Academic Radiology最新文献

Rationale and objectives: To evaluate the diagnostic performance of preoperative computed tomography (CT) and magnetic resonance imaging (MRI)-based radiomics models in detecting liver metastases in patients with colorectal cancer (CRC).

Materials and methods: Following PRISMA 2020 guidelines, we systematically searched major databases up to July 2025. Study selection, data extraction, and quality assessment (Radiomics Quality Score and QUADAS-2) were performed independently. Separate bivariate random-effects meta-analyses were conducted for prognostic (metachronous) and diagnostic (synchronous) predictions.

Results: Twenty studies (3765 patients) were included in the systematic review. Twenty studies were included in the systematic review. Of these, 18 studies were included in the quantitative meta-analysis. For predicting metachronous metastases (13 studies), the pooled AUC was 0.83 (95% CI: 0.73-0.90), although significant publication bias suggested that this estimate may be optimistically inflated. For the detection of synchronous metastases (five studies), the pooled AUC was 0.85 (95% CI: 0.76-0.91). Heterogeneity was moderate to substantial. However, significant publication bias was detected for prognostic models (Deeks' test, P < 0.001), suggesting that these pooled estimates may be optimistically inflated.

Conclusion: Radiomics has the potential to predict metachronous and detect synchronous liver metastases in CRC. However, methodological weaknesses (mean Radiomics Quality Score ∼48%), geographic bias, and publication bias limit this evidence. Multinational validation is required before clinical application of the findings.

Rationale and objectives: Inflammation plays a crucial role in the pathophysiology of intracranial aneurysms (IAs). Our previous research demonstrated that blood inflammatory indices can serve as predictors of aneurysmal wall enhancement (AWE), which signifies the presence of inflammation within the aneurysmal wall and functions as an imaging biomarker for IA instability. Here, we aimed to further examine the relationship between blood inflammatory indices, AWE, and long-term clinical outcomes in patients with intracranial fusiform aneurysms (IFAs).

Materials and methods: We reviewed patients with IFAs who underwent high-resolution magnetic resonance imaging and blood laboratory tests, as recorded in our maintained database. Initially, a cross-sectional study was conducted to identify potential blood inflammatory indices that could predict the average value of aneurysmal wall enhancement in three dimensions (3D-AWE_avg). Subsequently, a follow-up study was performed to further elucidate the potential predictors associated with overall poor outcomes (CAO) in the same cohort.

Results: A total of 92 patients were included in the cross-sectional study. Both the systemic inflammation response index (SIRI), the systemic immune-inflammation index (SII), and the neutrophil-to-lymphocyte ratio (NLR) were associated with 3D-AWE_avg in univariate analysis; however, only SIRI was found to independently predict 3D-AWE_avg (P = 1.1 × 10^-5). In the follow-up study, 64 patients were included, with a mean follow-up period of 29.27 months. SIRI (13.725 [2.467-76.349], P = .003) and 3D-AWE_avg (5.387 [1.320-21.988], P = .019) were identified as the predictors of CAO in patients with IFAs. Furthermore, patients with a high SIRI value (≥0.725 × 10⁹/L, log-rank = 0.002) or a 3D-AWE_avg ≥ 0.604 (log-rank = 3 × 10^-5) had significantly higher risk of CAO.

Conclusion: SIRI predicts both aneurysmal wall enhancement and long-term adverse outcomes in patients with IFAs, supporting its potential role as a novel biomarker for risk stratification and clinical decision-making in this population.

Rationale and objectives: This study aims to compare the breast magnetic resonance imaging (MRI) features and clinicopathological characteristics of invasive breast cancer patients with different enhancement patterns, and to investigate the relationship between enhancement patterns and lymphovascular invasion (LVI).

Materials and methods: This retrospective study consecutively enrolled 1185 female patients with invasive breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) at our institution. Propensity score matching (PSM) was employed to match patients between mass enhancement (ME) group and non-mass enhancement (NME) group. With the occurrence of LVI as the clinical endpoint, covariates with a standardized mean difference (SMD) greater than 0.1 and the enhancement patterns were incorporated into a Firth's bias-reduced logistic regression analysis to further evaluate the relationship between enhancement patterns and LVI.

