BJR open最新文献

Objectives: Coronary and cardiac calcification are frequent incidental findings on non-gated thoracic computed tomography (CT). However, radiologist opinions and practices regarding the reporting of incidental calcification are poorly understood.

Methods: UK radiologists were invited to complete this online survey, organised by the British Society of Cardiovascular Imaging (BSCI). Questions included anonymous information on subspecialty, level of training and reporting practices for incidental coronary artery, aortic valve, mitral and thoracic aorta calcification.

Results: The survey was completed by 200 respondents: 10% trainees and 90% consultants. Calcification was not reported by 11% for the coronary arteries, 22% for the aortic valve, 35% for the mitral valve and 37% for the thoracic aorta. Those who did not subspecialise in cardiac imaging were less likely to report coronary artery calcification (p = 0.005), aortic valve calcification (p = 0.001) or mitral valve calcification (p = 0.008), but there was no difference in the reporting of thoracic aorta calcification. Those who did not subspecialise in cardiac imaging were also less likely to provide management recommendations for coronary artery calcification (p < 0.001) or recommend echocardiography for aortic valve calcification (p < 0.001), but there was no difference for mitral valve or thoracic aorta recommendations.

Conclusion: Incidental coronary artery, valvular and aorta calcification are frequently not reported on thoracic CT and there are differences in reporting practices based on subspeciality.

Advances in knowledge: On routine thoracic CT, 11% of radiologists do not report coronary artery calcification. Radiologist reporting practices vary depending on subspeciality but not level of training.

Objectives: To assess the frequency and intensity of [¹⁸F]-prostate-specific membrane antigen (PSMA)-1007 axillary uptake in lymph nodes ipsilateral to COVID-19 vaccination with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) in patients with prostate cancer referred for oncological [¹⁸F]-PSMA positron emission tomography (PET)/CT or PET/MR imaging.

Methods: 126 patients undergoing [¹⁸F]-PSMA PET/CT or PET/MR imaging were retrospectively included. [¹⁸F]-PSMA activity (maximum standardized uptake value) of ipsilateral axillary lymph nodes was measured and compared with the non-vaccinated contralateral side and with a non-vaccinated negative control group. [¹⁸F]-PSMA active lymph node metastases were measured to serve as quantitative reference.

Results: There was a significant difference in maximum standardized uptake value in ipsilateral and compared to contralateral axillary lymph nodes in the vaccination group (n = 63, p < 0.001) and no such difference in the non-vaccinated control group (n = 63, p = 0.379). Vaccinated patients showed mildly increased axillary lymph node [¹⁸F]-PSMA uptake as compared to non-vaccinated patients (p = 0.03). [¹⁸F]-PSMA activity of of lymph node metastases was significantly higher (p < 0.001) compared to axillary lymph nodes of vaccinated patients.

Conclusion: Our data suggest mildly increased [¹⁸F]-PSMA uptake after COVID-19 vaccination in ipsilateral axillary lymph nodes. However, given the significantly higher [¹⁸F]-PSMA uptake of prostatic lymph node metastases compared to "reactive" nodes after COVID-19 vaccination, no therapeutic and diagnostic dilemma is to be expected.

Advances in knowledge: No specific preparations or precautions (e.g. adaption of vaccination scheduling) need to be undertaken in patients undergoing [¹⁸F]-PSMA PET imaging after COVID-19 vaccination.

Objectives: To assess the impact of noise-optimised virtual monoenergetic imaging (VMI+) on image quality and diagnostic evaluation in abdominal dual-energy CT scans with impaired portal-venous contrast.

Methods: We screened 11,746 patients who underwent portal-venous abdominal dual-energy CT for cancer staging between 08/2014 and 11/2019 and identified those with poor portal-venous contrast.Standard linearly-blended image series and VMI+ image series at 40, 50, and 60 keV were reconstructed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal organs and vascular structures were calculated. Image noise, image contrast and overall image quality were rated by three radiologists using 5-point Likert scale.

