British Journal of Radiology最新文献

Objectives: Management guidelines for incidental aortic dilation detected on low-dose chest computed tomography (LDCT) lung cancer screening (LCS) are lacking. Therefore, this study aims to validate artificial intelligence (AI) software for automated aortic measurements and assess aortic dilation distribution in screening participants.

Methods: Baseline LDCT scans from 2 tertiary centres (April 2017 to December 2023) were reviewed. In 100 randomly selected cases, radiologist- and AI-measured maximum aortic diameters (MADs) were compared at the ascending thoracic aorta (ATA), aortic arch (AACH), descending thoracic aorta (DTA), and abdominal aorta (AA). AI then analysed all scans, and coronary artery calcification (CAC) was assessed using the Agatston method to evaluate correlations with MADs.

Results: Overall, 1204 patients (99.2% men; mean age ± SD: 62.7 ± 5.4 years) were included. Intraclass correlation coefficients between radiologists and AI were 0.950, 0.758, 0.933, and 0.931 for ATA, AACH, DTA, and AA, respectively. Mean maximum diameters were: ATA, 38.7 ± 3.7 mm (33.4% ≥ 40 mm, 18.5% ≥ 42 mm, and 5.6% ≥ 45 mm); AACH, 37.3 ± 3.3 mm; DTA, 29.4 ± 2.9 mm; and AA, 26.3 ± 2.3 mm. MADs significantly correlated with CAC severity (P ≤ .001).

Conclusions: AI software reliably measures MADs. Aortic dilation distribution may serve as a reference in LDCT LCS, and its association with CAC highlights the clinical importance of incorporating MADs into patient management.

Advances in knowledge: Validated AI software enables reliable MAD assessment; reported aortic dilation prevalence offers valuable reference data for LDCT LCS.

Objectives: To evaluate the clinical utility of Node-Reporting and Data System (Node-RADS) 1.0 in assessing axillary lymph node (ALN) on breast MRI.

Methods: This retrospective study included women with clinical T1-T2 stage breast cancer who underwent surgery between April 2014 and March 2023. MRI-ALN status was first evaluated using routine clinical diagnostic criteria. Node-RADS scores were subsequently assigned based on preoperative MRI after a 2-week interval. The optimal cut-off was determined using the Youden index. Diagnostic performance was compared for assessing ALN metastasis (ALNM) and pathological ALN (pALN) burden. In the secondary analysis, differences in imaging features were further evaluated in patients with Node-RADS < 3.

Results: The optimal cut-off for diagnosing ALNM was Node-RADS >1. Compared with MRI-ALN status, Node-RADS score demonstrated lower sensitivity (56% vs. 71%, P < .01), but comparable specificity (92% vs. 85%, P = .16). For assessing pALN burden, Node-RADS > 2 was the optimal cut-off with lower sensitivity (55% vs. 77%, P < .01) but higher specificity (82% vs. 62%, P < .01). In the secondary analysis, patients with ALNM showed significant differences in cortical morphology and the suspicious breast-side lymph node sign.

Conclusions: Node-RADS 1.0 showed lower sensitivity than conventional MRI diagnosis in assessing ALNM but demonstrated good specificity in evaluating ALNM and pALN burden.

Advances in knowledge: Node-RADS 1.0 showed lower sensitivity than conventional MRI diagnosis in assessing ALNM. The potential values of cortical morphology and the suspicious breast-side lymph node sign in improving the sensitivity of Node-RADS 1.0 warrant further investigation.

Skeletal dysplasias constitute a heterogeneous assemblage of over 770 genetically determined disorders. The 2023 revision of the classification of Genetic Skeletal Disorders represents a seminal advancement, integrating radiologic phenotypes with molecular and biochemical information to accommodate the expanding complexity of skeletal pathology. Radiologists remain indispensable in elucidating these conditions, as imaging phenotypes continue to underpin the delineation of these disorders. This review discusses the intricate radiologic manifestations of skeletal dysplasias within the framework of the current classification, advocating for the adoption of a structured radiological reporting.

Objectives: To assess whether 3D photoacoustic tomography (PAT) using the Fabry-Perot (FP) scanner can (1) reliably detect synovitis in participants with rheumatoid arthritis (RA), and (2) assess the severity of active inflammation.

