British Journal of Radiology最新文献

Endocrine hypertension is an uncommon but treatable cause of secondary hypertension. It results from excessive hormone production by the endocrine glands or due to ectopic hormone production. The causes of abnormal hormonal production can be congenital or acquired. Specific syndromes can also predispose to the development of endocrine hypertension. Extensive catecholamine production can occur due to pheochromocytomas and paragangliomas. Excessive aldosterone secretion by the adrenal cortex commonly occurs due to idiopathic (bilateral) adrenal hyperplasia or aldosterone-producing adrenal adenomas. Excessive cortisol production can occur secondary to abnormalities in the adrenal gland, the pituitary gland, or ectopic hormone production, or it can be caused by exogenous steroid intake. Other endocrine conditions that can lead to hypertension include acromegaly, primary hyperparathyroidism, hyperthyroidism, and hypothyroidism. Imaging plays a vital role in diagnosing the cause of endocrine hypertension, leading to appropriate management. The clinical presentation and laboratory investigations serve as a guide to the appropriate imaging investigation that needs to be performed to confirm a diagnosis.

Objectives: To develop and test a convolutional neural network model for automated segmentation of complicated cystic renal masses (cCRMs) on MRI.

Methods: This multicenter retrospective study analyzed 210 cCRMs between October 2019 and May 2021, divided into training/internal validation (n = 150, Institution 1) and test sets (n = 60, Institutions 2-4). Comparative 3D V-Net and U-Net models were developed across seven MRI sequences (T2-weighted, diffusion-weighted, apparent diffusion coefficient maps, unenhanced T1-weighted, and enhanced corticomedullary, nephrographic, and excretory phases images). A total of 14 models were developed, and seven pairwise comparisons were performed between the 3D V-Net and U-Net models. Segmentation performance was evaluated using Dice similarity coefficient (DSC) and Hausdorff distance (HD), with subgroup analysis of small cCRMs (≤40mm).

Results: In the test set, the excretory-phase V-Net (EPV-Net model) showed the highest DSC, and perform better than the corresponding U-Net (EPU-Net model) across all cCRMs (DSC: 0.74 ± 0.05 vs 0.70 ± 0.06, P < 0.001; HD: 27.41 ± 7.44 mm vs 39.18 ± 11.07 mm, P < 0.001) and the 35 small cCRMs subgroup (DSC: 0.74 ± 0.05 vs 0.70 ± 0.06, P < 0.001; HD: 27.48 mm ± 6.32 vs 38.72 ± 10.69 mm, P < 0.001).

Conclusions: The 3D EPV-Net model demonstrated good segmentation accuracy, even for small lesions, supporting its clinical utility for cCRMs evaluation.

Advances in knowledge: This automated approach may streamline workflow compared to manual segmentation in cCRMs assessment.

Objectives: We assessed if there was disparity between qualified statisticians and other researchers regarding the level of statistical assistance deemed necessary to support radiological research.

Methods: We categorised 50 consecutive, eligible original research articles published in an indexed imaging journal (European Radiology) 2024, according to authors' statements regarding statistical support, declared in the "statistics and biometry" section. Two reviewers extracted data related to study design, statistical methods, and analysis. Two medical statisticians categorised each study as presenting "complex" statistical methods or not and then compared this with authors' own assessment of statistical complexity, stated in the published article. We performed descriptive analyses.

Results: Most studies were observational (49, 98%) and retrospective (38, 76%). 35 (70%) studies were diagnostic, 7 (14%) prognostic, and 6 (12%) mixed. Malignancy was the most frequent topic (29 studies, 58%), and MRI the most frequent modality (35 studies, 70%). We deemed most studies (33, 66%) presented complex statistical methods. Of these, 13 studies (26% overall) declared that "no complex statistical methods were necessary for this paper". However, 10 of these employed hypothesis testing, frequently using multiple methods; 9 employed agreement and/or reliability analyses; all presented accuracy measures; 11 (85%) presented a regression model.

Conclusion: We found that approximately one quarter of original research articles published in our sample stated that "no complex statistical methods were necessary", but then presented complex analyses.

Advances in knowledge: Some radiological researchers may underestimate the complexities of statistical analysis and requirement for specialist statistical support, which risks inappropriate analyses and misleading results.

