Academic Radiology最新文献

Rationale and objectives: The metabolic predominance of hepatocellular carcinoma (HCC) on dual-tracer PET/CT with C-11 acetate and F-18 fluorodeoxyglucose (FDG) reflects tumor differentiation, but a practical MRI-based biomarker for predicting this phenotype remains needed. This study aimed to assess whether enhancement patterns on gadoxetic acid-enhanced liver MRI can differentiate the metabolic predominance of HCC.

Materials and methods: This exploratory retrospective study included patients with HCC who underwent both dual-tracer PET/CT and liver MRI between January 2015 and December 2021. HCC lesions were categorized as acetate- or FDG-dominant based on tracer avidity. Signal intensity (SI) in each MRI phase was quantified using the percentage signal ratio (PSR), defined as the ratio of SI in adjacent normal liver parenchyma to that in the lesion. The area under the receiver operating characteristic curve (AUC) was measured to determine the MRI phase that best distinguished the metabolic predominance of HCC lesions.

Results: In 38 patients (mean age, 62.0±8.0 years) with 48 HCC lesions, median PSR values were lower in acetate-dominant group (28 lesions) than in FDG-dominant group (20 lesions) during the early arterial (64.7 vs. 113.9; P<0.001), late arterial (103.8 vs. 135.4; P<0.001), and portal venous (114.5 vs. 142.3; P = 0.001) phases, but not in the hepatobiliary phase (171.6 vs. 161.9; P = 0.390). The AUC was highest in the early arterial phase (0.99; 95% CI: 0.96, 1.00; P<0.001), where a PSR cutoff of 95.2 effectively distinguished the metabolic predominance of HCC.

Conclusion: PSR on liver MRI, especially in the early arterial phase, differentiates acetate- from FDG-dominant HCCs, indicating metabolic predominance.

Rationale and objectives: Contrast-enhanced ultrasound (CEUS) is an effective method for assessing varicocele (VC) hemodynamics; however, its value in predicting improvements in semen parameters after varicocelectomy remains unclear. This study investigated whether CEUS-based hemodynamic patterns could predict surgical outcomes and developed and validated a novel predictive model.

Materials and methods: In this prospective cohort study, 187 patients with VC undergoing microscopic varicocelectomy were included. A total of 23 clinical, grayscale ultrasound, and CEUS-based hemodynamic parameters were collected. The primary outcome was semen parameter improvement (≥25% increase in total motile sperm count) six months postoperatively. Elastic net regression (ENR) selected key predictors, which were then included in a multivariate logistic regression model. The resulting nomogram's performance was evaluated using receiver operating characteristic curves, calibration, and decision curve analysis, and was internally validated using 1000 bootstrap resamples.

Results: A total of 118 patients achieved post-operative semen parameter improvement. From 23 candidate variables, ENR identified three independent predictors as follows: left and right CEUS hemodynamic patterns and follicle-stimulating hormone. The nomogram demonstrated excellent discrimination, with an area under the curve of 0.822 and good calibration (Hosmer-Lemeshow test, P = 0.405). Decision curve analysis confirmed the clinical utility of the model across a wide range of risk thresholds.

Conclusion: CEUS-based pampiniform plexus hemodynamic patterns are independent predictors of post-operative semen parameter improvement in patients with VC. We successfully developed and validated a novel predictive nomogram. This study provides an effective, non-invasive tool for surgical decision-making and expands the clinical application of CEUS.

Background: Lung cancer remains the leading cause of cancer-related death worldwide. Low-dose computed tomography (LDCT) is the current gold standard for screening, but radiation exposure remains a concern. Cone-Beam Computed Tomography (CBCT) offers ultra-low-dose acquisition but has not yet been tested for thoracic screening.

Purpose: To evaluate the feasibility of using a latest-generation CBCT system for thoracic imaging in a lung cancer screening context.

Materials and methods: 88 patients, current or former smokers, were retrospectively included. They underwent thoracic CBCT (7 G Dual Energy system, NewTom, Italy) between July 2024 and April 2025 at a preventive health center, with either a standard-dose protocol (n = 48) or a low-dose protocol (n = 40). Two board-certified radiologists independently assessed pulmonary findings and image quality. Radiation doses were compared. Statistical analysis included mixed-effects models and interobserver agreement.