Methods: A total of 247 3D-PAT images of the finger and wrist joint from 11 healthy volunteers and 9 patients with RA were obtained using the FP scanner. Patients underwent power doppler ultrasound (PDUS) assessment of RA disease activity. The 3D-PAT images were acquired over a 15 × 15 × 10 mm3 volume in <15 s. The images were assessed quantitatively by segmentation of the vasculature using k-means clustering followed by skeletonization to measure vascular path length (PAT-VPL). Patient and volunteer PAT-VPL were compared using t-tests and area under the receiver operating characteristic curve (ROC-AUC).

Results: The 3D-PAT differentiates between unaffected and affected joints in patients with RA, showing a statistically significant difference in PAT-VPL (unaffected joint: 11.88 mm, affected joints: 45.7 mm, P < .001). The ROC-AUC was 0.91 (95% confidence interval [CI]: 0.81-0.96), with sensitivity and specificity of 86% (95% CI: 72%-95%) and 78% (95% CI: 60%-90%), respectively. PAT-VPL was associated with PDUS-based clinical severity grade, with a correlation coefficient of 0.74 (95% CI: 0.67-0.8). Significant differences in PAT-VPL were observed between each clinical severity group.

Conclusions: The FP scanner generates rapid high-resolution quantitative 3D-PAT images of the synovial microvasculature that can be used to distinguish between inflamed and non-inflamed joints, and assess the severity of active inflammation.

Advances in knowledge: The 3D-PAT FP scanner reliably distinguishes between affected and unaffected finger and wrist joints in patients with rheumatoid arthritis. Compared to previous 2D PAT scanners used to image joint inflammation, it offers superior image quality and the prospect of mitigating operator-dependent variations in probe positioning by virtue of its 3D imaging capability.

Objectives: To test the feasibility and quantify the performance of low-dose CT urography (CTU) with artificial intelligence iterative reconstruction (AIIR) for bladder cancer (BC) evaluation.

Methods: A total of 122 patients undergoing CTU examination were prospectively enrolled, where the routine-dose scan (120 kVp, ref 100 mAs) at corticomedullary phase (CMP) was followed immediately by a low-dose scan (120 kVp, ref 20 mAs). Routine-dose images were reconstructed with hybrid iterative reconstruction (HIR, RD-HIR), while low-dose images were with AIIR (LD-AIIR) and HIR (LD-HIR). The image quality was first evaluated regarding streak artefacts around the bladder and then in contrast-to-noise ratio (CNR) for various manifestations of bladder wall. The diagnostic performance of BC was characterized using receiver operating characteristic (ROC) analysis, in respect to the clinical diagnostic report.

Results: The effective dose at low-dose CMP was 80.2% lower than routine-dose scan (7.6 ± 1.2 vs 1.5 ± 0.3 mSv). Nineteen cases in LD-HIR were deemed clinically unacceptable for presenting severe artefacts around the bladder, while found well above the basic requirement in LD-AIIR. The highest CNR was found in LD-AIIR in all scenarios (all P < .001). The area under ROC curve in LD-AIIR was comparable to RD-HIR (0.988 vs 0.990, P = .172) and significantly higher than LD-HIR (0.988 vs 0.831, P < .001).

Conclusions: The low-dose AIIR protocol allows for a profound dose reduction (80.2%) while maintaining reliable diagnosis of BC on corticomedullary phase CTU images.

Advances in knowledge: Corticomedullary phase CTU with AIIR permits 80.2% dose reduction while preserving reliable BC diagnosis.

Objectives: This study aimed to develop and construct a predictive model based on the quantitative parameters of full-volume dual-energy computed tomography (DECT) to forecast the International Association for the Study of Lung Cancer classification of non-mucinous invasive pulmonary adenocarcinoma (IPA).

Methods: The preoperative clinical and imaging data of 161 patients with pure solid type non-mucinous IPA from September 2021 to May 2024 were retrospectively analysed. The semiautomated software was used to perform full-volume segmentation of the lesions and the associated DECT quantitative parameters were recorded. Through univariate and multivariate logistic regression analyses, we identified independent characteristic variables that distinguished high-grade from low-grade non-mucinous IPA. We subsequently used these characteristic variables to construct a multiparameter model.

