British Journal of Radiology最新文献

Objectives: To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD).

Methods: A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models.

Results: All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features.

Conclusions: Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD.

Advances in knowledge: Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

Objectives: Stereotactic radiotherapy (SRT) for brain metastases (BM) allows very good local control (LC). However, approximately 20%-30% of these lesions will recur. The objective of this retrospective study was to evaluate the impact of dosimetric parameters on LC in cerebral SRT.

Methods: Patients treated with SRT for 1-3 BM between January 2015 and December 2018 were retrospectively included. A total of 349 patients with 538 lesions were included. The median gross tumour volume (GTV) was 2 cm3 (IQR, 0-7). The median biological effective dose with α/β = 10 (BED10) was 60 Gy (IQR, 32-82). The median prescription isodose was 71% (IQR, 70-80). Correlations with LC were examined using the Cox regression model.

Results: The median follow-up period was 55 months (min-max, 7-85). Median overall survival was 17.8 months (IQR, 15.2-21.9). There were 95 recurrences and LC at 1 and 2 years was 87.1% (95% CI, 84-90) and 78.1% (95% CI, 73.9-82.4), respectively. Univariate analysis showed that systemic treatment, dose to 2% and 50% of the planning target volume (PTV), BED10 > 50 Gy, and low PTV and GTV volume were significantly correlated with better LC. In the multivariate analysis, GTV volume, isodose, and BED10 were significantly associated with LC.

Conclusion: These results show the importance of a BED10 > 50 Gy associated with a prescription isodose <80% to optimize LC during SRT for BM.

Advances in knowledge: Isodose, BED, and GTV volume were significantly associated with LC. A low isodose improves LC without increasing the risk of radionecrosis.

Objective: Although radiofrequency ablation (RFA) has been considered as the favourable treatment option for hepatocellular carcinoma (HCC), there still exist some challenges for new recurrence after RFA. The present study aims to determine the factors affecting recurrence and develop an effective model to predict intrahepatic recurrence-free survival (RFS).

Methods: Patients with HCC followed by RFA between 2000 and 2021 were included in this study. Multivariable Cox regression analysis was used to determine the independent prognostic factors and establish the nomogram predicting intrahepatic RFS after RFA. The predictive performance of the nomogram was assessed according to the C-index, calibration plots, and Kaplan-Meier curves stratified by the tertiles.

Results: A total of 801 sessions in 660 patients (including 1155 lesions) were enrolled into this study. Intrahepatic new recurrence was observed in all patients during the follow-up, and the mean intrahepatic RFS was 21.9 months in the present cohort. According to multivariate COX regression analysis, five independent prognostic factors affecting intrahepatic RFS were determined, including age, Child-Pugh class, tumour distribution, number of tumours, and a-fetoprotein (AFP). Based on all independent prognostic factors, the nomogram model was developed and evaluated, which achieved favourable discrimination and calibration.

Conclusion: This study established five independent prognostic factors and constructed a nomogram model to predict intrahepatic RFS for HCC patients followed by RFA. It could better help clinicians select RFA candidates, as well as offering the important information about whether patients need receive comprehensive treatment to prevent new recurrence after RFA.

Advances in knowledge: (1) In this study, 5 preoperative clinic-pathological variables were determined as the independent prognostic factors affecting RFS after RFA in the current largest sample size. (2) Based on these independent prognostic factors, a prognostic nomogram predicting RFS after RFA was established, which may be used to select patients who benefit from RFA and could help both surgeons and patients provide useful information for choosing the personalized treatment.

Objective: To assess the potential values of radiomics signatures of pericoronary adipose tissue (PCAT) in identifying patients with acute coronary syndrome (ACS).

Methods: In total, 149, 227, and 244 patients were clinically diagnosed with ACS, chronic coronary syndrome (CCS), and without coronary artery disease (CAD), respectively, and were retrospectively analysed and randomly divided into training and testing cohorts at a 2:1 ratio. From the PCATs of the proximal left anterior descending branch, left circumflex branch, and right coronary artery (RCA), the pericoronary fat attenuation index (FAI) value and radiomics signatures were calculated, among which features closely related to ACS were screened out. The ACS differentiation models AC1, AC2, AC3, AN1, AN2, and AN3 were constructed based on the FAI value of RCA and the final screened out first-order and texture features, respectively.

Results: The FAI values were all higher in patients with ACS than in those with CCS and no CAD (all P < .05). For the identification of ACS and CCS, the area-under-the-curve (AUC) values of AC1, AC2, and AC3 were 0.92, 0.94, and 0.91 and 0.91, 0.86, and 0.88 in the training and testing cohorts, respectively. For the identification of ACS and no CAD, the AUC values of AN1, AN2, and AN3 were 0.95, 0.94, and 0.94 and 0.93, 0.87, and 0.89 in the training and testing cohorts, respectively.

Conclusions: Identification models constructed based on the radiomics signatures of PCAT are expected to be an effective tool for identifying patients with ACS.

Advances in knowledge: The radiomics signatures of PCAT and FAI values are expected to differentiate between patients with ACS, CCS and those without CAD on imaging.

Objectives: To evaluate the clinical value of using a split-bolus contrast injection protocol in improving image quality consistency and diagnostic accuracy in lower extremity CT angiography (CTA).

