

Conclusion: STS displays extensive heterogeneity in response patterns after nRT. In some subgroups, particularly UPS and mLPS, tumour size changes or tumour-to-muscle SI ratio are significantly linked with the percentage of viable cells, fibrosis, or necrosis.

Key points: Question How do primary soft tissue sarcomas (STS) respond to neoadjuvant therapy, and what correlations exist between pathological findings and imaging characteristics in assessing treatment response? Findings mLPS shrank post-nRT; undifferentiated pleomorphic and dLPSs enlarged. Volume increase correlated with higher necrosis and lower fibrosis; tumour-to-muscle intensity ratio correlated with viable cells. Clinical relevance These findings emphasise the extensive heterogeneity in STS response to nRT across different subtypes. Preoperative correlations between tumour volume and SI changes with necrosis, fibrosis, and viable cells can aid in more precise treatment assessment and prognostication.

Objective: Intensive medical management has been recommended for ischemic stroke of intracranial atherosclerosis (ICAS), but 9.4-15% probability of recurrent stroke remains an inevitable reality. The characteristics of high-risk intracranial plaque that contribute to stroke recurrence after intensive therapy are unclear.

Methods: The patients of acute ischemic stroke due to ICAS from two centers were prospectively analyzed, who underwent the 3D high-resolution head and neck vessel wall magnetic resonance imaging (hr-VW-MRI) at baseline and received intensive medical management within 90 days. The morphological features, such as minimal lumen area (MLA), and histogram parameters including entropy were assessed based on hr-VW-MR images. The recurrence of ischemic events after 6 months was defined as hyperintensity on diffusion-weighted images in the ipsilateral vascular territory. Cox regression analysis was used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for recurrent events.

Results: A total of 222 patients (age 59.5 ± 12.1; males 153) were finally included, and 38 had recurrent stroke after 6 months. After adjusting the age and gender, Cox regression demonstrated that smoking (HR = 4.321; 95% CI, 1.838-10.161; p = 0.001), taking exercise (HR = 0.409; 95% CI, 0.198-0.843; p = 0.015), blood pressure management (HR = 0.180; 95% CI, 0.073-0.443; p = 0.001), MLA (HR = 0.771; 95% CI, 0.625-0.951; p = 0.015) and entropy (HR = 0.274; 95% CI, 0.130-0.576; p = 0.001) were significant predictors of recurrent ischemic stroke. However, the area under curve value of MRI parameters was significantly higher than that of traditional clinical factors (0.86 vs 0.79; p = 0.01).

Conclusions: The plaque characteristics based on hr-VW-MRI may provide complementary values over traditional clinical features in predicting ischemic recurrence for ICAS.

Key points: Question The study addresses recurrent ischemic stroke in intracranial atherosclerosis patients, identifying high-risk plaque features that contribute to recurrence despite intensive medical management. Findings Plaque features on high-resolution vessel wall magnetic resonance imaging (hr-VW-MRI), such as minimal lumen area and entropy, improve prediction of stroke recurrence over clinical factors. Clinical relevance This two-center prospective study improves patient care by using hr-VW-MRI and histogram factors like entropy to better predict stroke recurrence, allowing for more personalized treatment strategies and potentially reducing ischemic events in patients with intracranial atherosclerosis.

Objectives: To evaluate the quality of radiomics research in prostate MRI for the evaluation of prostate cancer (PCa) through the assessment of METhodological RadiomICs (METRICS) score, a new scoring tool recently introduced with the goal of fostering further improvement in radiomics and machine learning methodology.

Materials and methods: A literature search was conducted from July 1st, 2019, to November 30th, 2023, to identify original investigations assessing MRI-based radiomics in the setting of PCa. Seven readers with varying expertise underwent a quality assessment using METRICS. Subgroup analyses were performed to assess whether the quality score varied according to papers' categories (diagnosis, staging, prognosis, technical) and quality ratings among these latter.

Results: From a total of 1106 records, 185 manuscripts were available. Overall, the average METRICS total score was 52% ± 16%. ANOVA and chi-square tests revealed no statistically significant differences between subgroups. Items with the lowest positive scores were adherence to guidelines/checklists (4.9%), handling of confounding factors (14.1%), external testing (15.1%), and the availability of data (15.7%), code (4.3%), and models (1.6%). Conversely, most studies clearly defined patient selection criteria (86.5%), employed a high-quality reference standard (89.2%), and utilized a well-described (85.9%) and clinically applicable (87%) imaging protocol as a radiomics data source.

Conclusion: The quality of MRI-based radiomics research for PCa in recent studies demonstrated good homogeneity and overall moderate quality.