Objectives: Fistula T2 signal intensity and volume are predictive of treatment response in IBD. We aimed to evaluate pTRACK-generated MRI metrics against MAGNIFI-CD, assessing predictive value for clinical outcomes.

Methods: MRI and clinical assessment cases of 46 patients with pCD were included. T2 signal intensity was normalized using ROI in adjacent pelvic muscles. Clinical outcomes were scored using physician global assessment. Correlations, unpaired 2-tailed t-tests, and receiver operating characteristics curve analyses performance metrics were used to compare MRI metrics and predict PGA categories.

Results: Normalized average T2 signal intensity demonstrated AUC 0.784 (95% CI: 0.648-0.893). Optimal threshold for classification was 1.527, with accuracy of 80.0%, with high sensitivity (0.857) and moderate specificity (0.632). MAGNIFI-CD scores (ICC = 0.87, 95% CI: 0.45-0.98, P = .0014), fistula volume (ICC = 0.81, 95% CI: 0.21-0.97, P = .0103) showed good-excellent reliability. Significant differences were observed when dichotomized into remission and active groups: T2 signal intensity (1.55 ms ± 0.88 vs. 2.87 ms ± 1.42, P < .001), fistula volume (2.0 ± 3.3 mL vs. 4.3 ± 8.7 mL, P = .04) and MAGNIFI-CD (5.86 ± 4.70 vs. 12.41 ± 5.80, P < .001).

Conclusions: pTRACK's normalized T2 signal demonstrated comparable accuracy to MAGNIFI-CD, with higher sensitivity for predicting pCD activity. These findings underscore pTRACK's potential utility, warranting prospective validation in cohorts with more severe disease.

Advances in knowledge: pTRACK enables radiologists to measure fistula volume, normalized average T2 signal intensity, and generate 3D digital models, and has shown comparable accuracy to MAGNIFI-CD.

Objectives: To systematically review existing MRI-safe lower limb traction methodology and describe a simple, accessible technique that provides an economically viable alternative to specialized equipment.

Methods: Embase, Emcare, HMIC, Medline, and Ovid Journal databases were queried without time or language limitations (registered protocol INPLASY2025100045). Studies with lower limb traction-enhanced MRI were included, studies without traction methodology were excluded. The novel MRI-safe-traction setup consisted of a pillow "traction-hill" supporting the leg, a rolled-up blanket fulcrum and saline weights. In-line traction forces were experimentally measured at different weights and "traction-hill" angles.

Results: Included studies (n = 31; 22 hip, 6 ankle, 4 knee, 1 foot; 2321 joints total) demonstrated 4 main types of MRI-safe-traction methods. Custom components were common (n = 24), bias and traction method reporting completeness varied. Excluded studies (n = 31) mainly lacked traction-enhanced MRIs (n = 16) or traction method description (n = 11). The described novel technique provided adequate pain relief and satisfactory MRI image quality for a paediatric midshaft femoral fracture patient. Mechanical in-line traction force validation was at least equal to or greater than the suspended weight.

Conclusions: A wide variety of approaches exist for MRI-safe lower limb traction, many relying on custom or special kit. We have described a successful compact MRI-safe lower limb traction setup utilizing only commonly available items, enhancing accessibility.

Objectives: To develop and validate a deep learning-based algorithm for quantifying bronchiolitis obliterans (BO) on paediatric chest CT.

Methods: This retrospective study included 86 children (39 males; median age, 10 years) diagnosed with BO who underwent both inspiratory and expiratory CT between January 2018 and November 2021. The deep learning-based BO quantification model was trained on 26 CT scans using a 3D nnU-Net, with radiologist-segmented low attenuation regions (LARs) serving as ground truth. Model performance was evaluated through internal test with 4 CT scans and external test with 6 CT scans. Intra-vendor robustness was assessed using 22 CT scans with varying reconstruction methods, kernel types, and slice thicknesses. Comparison with semiquantitative radiologist grading was performed using 28 CT scans. Dice similarity coefficient (DSC), sensitivity, and precision were used to evaluate model performance.

