Academic Radiology最新文献

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.

Radiological reports are essential clinical documents often written in highly technical language that is challenging for patients to comprehend. Despite advancements in digital imaging and reporting technologies, the inherent complexity of radiology reports creates significant barriers to effective patient understanding. Recently, large language models (LLMs) have emerged as a promising solution to simplify radiological reports. Therefore, this narrative review aims to provide a comprehensive overview of LLMs for simplifying patient-centered radiology reports. We examined 19 studies evaluating various LLMs including GPT-3.5, GPT-4, Claude, Gemini, and others across multiple imaging modalities. All studies reported descriptive/consistent improvements in readability metrics, with simplified reports typically achieving 5th-8th grade reading levels compared to the original 10th-14th grade levels. However, many studies identified accuracy concerns, with reports containing a range of omissions, commissions, and distortions depending on modality and model. Building upon these findings, we discuss medicolegal considerations, workflow integration challenges, and strategies for effective LLM implementation. We also explore potential impacts on radiologist workflow, including the impact of LLM biases and liability for simplified reports. Despite promising results, significant challenges remain in ensuring accurate simplification across diverse patient populations while maintaining clinical precision. In conclusion, this review underscores the transformative potential of LLMs in enhancing patient understanding of radiological findings while highlighting the need for careful implementation with appropriate oversight mechanisms.

Rationale and objectives: Innovative, evidence-based, and feasible educational interventions to teach pediatric musculoskeletal (pMSK) radiograph interpretation to radiology post-graduate trainees (R-PGT) are currently lacking.

Purpose: We evaluated the effectiveness of a pMSK radiograph education intervention in improving the identification and risk stratification of fractures and dislocations. We also determined cases most at risk of diagnostic error.

Methods: This was a multicenter prospective cross-sectional study in a convenience sample of R-PGT practicing in the United States and Canada. The web-based education intervention included 1609 pMSK extremity radiographs organized into six anatomic regions. R-PGT deliberately practiced identifying if there was a fracture/dislocation present or absent, and if present, they located and risk-stratified the fracture. Participants completed cases until they achieved a performance standard.

Results: We enrolled 100 R-PGT and derived 48,166 unique case interpretations. From the initial to final 25 case completions, there were learning gains in diagnostic sensitivity (14.9%; 95% CI 13.4, 16.4), fracture location accuracy (14.1%; 95% 12.6, 15.5), and risk stratification (23.6%; 95% CI 21.5, 25.7). Of the 100 R-PGT, 77.5% (95% CI 71.1; 83.1) achieved the performance standard in at least one anatomic region in a median of 173 cases (IQR 94, 315) or a median of 41.5 min (IQR 22.6, 76.6). There was a higher odds of correctness in older versus younger children (OR=1.3; 95% 1.2, 1.4) and those without versus with a suspicion for non-accidental injury (OR=2.0; 95% CI 1.6, 2.4). The most frequent locations among the 171 high-risk false negative cases were the elbow (n=48 [28.1%]), pelvis (n=39 [22.8%]), and ankle (n=27 [15.8%]).

Conclusion: This study demonstrates that a web-based and competency-focused intervention can improve pMSK radiograph interpretation among R-PGTs and identifies cases prone to diagnostic error. These findings align with prior work showing the value of deliberate practice in radiology education.

Background: Deep learning image reconstruction (DLIR) has gained recognition as a promising technique to improve image quality in low-dose CT imaging. However, its performance in dual-energy CT portal venography (DE-CTPV), particularly under reduced contrast medium volume and radiation dose (dual-low dose) conditions, remains underexplored.

Objective: This study aims to compare the performance of DLIR and adaptive statistical iterative reconstruction (ASIR-V) in DE-CTPV, with a focus on image quality across multiple vascular segments of the portal venous (PV) system under dual-low dose protocols.

Methods: Patients undergoing DE-CTPV were reconstructed using DLIR medium (DLIR-M) and high strength (DLIR-H) and ASIR-V (50%). Image quality was assessed both subjectively and objectively in the main portal vein (MPV), left and right portal veins (LPV, RPV), splenic vein (SV), and superior mesenteric vein (SMV). Objective metrics, including image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were calculated. Additionally, radiation dose parameters (CTDI_vol, DLP, ED) and contrast medium volume were compared with data from previous studies.

Results: In this study, the mean CTDI_vol, DLP, and ED were 9.79 ± 2.13 mGy, 326.26 ± 84.58 mGy·cm, and 4.89 ± 1.27 mSv, respectively. The mean contrast medium volume was 79.5 ± 11.4 mL. DLIR-H significantly enhanced image quality across all vascular segments, achieving substantial reductions in image noise and notable increases in CNR and SNR (P < 0.05). It also received the highest subjective ratings for overall image quality, image noise, vascular edge sharpness, and diagnostic confidence compared to ASIR-V 50%. The use of 55 keV virtual monoenergetic imaging (VMI) further enhanced iodine contrast effectiveness, while DLIR effectively reduced noise, ensuring clearer and more consistent vascular delineation across all assessed vascular segments.

Conclusion: DLIR substantially improves image quality in DE-CTPV compared with ASIR-V 50%, even when utilizing dual-low dose protocol. By providing consistent, high-quality imaging across multiple portal venous segments, DLIR may offers a safer and more reliable approach for preoperative evaluation and postoperative monitoring in liver transplantation.