Results: Volume, slope of the spectral curve (λ40keV-100keV) and normalized iodine concentration (NIC) were identified as independent feature variables to distinguish low-grade and high-grade non-mucinous IPA. By utilizing these 3 variables, we constructed a quantitative visualization nomogram to distinguish the new IASLC grade of non-mucinous IPA. The model exhibited excellent performance in both the training and testing groups, with area under the curve (AUC) values of 0.884 (95% CI: 0.826-0.943) and 0.848 (95% CI: 0.738-0.958), respectively.

Conclusion: This study successfully established and validated a nomogram based on DECT quantitative parameters, which can effectively differentiate high-grade and low-grade non-mucinous IPA and provides potential value for clinical decision-making.

Advances in knowledge: This study is the first attempt to apply a nomogram based on DECT to assess the invasiveness of non-mucous IPA.

Ultrasonography, especially Transvaginal sonography (TVS) is an effective, non-invasive and reliable investigation for the diagnosis of adenomyosis. The Morphological Uterus Sonographic Assessment group consensus terminology provides a standardized lexicon for the description of myometrial lesions and has been recently revised to include direct and indirect features of adenomyosis on sonography. In this article, we aim to provide a simplified framework for the practical application of the MUSA group consensus terminology in the ultrasonographic evaluation of adenomyosis, aiding in accurate diagnosis and informed decision-making.

Objectives: To determine the diagnostic accuracy of ultrasonography (USG) for the evaluation of proximal hamstring injury.

Methods: A cross-sectional observational study was carried out in the Department of Radiodiagnosis and Interventional Radiology in a tertiary care centre, over a period of 18 months. Patients (20-50 years age) with clinical suspicion of proximal hamstring injury were included, after subjecting them to the selection criteria. Ultrasonography was performed using the 9-12 and 12-18 MHZ linear transducers on Siemens S-3000 machine, while MR imaging was done on 3 Tesla GE Discovery machine.

Results: Fifty patients (M:F = 33:17) were included in the study. Ultrasonography detected proximal hamstring injuries in 34 (68%) participants, consistent with magnetic resonance imaging (MRI) findings. Tendinopathy emerged as the most prevalent diagnosis (42%), followed by isolated tears (14%) and combined tendinopathy and tears (12%). USG demonstrated a sensitivity of 91.2% and a specificity of 81.2%, with an overall diagnostic accuracy of 88%. The positive predictive value and negative predictive value were 91.2% and 81.2%, respectively. A Cohen's Kappa coefficient of 0.724 indicated substantial agreement between USG and MRI findings.

Conclusion: Ultrasonography is a reliable and cost-effective diagnostic tool for proximal hamstring injuries, demonstrating high concordance with MRI. Its integration into clinical practice has the potential to improve accessibility and expedite diagnosis while reducing healthcare costs.

Advances in knowledge: In our study, high diagnostic accuracy was obtained through a combination of clinical and USG examination, therefore, USG can be used as a first line investigative modality for suspected Hamstring injury.

Objectives: This work aimed to introduce Advanced intelligence Clear-IQ Engine (Canon, Japan) (AiCE) reconstruction to the clinical workflow by assessing the image quality and impact on radiotherapy treatment planning. This introduction would then allow for optimization of CT scanning doses.

Methods: A CT scan of an electron density phantom was performed using clinical protocols using AIDR3D and AiCE reconstructions at different mAs values. Clinical scans were reconstructed using AiCE and the dose distributions of the clinical plans recalculated. An optimization project was initiated for breast radiotherapy patients after the phantom testing proved the image quality is stable with reducing dose.

Results: In the electron density phantom the difference between the Hounsfield Number of water for AIDR3D and AiCE images was 5-6 HU depending on the mAs. This was within IPEM 91 tolerances for HU variation. When comparing the clinical goals of plans calculated the dose difference was a maximum of 0.45 Gy. The median dose difference was a 0.02 Gy difference. The clinical doses were successfully reduced to the limit of what was tested in phantom measurements with the image quality being clinically acceptable and a 12% reduction in mean DLP.

Conclusion: AiCE has been evaluated to demonstrate HU stability that is suitable for use in Radiotherapy treatment planning. The breast CT protocol was optimized with the introduction of AiCE and the doses were reduced without affecting image quality.

Advances in knowledge: AiCE is a novel reconstruction technique and has been used for clinical treatment planning and for optimizing CT doses.