Results: The model achieved a DSC of 85.41 ± 3.28%, sensitivity of 85.14 ± 7.66%, and precision of 86.21 ± 3.92% in the internal test, and 82.53 ± 4.34%, 82.17 ± 6.15%, and 84.15 ± 3.16% in the external test, respectively. For intra-vendor robustness, no significant differences in BO quantification were observed across different reconstruction methods, kernel types, and slice thicknesses (all P > .05). Compared to radiologists' grading, the model demonstrated strong to very strong correlations across all lung lobes (all P < .001).

Conclusion: The model demonstrated accurate quantification of BO on paediatric CT, with good agreement with the radiologist-segmented ground truth.

Advances in knowledge: This study presents a 3D nnU-Net-based deep learning algorithm for robust quantification of BO on paediatric CT, providing reproducible measurements of LARs.

The evidence base supporting the use of thoracic ultrasound to assess the lung parenchyma has expanded and consolidated itself significantly within the last decade. Thoracic ultrasound for lung parenchyma assessment is now finding its way into statements and clinical practice guidelines for several conditions in various settings. Since assessment of patients with possible chest disease is a very common clinical scenario, knowledge of the various types of chest imaging is essential for any physician. The most common indication for thoracic ultrasound for lung parenchymal assessment is for screening and diagnostic purposes. Several new studies have, however, demonstrated a possible large potential for using thoracic lung ultrasound to monitor lung diseases. The recent COVID-19 pandemic has increased the scope of lung parenchymal ultrasound, from diagnosis to monitoring of the disease. Deep learning of contrast-enhanced thoracic ultrasound to aid diagnosis is a new developing area. Despite increasing use of thoracic ultrasound in respiratory medicine, a consensus on assessment of competencies, and education is lacking. The aim of this review is to provide the reader with a focus overview of the current use and diagnostic limitation of thoracic ultrasound for assessment of the lung parenchyma, and future development.

Congenital bronchopulmonary malinosculations encompass a spectrum of disorders characterized by anomalous connections among the 3 components in the lung-airways, parenchyma, and vessels. This review describes these anomalies using a simplified systematic approach to facilitate understanding and diagnosis. The classification framework, based on the presence or absence of arterial, venous, and bronchial anomalies, organizes malinosculations into 7 groups. Cross-sectional imaging techniques such as CT angiography plays a critical role in delineating anatomical abnormalities, enabling precise identification of malformed components. This systematic approach aids in identifying and managing these complex congenital anomalies, improving diagnostic accuracy and treatment planning.

Objectives: We investigated the relationship between enhancement and perfusion of abdominal tissues with high arterial vascularization, testing the hypothesis that in pancreas, kidney cortex, and HCC the iodine accumulated be proportional to the fraction of cardiac output (CO).

Methods: Computed tomography perfusion scans (every 1.5 s, 40 frames) of 11 patients with HCC (aged 69 ± 9 years, 8 males) were retrospectively analysed. Regions of interest (ROIs) were drawn on aorta, HCC, pancreas, and kidney cortex. Perfusion was computed: (1) using the enhancement-to-time maximum slope (MS) method; and (2) based on the amount of iodine at the end of the bolus, when most of contrast is in the extravascular compartment. Consequently, the amount of iodine divided by total iodine injected represents the CO fraction perfusing the tissue encompassed by the ROI; this value multiplied by CO gives the "iodine-derived perfusion" as blood volume per unit of volume per unit of time.

Results: Values of iodine-derived perfusion was related with that computed from the MS method: iodine-derived perfusion (mL/s/mL) = 0.57 + 0.8 × MS perfusion (r2 = 0.82, P ≤ .001, Bland-Altman bias -0.01). Iodine-derived perfusion was 1.5 ± 0.3, 1.4 ± 0.5, and 2.7 ± 0.3 mL/s/mL while MS-derived perfusion was 1.1 ± 0.56, 1.3 ± 0.65, and 3.1 ± 0.91 mL/s/mL for HCC, pancreas, and kidney cortex, respectively. The results from the 2 methods were not different (P = .92). Kidney cortex perfusion was greater than those of HCC and pancreas (P ≤ .001).

Conclusions: Tissue enhancement in late arterial phase is well related to organ perfusion computed with the MS method.

Advances in knowledge: CE in late arterial phase is a proxy for organ perfusion and can be expressed quantitatively if the amount of iodine injected is considered or cardiac output is estimated.