Key points: Question To evaluate the quality of MRI-based radiomics research for PCa, assessed through the METRICS score. Findings The average METRICS total score was 52%, reflecting moderate quality in MRI-based radiomics research for PCa, with no statistically significant differences between subgroups. Clinical relevance Enhancing the quality of radiomics research can improve diagnostic accuracy for PCa, leading to better patient outcomes and more informed clinical decision-making.

Aim: This systematic review and meta-analysis investigate the added value of structured integration of Breast Imaging Reporting and Data System (BI-RADS) criteria using the Kaiser score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions.

Material and methods: A systematic review and meta-analysis were conducted using predefined criteria. Eligible articles, published in English until May 2024, dealt with KS in the context of BI-RADS 4 MRI. Two reviewers extracted study characteristics, including true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were calculated using bivariate random effects. Fagan nomograms identified the maximum pre-test probability at which post-test probabilities of a negative MRI aligned with the 2% malignancy rate benchmark for downgrading BI-RADS 4 to BI-RADS 3. I² statistics and meta-regression explored sources of heterogeneity. p-values < 0.05 were considered significant.

Results: Seven studies with 1877 lesions (833 malignant, 1044 benign) were included. The average breast cancer prevalence was 47.3%. Pooled sensitivity was 94.3% (95%-CI 88.9%-97.1%), and pooled specificity was 68.1% (95%-CI 56.6%-77.7%) using a random effects model. Overall, 52/833 cases were FNs (6.2%). Fagan nomograms showed that KS could rule out breast cancer in BI-RADS 4 lesions at a pre-test probability of 20.3% for all lesions, 25.4% for masses, and 15.2% for non-mass lesions.

Conclusions: In MRI-assessed BI-RADS 4 lesions, applying structured BI-RADS criteria with the KS reduces unnecessary biopsies by 70% with a 6.2% FN rate. Breast cancer can be ruled out up to pre-test probabilities of 20.3%.

Key points: Question What, if any, value is added by structured integration of BI-RADS criteria using the Kaiser Score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions? Findings The structured integration of BI-RADS criteria using the Kaiser Score (KS) reduces unnecessary biopsies in BI-RADS 4 lesions by 70%. Clinical relevance The structured approach offered by the Kaiser Score (KS) avoids unnecessary recalls, potentially reducing patient anxiety, lessening the burden on medical personnel, and the need for further imaging and biopsies due to more objective and thus efficient clinical decision-making in evaluating BI-RADS 4 lesions.

Background: There is an increasing need for objective treatment monitoring in perianal fistulising Crohn's disease (pfCD). Therefore, the magnetic resonance novel index for fistula imaging in CD (MAGNIFI-CD) index has been designed and internally validated on the ADMIRE-CD trial cohort. The aim of this study was to externally validate the MAGNIFI-CD index to monitor response to medical and surgical treatment regimens in pfCD.

Methods: A retrospective longitudinal cohort was established of consecutive patients with complex pfCD treated with surgical and/or medical therapy and a baseline and follow-up MRI between January 2007 and May 2021. The MAGNIFI-CD index was scored by two independent, abdominal radiologists blinded for time points and clinical outcomes. Responsiveness, reliability, and test accuracy regarding clinically important improvement were assessed. Cut-offs for response and remission were selected classified on fistula drainage assessment and physician global assessment.

Results: A total of 65 patients (51% female, median age 32 years) were included. A clinically relevant responsiveness of the MAGNIFI-CD was shown, with a significant decrease in clinical remitters and responders with a median MAGNIFI-CD of 18.0 [7.5-20.0] to 9.0 [0.8-16.0] (p < 0.001) and non-significant change in non-responders with a median MAGNIFI-CD of 20.0 [12.0-23.0] to 18.0 [13.0-21.0] (p = 0.22). There was an 'almost perfect' interobserver agreement (ICC = 0.87; 95% CI 0.80-0.92) for the MAGNIFI-CD index. An optimal cut-off value was defined as a decrease of 2 points for clinical response, and a MAGNIFI-CD ≤ 6 for remission at follow-up MRI.

Conclusion: The MAGNIFI-CD index is a responsive and reliable MRI scoring instrument for treatment monitoring in perianal fistulising Crohn's disease.

Clinical relevance statement: The MAGNIFI-CD index is a well-structured, responsive scoring instrument to assess fistula severity and activity that allows quantitative detection of changes in therapy response in patients with perianal fistulising Crohn's disease, thereby facilitating endpoints in clinical trials.

Key points: Well-defined cut-offs for response and remission are needed for objective treatment monitoring of perianal fistulising Crohn's disease (pfCD). Cut-off values for remission and for response at 6 months follow-up were defined. Interobserver agreement was good. The MAGNIFI-CD index is responsive and reliable for treatment monitoring and is suitable for use in clinical trials.

Objectives: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents.

Materials and methods: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs.

Results: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance.

Conclusion: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions.

Clinical relevance statement: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses.

Key points: Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma.