Insights into Imaging最新文献

Objectives: To test the Reason for Exam Imaging Reporting and Data System (RI-RADS) in assessing the quality of radiology requests in an Italian cohort of inpatients; to evaluate the interobserver reliability of RI-RADS.

Methods: A single-center quality care study was designed to retrospectively identify consecutive radiology request forms for computed tomography, magnetic resonance imaging, and conventional radiography examinations. One radiologist scored the requests using the RI-RADS. The association between RI-RADS and clinical request variables (urgent request, on-call requests, indication for imaging, requesting specialty, imaging modality, and body region) was evaluated. We calculated interobserver agreement between four readers in a subset of 450 requests.

Results: We included 762 imaging requests. RI-RADS grades A (adequate request), B (barely adequate request), C (considerably limited request), D (deficient request), and X were assigned to 8 (1%), 49 (7%), 237 (31%), 404 (53%), and 64 (8%) of cases, respectively. In the multivariate analysis, the indication for imaging, body region, and requesting specialty significantly influenced the RI-RADS. Indications for imaging with a high risk of poor RI-RADS grade were routine preoperative imaging and device check requests. The upper extremity was the body region with the highest risk of poor RI-RADS grade. Requesting specialties with a high risk of poor RI-RADS grade were cardiovascular surgery, intensive care medicine, and orthopedics. The analysis of the interobserver agreement revealed substantial agreement for the RI-RADS grade.

Conclusion: The majority of radiology exam requests were inadequate according to RI-RADS, especially those for routine imaging. RI-RADS demonstrated substantial reliability, suggesting that it can be satisfactorily employed in clinical settings.

Critical relevant statement: The implementation of RI-RADS can provide a framework for standardizing radiology requests, thereby enabling quality assurance and promoting a culture of quality improvement.

Key points: RI-RADS aims to grade the completeness of radiology requests. Over half of the imaging requests were RI-RADS D grade; RI-RADS demonstrated substantial reliability. Most radiology requests were inadequate and RI-RADS could classify them in clinical practice.

Percutaneous image-guided ablation techniques are a consolidated therapeutic alternative for patients with high preoperative surgical risk for the management of oncological diseases in multiple body districts. Each technique has both pros and cons according to the type of energy delivered, mechanism of action, and site of application. The present article reviews the most recent literature results on ablation techniques applied in the field of genitourinary diseases (kidney, adrenal glands, prostate, and uterus), describing the advantages of the use of each technique and their technical limitations and summarizing the major recommendations from an international consensus meeting. CRITICAL RELEVANT STATEMENT: The article critically evaluates the efficacy and safety of ablation therapies for various genitourinary tract diseases, demonstrating their potential to improve patient outcomes and advance clinical radiology by offering minimally invasive, effective alternatives to traditional surgical treatments. KEY POINTS: Ablation therapies are effective alternatives to surgery for renal cell carcinoma. Ablation techniques offer effective treatment for intermediate-risk prostate cancer. Ablation is a promising tool for adrenal tumor management. Ablation reduces fibroid symptoms and volume, offering an alternative to surgery.

Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI.

Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics.

Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance.

Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis.

Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts.

Key points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.

Objectives: To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).

Materials and methods: A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.

Results: A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.

Conclusion: The proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.

Critical relevance statement: This quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.

Key points: Serosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival.

Objective: This study aims to assess the diagnostic performance of Sonazoid-contrast-enhanced ultrasound (CEUS) in identifying definitive HCC within hepatic nodules in cirrhotic patients, comparing the KLCA-NCC 2022 and APASL 2017 diagnostic guidelines.

Materials and methods: This retrospective study analyzed cirrhotic patients who underwent Sonazoid-CEUS for liver lesion evaluation between October 2019 and October 2023. HCC diagnosis was based on the KLCA-NCC 2022 and APASL 2017 guidelines. Inter-reader agreement on CEUS imaging features and the diagnostic accuracy of the guidelines were evaluated. Sensitivity and specificity comparisons were made using McNemar's test.

Results: Among 400 patients with 432 lesions, CEUS showed excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects. The KLCA-NCC 2022 criteria notably enhanced sensitivity to 96.2%, with specificity and accuracy of 93.8% and 95.8%, respectively. APASL 2017 achieved the highest sensitivity at 97.8%, although specificity dropped to 46.9%, resulting in an accuracy of 90.3%. The KLCA-NCC 2022 guidelines demonstrated significantly higher specificity than APASL 2017 (p < 0.001), while APASL 2017 exhibited the highest sensitivity at 97.8%. Notably, the KLCA-NCC 2022 guidelines also demonstrated an impressive positive predictive value of 98.9%.

Conclusion: Sonazoid-enhanced CEUS, particularly when applied using the KLCA-NCC 2022 guidelines, is an effective diagnostic tool for HCC.

Critical relevance statement: Perfluorobutane CEUS, particularly in accordance with the KLCA-NCC 2022 guidelines, emerges as a valuable adjunct for diagnosing HCC in cirrhotic patients. It demonstrates superior positive predictive value and specificity compared to APASL 2017, underscoring its potential as an effective diagnostic tool.

Key points: Contrast-enhanced (CE)US using Sonazoid with KLCA-NCC 2022 guidelines is highly effective for HCC diagnosis. KLCA-NCC 2022 criteria showed high accuracy, 96.2% sensitivity, and 98.9% PPV. CEUS demonstrated excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects.

Purpose: To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

Methods: From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

Results: Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

Conclusions: The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

Critical relevance statement: Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

Key points: MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

Objective: To compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).

Methods: This prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIR^TM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.

Results: Fifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71-4.40 vs 3.37-3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).

Conclusion: DL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.

Critical relevance statement: DL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.

Key points: DL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases. DL reconstruction showed superior image quality with reduced noise and ringing artifacts. Hepatic anatomic structures were more conspicuous on DL-reconstructed images.

This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods. A comprehensive bias assessment was conducted, including risk assessment and model performance indicators. Ten studies between 2016 and 2023 were analyzed. Studies were mostly retrospective. Patient cohorts varied in size and characteristics, with the number of patients ranging from 79 to 315. The construction of the model involves various radiomic and genotic datasets, and most models show promising prediction performance with the area under the receiver operating characteristic curve (AUC) values ranging from 0.64 to 0.94 and the concordance index (C-index) values from 0.28 to 0.80. The combination model typically outperforms the single method model, indicating higher prediction accuracy and the highest AUC was 0.99. Combining radiomics and genomics in the prognostic model of lung cancer may improve the predictive performance. However, further research on standardized data and larger cohorts is needed to validate and integrate these findings into clinical practice. CRITICAL RELEVANCE STATEMENT: The combination of radiomics and genomics in the prognostic model of lung cancer improved prediction accuracy in most included studies. KEY POINTS: The combination of radiomics and genomics can improve model performance in most studies. The results of establishing prognosis models by different methods are discussed. The combination of radiomics and genomics may be helpful to provide better treatment for patients.

Objectives: To assess the performance of the "dark blood" (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients.

Materials and methods: Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a "contrast-enhancement-boost" technique. Quantitative parameters of aortic wall image quality were evaluated.

Results: Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNR_outer (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNR_outer (p = 0.006), and CNR_inner (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.

Conclusion: DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.

Critical relevance statement: Deep-learning-based "dark blood" images improve vessel wall image wall quality and boundary visualization.

Key points: Dark blood CTA improves image quality and provides better aortic wall visualization. Deep-learning CTA presented higher quality and subjective scores compared to HIR. Combination of dark blood and deep-learning reconstruction obtained the best overall performance.