Results: The radiation exposure was significantly lower in the low-dose group, with a median cumulative DLP of 42.8 mGy·cm, compared to 107.7 mGy·cm, representing a 2.5-fold reduction in radiation (p<0.001). Nodule detection occurred in 41% of patients overall. Overall image quality was rated good/excellent in 85% (low-dose) vs 83% (standard-dose); Risk Difference (RD) = +2% [IC95% -12; +16], Risk Ratio (RR) = 1.03. Diagnostic confidence was high in 82% vs 83%; RD = -1% [-14; +12], RR = 0.99. Spatial resolution was slightly lower in the low-dose group (OR = 0.51, p = 0.04). Interobserver agreement for nodule detection was substantial across both groups (κ = 0.73-0.83).

Conclusion: Low-dose CBCT offers substantial radiation dose reduction without compromising diagnostic confidence or interobserver reliability. CBCT may represent a promising, cost-effective alternative or complement to low-dose CT in preventive lung imaging programs, but prospective studies comparing CBCT to LDCT are warranted.

Rationale and objectives: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality by 20% and all-cause mortality by 6.7%. In 2013, the United States Preventive Services Task Force (USPSTF) recommended LCS with LDCT for adults aged 55-80 with a ≥30 pack-year smoking history who currently smoke or quit within the past 15 years. In 2021, these recommendations grew to include more at-risk populations by lowering the screening age to 50 years and reducing the smoking history threshold to 20 pack-years. We assessed the feasibility of a brief, multilingual smoking-history questionnaire in radiology waiting areas to identify LCS eligibility and standardize notification to primary care providers (PCPs) in a safety-net hospital.

Materials and methods: Quality improvement initiative, exempt from formal IRB review and the requirement for informed consent. Over an 18-month period between 2021 and 2024, we administered a voluntary smoking history questionnaire assessing demographics, lung cancer risk, LCS eligibility, and relevant medical and family history to all patients arriving for imaging appointments.

Results: From an estimated total of 54,000 surveys distributed, 6160 questionnaires were collected (11.4% response rate), and 373 patients (6.0%) self-identified as eligible for LCS based on either 2013 or 2021 USPSTF criteria. Among these patients, 202 (54.2%) were not currently undergoing LCS. Following PCP notification of their patients' LCS eligibility, only 19 of the 202 patients (9.4%) subsequently had baseline LCS exams ordered. These proportions reflect feasibility/process and are not evidence of effectiveness.

Conclusion: A brief, multilingual smoking-history questionnaire in radiology waiting areas in a safety-net setting was feasible to implement. LCS rates remain low despite patient self-identification of LCS eligibility and PCP notification. This low uptake highlights the challenges of LCS and may reflect patient, healthcare provider, and systems-level barriers faced by patients in safety-net hospitals, such as financial constraints and limited healthcare access.

Objective: Perineural invasion (PNI) and lymphovascular invasion (LVI) are critical predictors of aggressive behavior and poor prognosis in prostate cancer (PCa), yet their diagnosis relies on postoperative histopathology. This study aims to develop a noninvasive radiomic model based on biparametric magnetic resonance imaging (bpMRI) for preoperative prediction of PNI and LVI.

Methods: A total of 256 patients with pathologically confirmed PCa who underwent radical prostatectomy were retrospectively enrolled. Patients from Center 1 (n = 179) constituted the training set, while those from Center 2 (n = 77) formed the external test set. A rigorous imaging-pathology correlation protocol was applied to ensure accurate lesion matching. Inter-observer variability in segmentation was assessed (ICC > 0.75 for 85% of features), with final ROIs determined by consensus. Radiomic features were extracted from T2-weighted and diffusion-weighted imaging. Feature selection was performed using Spearman's correlation and LASSO algorithm. Multiple machine learning classifiers were constructed and interpreted with SHAP.

Results: The best-performing model for PNI prediction was Multilayer Perceptron (MLP), with an AUC of 0.805 (95% CI: 0.741-0.869) in the training set and 0.795 (95% CI: 0.698-0.896) in the test set. For LVI prediction, Logistic Regression achieved the highest performance, with an AUC of 0.859 (95% CI: 0.804-0.914) in the training set and 0.810 (95% CI: 0.714-0.906) in the test set. Calibration curves and decision curve analysis indicated good model accuracy and clinical utility.

Conclusion: Radiomic models derived from bpMRI can noninvasively and robustly predict PNI and LVI in PCa, demonstrating good generalizability across independent cohorts.

Rationale and objectives: Multiple approaches are available for defining the tibial anatomical axis when measuring the posterior tibial slope (PTS) with MRI. This study aims to evaluate the reliability of PTS measurements on MRI when the anatomical axis is defined at different tibial levels.

Materials and methods: This study included 103 patients who underwent two distinct MRI examinations of the same knee between 2018 and 2023, with each pair of scans performed within a one-year interval and without significant morphological changes. Two anatomical axes were defined: one below the tibial plateau and another below the tibial tuberosity. The medial and lateral posterior tibial slopes (MPTS and LPTS) were measured relative to each axis. Reliability was evaluated by assessing inter-scan (test-retest), intra-rater, and inter-rater agreement. Variability between scans was further examined using Bland-Altman limits of agreement (LOA).

Results: Defining the anatomical axis below the tibial plateau resulted in only moderate inter-scan agreement (MPTS: ICC = 0.651; LPTS: ICC = 0.618), whereas defining it below the tibial tuberosity yielded good agreement (MPTS: ICC = 0.864; LPTS: ICC = 0.852). The 95% LOA between scans for MPTS were -6.8° to 6.4° with the plateau-based axis and -4.4° to 4.8° with the tuberosity-based axis, while those for LPTS were -6.4° to 6.5° and -4.6° to 4.5°, respectively. Both definitions of the axis demonstrated good to excellent intra- and inter-rater reliability for MPTS and LPTS measurements.

Conclusion: Defining the anatomical axis below the tibial tuberosity yields more reliable PTS measurements, with better inter-scan agreement and good intra- and inter-rater agreement.

Rationale and objectives: Accurate visualization of periarticular arteries is crucial for vascular evaluation in knee osteoarthritis (KOA), which often entails complex vascular changes. This study aims to assess the impact of dual-energy computed tomography (DECT) combined with sublingual nitroglycerin (NTG) on the imaging quality of computed tomographic angiography (CTA) in KOA patients.

Materials and methods: A prospective observational study was conducted, from January 2024 to October 2024, involving 60 patients with KOA. Participants were divided into two groups: one receiving NTG (n = 30) and a control without NTG (n = 30). Lower-limb CTA was performed using DECT, and 40-keV virtual monochromatic images (VMI) were reconstructed. Evaluation metrics included contrast-to-noise ratio (CNR), arterial diameters, overall image quality, and the number of visible periarticular arteries.

Results: NTG administration significantly increased arterial diameters, with the popliteal artery measuring 5.06 ± 0.92 mm vs 5.98 ± 0.84 mm (P < .001) and the middle genicular artery from 0.79 ± 0.27 mm to 1.06 ± 0.30 mm (P < .001). The visualization rate of smaller arteries improved, notably the medial inferior genicular artery from 51.6% to 78.3% (P < .001). Overall image quality scores and CNR were higher in the NTG group (4.77 ± 0.54 vs 3.83 ± 0.73, P < .001; 77.87 ± 21.89 vs 50.25 ± 15.27, P = .002).

Conclusion: Combining sublingual NTG with 40-keV virtual monochromatic DECT enhances arterial visualization and imaging quality, especially for smaller vessels, indicating potential benefits for preoperative vascular evaluation in KOA management.

Rationale and objectives: To evaluate the clinical performance of a diffusion model-based motion correction algorithm for portable brain CT.

Materials and methods: We retrospectively collected 67 portable brain CT scans with corresponding fixed CT scans acquired within ±2 days as reference. A pre-trained diffusion model was applied to correct motion artifacts in the portable scans. Each case yielded three volumes as follows: original (motion group), corrected (corrected group), and fixed (reference group). Images were reviewed in randomized order by three professional readers (one neuroradiologist, one neuroradiology fellow, and one radiology resident), with at least two weeks between sessions to reduce recall bias. Eight lesion types and four image quality metrics were scored using a 5-point Likert scale. ACR phantom testing was performed to assess compliance with diagnostic image quality standards.

Results: Corrected images significantly outperformed motion images in all image quality metrics (improvement: 0.33-0.79, p<0.001), except for sharpness (p = 0.34). Diagnostic confidence improved from 2.52 to 2.86. Lesion detectability remained comparable before and after correction, with no significant differences in agreement rates (McNemar's p>0.10) or AUCs (DeLong's p>0.06) across all lesion types. Agreement rates ranged from 0.866 to 0.985 in the corrected group against the reference, and AUCs from 0.788 to 0.964. The net reclassification index was 2.66%. Corrected images passed all ACR criteria in phantom testing.

Conclusion: The diffusion model-based algorithm effectively improves image quality and diagnostic confidence without compromising lesion detection, supporting its potential for clinical use in portable brain CT.