Results: 452 of 11,746 (4%) exams were poorly opacified. We excluded 190 cases due to incomplete datasets or multiple exams of the same patient with a final study group of 262. Highest CNR values in all abdominal organs (liver, 6.4 ± 3.0; kidney, 17.4 ± 7.5; spleen, 8.0 ± 3.5) and vascular structures (aorta, 16.0 ± 7.3; intrahepatic vein, 11.3 ± 4.7; portal vein, 15.5 ± 6.7) were measured at 40 keV VMI+ with significantly superior values compared to all other series. In subjective analysis, highest image contrast was seen at 40 keV VMI+ (4.8 ± 0.4), whereas overall image quality peaked at 50 keV VMI+ (4.2 ± 0.5) with significantly superior results compared to all other series (p < 0.001).

Conclusions: Image reconstruction using VMI+ algorithm at 50 keV significantly improves image contrast and image quality of originally poorly opacified abdominal CT scans and reduces the number of non-diagnostic scans.

Advances in knowledge: We validated the impact of VMI+ reconstructions in poorly attenuated DECT studies of the abdomen in a big data cohort.

Objective: The aim of this study was to explore opinions and views towards radiology AI among Saudi Arabian radiologists including both consultants and trainees.

Methods: A qualitative approach was adopted, with radiologists working in radiology departments in the Western region of Saudi Arabia invited to participate in this interview-based study. Semi-structured interviews (n = 30) were conducted with consultant radiologists and trainees. A qualitative data analysis framework was used based on Miles and Huberman's philosophical underpinnings.

Results: Several factors, such as lack of training and support, were attributed to the non-use of AI-based applications in clinical practice and the absence of radiologists' involvement in AI development. Despite the expected benefits and positive impacts of AI on radiology, a reluctance to use AI-based applications might exist due to a lack of knowledge, fear of error and concerns about losing jobs and/or power. Medical students' radiology education and training appeared to be influenced by the absence of a governing body and training programmes.

Conclusion: The results of this study support the establishment of a governing body or national association to work in parallel with universities in monitoring training and integrating AI into the medical education curriculum and residency programmes.

Advances in knowledge: An extensive debate about AI-based applications and their potential effects was noted, and considerable exceptions of transformative impact may occur when AI is fully integrated into clinical practice. Therefore, future education and training programmes on how to work with AI-based applications in clinical practice may be recommended.

Objectives: Muscle volume may reflect both strength and functional capability and hence is a parameter often measured to assess the effect of various interventions. The aim of the current study was to determine the sensitivity of muscle volume calculations on participant postural position and hence gauge possible errors that may arise in longitudinal studies, especially those where an intervention leads to large muscle changes and potentially the degree of spinal curvature.

Methods: Twenty healthy participants (22-49 years, 10 male and 10 female), were recruited and MRI images acquired with them lying in four different positions; neutral spine (P1), decreased lordosis (P2), increased lordosis (P3) and neutral spine repeated (P4). Images were analysed in Simpleware ScanIP, and lumbar muscle volume and Cobb's angle, as an indicator of spine curvature, determined.

Results: After comparing volume determinations, no statistically significant differences were found for P1 - P2 and P1 - P4, whereas significant changes were determined for P2 - P3 and P1 - P3. P2 and P3 represent the two extremes of spinal curvature with a difference in Cobb's angle of 17°. However, the mean difference between volume determinations was only 29 cm³. These results suggest the differences in muscle volume determinations are generally greater with increasing differences in curvature between measurements, but that overall the effects are small.

Conclusions: Thus, generally, spinal muscle volume determinations are robust in terms of participant positioning.

Advances in knowledge: Differences in muscle volume calculations appear to become larger the greater the difference in spinal curvature between positions. Thus, spinal curvature should not have a major impact on the results of spinal muscle volume determinations following interventions in longitudinal studies.

Objective: We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants.

Methods: We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software.

Results: The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] vs 6.6% [IQR:1.2-18.3%]; p < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] vs 0.1% [IQR:0.0-0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs  4.9%; p = .032). Mortality rate was similar in all age groups.