Results: Compared to the ME group, lesions in the NME group demonstrated significantly higher rates of axillary lymph node positivity and LVI (both P<0.001). After PSM, differences in ADC values and the distribution of the triple-negative subtype persisted between the two groups. Firth regression analysis identified NME as a risk factor for LVI (P<0.001). Compared to the Luminal A subtype, the Luminal B (P<0.001), HER2-positive (P<0.001), and triple-negative (P=0.001) subtypes were all associated with a significantly increased risk of LVI. ADC value did not demonstrate a significant association with LVI (P=0.537).

Conclusion: NME was identified as a significant independent risk factor for LVI. Compared to the Luminal A subtype, the Luminal B, HER2-positive, and triple-negative subtypes were all associated with a significantly increased risk of LVI. The ADC value did not demonstrate a significant association with LVI.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic and prognostic utility of the Node Reporting and Data System (Node-RADS) using preoperative MRI.

Methods: This was a retrospective, single-center cohort study of 126 consecutive patients with newly diagnosed rectal adenocarcinoma who underwent preoperative pelvic MRI and curative-intent surgery between 2017 and 2023. Regional nodes were graded using Node-RADS. Standard MRI descriptors (T stage, tumor length, and tumor location) and high-risk features-including MRI-detected extramural venous invasion (mrEMVI) and circumferential resection margin (CRM) involvement-were recorded. Serum tumor markers (CEA and CA19-9) obtained proximate to imaging were assessed. Associations with oncologic outcomes (RFS and OS) were examined using multivariable models adjusted for clinicopathologic covariates; nomogram model performance was quantified by discrimination, calibration, and decision-curve analysis. SHapley Additive exPlanations (SHAP) was used to quantify variable contributions and enhance model interpretability.

Results: In the assessment of regional lymph node status, Node-RADS achieved an AUC of 0.861. When a Node-RADS score ≥4 was used as the positivity threshold, the diagnostic accuracy reached 0.841. During a median follow-up of 56.2 months, the Kaplan-Meier estimated 3-year RFS and OS were 85.0% and 88.2%, and the corresponding 5-year RFS and OS were 82.0% and 78.6%, respectively. Multivariable analysis revealed that Node-RADS, age, and CA19-9 were independent predictors of OS, whereas Node-RADS, clinical EMVI, and CA19-9 were independent predictors of RFS. Based on these factors, nomograms for OS and RFS prediction were developed. For OS prediction, the 3-year and 5-year AUCs were 0.825 and 0.831, respectively, with a C-index of 0.821 being observed. For RFS prediction, the 3-year and 5-year AUCs were 0.741 and 0.786, respectively, with a C-index of 0.726 being observed. SHAP analysis ranked Node-RADS as the primary contributor to RFS predictions (mean |ϕ|=0.6) and the secondary contributor to OS predictions (mean |ϕ|=0.4).

Conclusion: Preoperative MRI-based Node-RADS has diagnostic and prognostic utility and may serve as a standardized imaging biomarker for preoperative risk stratification, supporting individualized treatment and surveillance.

Purpose: Leveraging hypergraph theory and spatio-temporal graph convolutional network (ST-GCN), this study uses multimodal MRI to elucidate thalamus-mediated high-order network dyscoordination of motor impairment in Parkinson's disease (PD).

Materials and methods: 64 PD patients and 64 age- and sex-matched healthy controls (HC) underwent resting-state functional MRI (rs-fMRI) and T1-weighted anatomical imaging (T1WI). Functional hypergraphs were constructed using dynamic thresholds on Pearson correlations; structural hypergraphs were generated from gray matter volume (GMV) via k-nearest neighbors (KNN). ST-GCN was employed to fuse the multimodal hypergraph features, and discriminative features were identified via stratified five-fold cross-validation. Group differences and clinical correlations were assessed using t-tests/Mann-Whitney U and Spearman's correlation (P<0.05), respectively.