Methods: Fifty (mean age, 66 ± 12 years) and 39 (mean age, 66 ± 11 years) patients underwent CTA in the lower extremity arteries using split-bolus and fixed-bolus injection schemes, respectively. The objective and subjective image quality of the 2 groups were compared and the diagnostic efficacy for the degree of vessel stenosis was compared using digital subtraction angiography as the gold standard. A P < .05 was considered statistically significant.

Results: In comparison with the fixed-bolus scheme, the split-bolus scheme greatly improved the consistency of image quality of the low extremities by significantly increasing the arterial enhancement (337.87 ± 64.67HU vs. 254.74 ± 71.58HU, P < .001), signal-to-noise ratio (22.58 ± 11.64 vs. 7.14 ± 1.98, P < .001), and contrast-to-noise ratio (37.21 ± 10.46 vs. 31.10 ± 15.40, P = .041) in the infrapopliteal segment. The subjective image quality was better (P < .001) and the diagnostic accuracy was higher in the split-bolus group than in the fixed-bolus group (96.00% vs. 91.67%, P < .05, for diagnosing >50% stenosis, and 97.00% vs. 89.10%, P < .05, for diagnosing occlusion) for the infrapopliteal segment arteries.

Conclusions: Compared with the fixed-bolus injection scheme, the split-bolus injection scheme improves the image quality consistency and diagnostic accuracy especially for the infrapopliteal segment arteries in lower extremity CTA.

Advances in knowledge: (1) The split-bolus injection scheme of CTA of the lower extremity arteries improves the overall image quality, uniformity of contrast enhancement. (2) Compared with the fixed-bolus injection scheme, the split-bolus injection scheme especially improves the infrapopliteal segment arteries image quality and diagnostic efficacy.

Objectives: Metallic implants cause artefacts and distortion on MRI. To ensure accurate dose delivery and plan adaptation on an MR Linac, there is a need to evaluate distortion caused.

Methods: Participants were imaged on an MR Linac (Elekta Unity, Elekta AB Stockholm). Three sequences were evaluated. Two vendor supplied (T2W TSE 3D), and one T2W TSE 3D optimized to reduce metal artefact distortions. Images were rigidly registered to CT images by a single observer, using bony anatomy. Three coronal and three axial images were selected, and six paired, adjacent, bony landmarks were identified on each slice. Images bisecting treatment isocentre were included. Difference between landmark coordinates was taken to be measure of distortion.

Results: Five observers participated. Thirty six pairs of bony landmarks were identified. Median difference in position of landmarks was ≤3 mm (range 0.3-4.4 mm). One-way analysis of variance (ANOVA) between observer means showed no significant variation between sequences or patients (P = 1.26 in plane, P = 0.11 through plane). Interobserver intra class correlation (ICC) was 0.70 in-plane and 0.78 through-plane. Intra-observer ICC for three observers was 0.76, 0.81, 0.83, showing moderate to good reliability on this small cohort.

Conclusions: This in-vivo feasibility study suggests distortion due to metallic hip prosthesis is not an obstacle for pelvic radiotherapy on an MR Linac. Research on the impact on plan quality is warranted.

Advances in knowledge: This work supports feasibility of treating patients with metallic hip prosthesis on an MR Linac.

Objective: Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls.

Methods: In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived.

Results: Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686.

Conclusion: Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging.

Advances in knowledge: CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Dual-energy CT (DECT) is an exciting application in CT technology conferring many advantages over conventional single-energy CT at no additional with comparable radiation dose to the patient. Various emerging and increasingly established clinical DECT applications in musculoskeletal (MSK) imaging such as bone marrow oedema detection, metal artefact reduction, monosodium urate analysis, and collagen analysis for ligamentous, meniscal, and disc injuries are made possible through its advanced DECT post-processing capabilities. These provide superior information on tissue composition, artefact reduction and image optimization. Newer DECT applications to evaluate fat fraction for sarcopenia, Rho/Z application for soft tissue calcification differentiation, 3D rendering, and AI integration are being assessed for future use. In this article, we will discuss the established and developing applications of DECT in the setting of MSK radiology as well as the basic principles of DECT which facilitate them.

Objective: To demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise.

Methods: From December 2020 to March 2021, 35 patients examined cervical spine MRI were included in this study. Four sets of images including fast spin echo (FSE), original PROPELLER, PROPELLER DLR50%, and DLR75% were quantitatively and qualitatively reviewed. We calculated the signal-to-noise ratio (SNR) of the spinal cord and sternocleidomastoid (SCM) muscle and the contrast-to-noise ratio (CNR) of the spinal cord by applying region-of-interest at the spinal cord, SCM muscle, and background air. We evaluated image noise with regard to the spinal cord, SCM, and back muscles at each level from C2-3 to C6-7 in the 4 sets.

Results: At all disc levels, the mean SNR values for the spinal cord and SCM muscles were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE and original PROPELLER images (P < .0083). The mean CNR values of the spinal cord were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE at the C3-4 and 4-5 levels and PROPELLER DLR75% compared to FSE at the C6-7 level (P < .0083). Qualitative analysis of image noise on the spinal cord, SCM, and back muscles showed that PROPELLER DLR50% and PROPELLER DLR75% images showed a significant denoising effect compared to the FSE and original PROPELLER images.

Conclusion: The combination of PROPELLER and DLR improved image quality with a high SNR and CNR and reduced noise.

Advances in knowledge: Motion-insensitive imaging technique (PROPELLER) increased the image quality compared to conventional FSE images. PROPELLER technique with a DLR reduced image noise and improved image quality.

Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.