Conclusion: Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups.

Advances in knowledge: Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.

Objective: To analyse patterns of treatment with curative intent commonly used in elderly patients with locally advanced non-small-cell lung carcinoma (NSCLC) and predictive factors of overall survival in routine clinical practice.

Methods: This multicentre prospective study included consecutive patients aged ≥65 years old diagnosed with NSCLC between February 2014 and January 2018. Inclusion criteria: age ≥65 years, stage IIIA/IIIB NSCLC. Treatment decisions were taken by a multidisciplinary committee. Kaplan-Meier curves and log-rank test were used to identify which clinical/treatment-associated variables, or pre-treatment quality of life (QOL) considering EORTC QLQ-C30 (and LC13 module) were predictive of overall survival.

Results: A total of 139 patients were recruited. Median follow-up was 9.9 months (1.18-57.36 months) with a median survival of 14 months (range 11-17 months). In the group>75-year-old patients, the committee recommended chemotherapy and sequential radiotherapy (55.6%) or radiotherapy alone (22.2%), rather than surgery (3.7%) or concomitant radiochemotherapy (16.5%). However, in 65- to 75-year-old patients, surgery and concomitant radiochemotherapy were recommended in half of cases (p=0.003). Regarding multivariate analysis, the risk of death was higher in patients with pre-existing heart disease (p=0.002), low score for physical functioning (p=0.0001), symptoms of dysphagia (p=0,01), chest pain (p=0.001), and those not undergoing surgical treatment (p=0.024).

Conclusions: Patients >75 years received more conservative treatments. Surgery improved survival and should be carefully considered, regardless of patient age. Comorbidities and poor baseline QOL are predictive of shorter survival.

Advances in knowledge: Measuring these parameters before treatment may help us to define a population of frail patients with a poorer prognosis to facilitate decision making in clinical practice.

Objective: Stereotactic ablative radiotherapy (SABR) has been suggested to be an effective non-invasive ablative therapy for oligometastases originated from colorectal cancer (CRC). This study aimed to report CRC oligometastases SABR treatment outcomes in terms of overall survival (OS), progression-free survival (PFS) and post-treatment toxicities.

Methods: Treatment records of patients with CRC metachronous oligometastases who underwent SABR at a single institution between February 2015 and December 2018 were retrospectively reviewed. OS and PFS were calculated using Kaplan-Meier statistics and post-RT toxicity data was scored following CTCAE v. 4.0. Analysis of prognostic factors on OS and PFS was performed based on site of primary cancer, types of treatment to primary cancer, number of oligometastases, SABR treatment sites, intervals between treatment to primary cancer and SABR to oligometastases, biological equivalent dose, cumulative gross tumour volume and planning target volume.

Results: 75 patients with 86 CRC metachronous oligometastases (including liver, lung, lymph nodes and bone) were included. The median age was 65.5 years (range 42.5-87.2) with a median follow-up of 23.8 months (range 3.1-46.5). The estimated median PFS was 14.6 months (95% CI 9.6-19.6). and estimated median OS was 33.3 months (95% CI 22.9-43.7). Majority of patients tolerated SABR well with the most common acute side-effects of Grade 1 fatigue. No Grade 3 or higher toxicities were reported at any time points.Only SABR treatment sites (p = 0.03) and cumulative volumes of planning target volume (p = 0.02) were found to be statistically significant independent predictors of PFS and OS respectively.

Conclusion: This study showed modest PFS, OS, and post-treatment toxicity outcomes on SABR to metachronous oligometastases from CRC. It has highlighted that cumulative tumour volume may be a stronger prognostic factor of OS comparing to the number of metastases.

Advances in knowledge: There are limited data published on the efficacy and post-treatment toxicity of CRC oligometastases SABR with adequate length of follow-up. Our retrospective study suggests that cumulative tumour volume may be a stronger prognostic factor of OS comparing to the number of oligometastases.

Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over-burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs.

Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.