Results: Compared to HC, eight key brain regions exhibited abnormalities in PD: left precentral gyrus (PreCG.L), left middle frontal gyrus (MFG.L), right superior occipital gyrus (SOG.R), left thalamus (THA.L), left hippocampus (HIP.L), right caudate nucleus (CAU.R), right supplementary motor area (SMA.R), and right paracentral lobule (PCL.R). Three significant hyperedges were identified: left putamen-left thalamus-right supplementary motor area (PUT.L-THA.L-SMA.R), right globus pallidus-right thalamus-right cerebellar Crus II (PAL.R-THA.R-Crus II.R), and left thalamus-left hippocampus-right angular gyrus (THA.L-HIP.L-ANG.R). Hyperedge strengths revealed a modest increase in PUT.L-THA.L-SMA.R, a significant increase in THA.L-HIP.L-ANG.R (P<0.05), and a reduction in PAL.R-THA.R-Crus II.R. These hyperedges were all positively correlated with UPDRS-III scores (P<0.05).

Conclusion: Multimodal hypergraph analysis reveals high-order network dysregulation of motor impairment in PD, involving the cerebellum, limbic system, and cortical-basal ganglia circuits, mediated by the thalamus. Furthermore, hyperedges may serve as potential biomarkers for motor dysfunction.

Rationale and objectives: This study aimed to develop a nomogram integrating extracellular volume fraction (ECV), dual-energy CT (DECT) quantitative parameters, and morphological features to predict perineural invasion (PNI) and recurrence-free survival (RFS) in gastric cancer (GC).

Materials and methods: We retrospectively collected GC patients' data from two centers. Two radiologists independently assessed ECV, DECT quantitative parameters, and morphological features. Multivariate logistic regression analyses were performed to identify independent risk factors for PNI and construct a predictive nomogram. The nomogram's predictive performance was evaluated using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Multivariate Cox regression analyses were conducted to determine independent prognostic factors for RFS. Kaplan-Meier survival curves were generated to compare RFS between nomogram predicted PNI-positive and PNI-negative groups.

Results: A total of 268 patients were included in the analysis, with 166 in the training cohort and 102 in the validation cohort. ECV, NICdelay, and ctEMVI were identified as independent risk factors for PNI. The nomogram demonstrated good predictive performance for PNI, with area under the ROC curve (AUC) of 0.822 and 0.810 in the training and validation cohorts. Calibration curves indicated good agreement between predicted and observed PNI, and DCA demonstrated clinical utility. Nomogram-predicted PNI was an independent prognostic factor for RFS, with the predicted PNI-positive group exhibiting significantly lower RFS rate than the predicted PNI-negative group.

Conclusion: A nomogram integrating ECV, DECT quantitative parameters, and morphological features could effectively predict PNI in GC and provide significant prognostic value for postoperative RFS.

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Rationale and objectives: This study aimed to preoperatively predict the invasiveness of ground-glass nodules (GGNs) using habitat-based radiomics derived from dual-energy computed tomography (CT).

Materials and methods: This study retrospectively included 199 patients (267 GGNs) in the internal cohort and 52 patients (54 GGNs) in the external cohort, all with histologically confirmed minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). All patients underwent conventional CT imaging using a dual-layer spectral detector system, and electron density (ED) images were reconstructed from the spectral base images. Habitat subregions were delineated by applying k-means clustering to voxel intensity values, and radiomic features were subsequently extracted from these subregions. The internal cohort was randomly divided into training and test sets in a 7:3 ratio. Extreme gradient boosting (XGBoost) models were developed, and model performance was evaluated using area under the curve (AUC) with 95% confidence intervals (CIs).

Results: For nonhabitat models, the ED-based approach outperformed conventional CT in both the internal test set (AUC: 0.7491; 95% CI, 0.5930-0.8799 vs. 0.7303; 95% CI, 0.6005-0.8514) and the external test set (AUC 0.7366; 95% CI, 0.5986-0.8586 vs. 0.7090; 95% CI, 0.5556-0.8436). For habitat-based models, the integration of subregional features from both ED and conventional CT images achieved the best predictive performance, yielding an AUC of 0.9052 (95% CI, 0.8255-0.9676) in the internal test set and 0.8414 (95% CI, 0.7215-0.9361) in the external test set.

Conclusion: This habitat-based radiomics strategy may offer a promising approach for preoperative differentiation of IAC from MIA in pulmonary GGNs, supporting personalized treatment planning.

Introduction: Artificial intelligence (AI) is increasingly being used to support diagnostic accuracy and efficiency in radiology. While its technical potential is well recognized, little is known about how patients perceive these tools or whether their expectations align with clinical adoption. We aimed to synthesize literature capturing patient perceptions of the use of AI in radiology.

Methods: This was a scoping review of empirical literature that has explored patient perceptions of AI in radiology. We conducted a comprehensive search across Medline, Embase and Google Scholar for studies published before December 2025. Eligible studies focused on patient views regarding AI in any radiologic context. Data were synthesized using descriptive and thematic analysis.

Results: Out of 5284 abstracts screened, 18 studies were included, representing 11 countries and 6574 patients. Six key themes emerged: (i) Trust and confidence in AI, (ii) Need for human oversight, (iii) Understanding and literacy, (iv) Emotional reactions to AI, (v) Accountability, and (vi) Expectations from AI. Patients expressed cautious interest in AI applications but emphasized the need for radiologist involvement. They also showed a preference for using AI as a supportive tool rather than as a replacement for clinicians.

Conclusion: Patients are central to the integration of AI in radiology, yet literature examining patient perceptions of the use of AI in radiology is scarce. In the era of AI-driven technology, understanding and incorporating patient views is essential to the successful and ethical implementation of AI in radiology.

Objectives: To establish normative T1 and T2 relaxation times for the liver, pancreas, and kidneys at 5.0 T MRI, providing reference data for imaging parameter optimization and quantitative diagnosis.

Materials and methods: Standardized T1/T2 phantoms were used for in vitro validation. From January to March 2025, healthy adults underwent 5.0 T abdominal MRI. T1/T2 maps were obtained using Modified Look-Locker Inversion Recovery (MOLLI) and T2-prepared gradient echo (T2 prep) sequences. Values, reproducibility, and correlations with age, sex, and other factors were evaluated.

Results: Results from the 10 tubes showed excellent agreement with MOLLI-based T1 and T2-prep-based T2 values and reference standards. The final cohort consisted of 41 participants. In healthy volunteers at 5.0 T MRI, mean T1 values were 1131.68±123.03ms(liver), 1164.63±57.81ms(pancreas), 1753.62±80.44ms (kidney cortex), and 2175.06±100.80ms (kidney medulla); mean T2 values were 31.16±3.37ms, 47.48±4.55ms, 59.36±6.35ms, and 41±4.07ms, respectively. T1 and T2 showed excellent reproducibility. Correlation analysis demonstrated a significant linear negative correlation between R2* and liver T1, and the R2* corrected liver T1 was 1156.57±69.25ms. Liver T2 decreased with age, and both measured and corrected liver T1 decreased with waist-to-hip ratio. Measured T1 and T2 of liver and kidney (cortex and medulla) were all lower in males than females, but liver corrected T1 showed no sex difference.

Conclusion: This study reports normative T1 and T2 relaxation times of the liver, pancreas, renal cortex, and medulla at 5.0 T MRI, confirming the method's feasibility and reproducibility, thus providing reference values for parameter optimization and future disease-related research.

Key points: Question Quantitative 5.0 T MRI is diagnostically significant, but normative reference values for abdominal T1/T2 relaxation times are currently lacking. Findings We established normative T1/T2 values for abdominal organs at 5.0 T MRI, providing a reference for protocol optimization and diagnosis. Clinical relevance This study establishes normative T1 and T2 relaxation times for abdominal organs at 5.0 T MRI. These reproducible, quantitative reference values are essential for optimizing imaging protocols and provide a robust baseline for diagnosing abdominal diseases.