BJR open最新文献

[This corrects the article DOI: 10.1093/bjro/tzae034.].

Objectives: To evaluate the outcome of patients with cranial (C) and extra-cranial (EC) oligometastases treated with stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) and standard of care systemic therapy.

Methods: During the period 2018-2022, patients who received SBRT or SRS for oligometastases (≤5 lesions) in addition to systemic therapy were evaluated. PET-CT was done to categorize them as C or EC oligometastases. Local control, distant progression, progression-free survival (PFS), overall survival (OS), and toxicity of the treatment were recorded.

Results: 43 patients received SBRT/SRS to 88 oligometastatic lesions. Eighteen patients had C metastases, 23 had EC metastases and 2 patients had both. 40/43 patients had received systemic therapy. At a median follow-up of 13 months, median PFS was 14 months and 1 and 2 years OS was 83.2% and 67.4%. Local control with SRS was 92.8% and with SBRT was 86.3%. Distant failure in C vs EC oligometastases was seen in 12/14 vs 7/20 patients (P = 0.03). Median PFS was 30 months for EC and 6 months for C oligometastases (P = 0.003). 1 and 2 years OS was 89.6% and 82.7% for EC and 77.6% and 48.5% for C oligometastases (P = 0.21). One patient had grade 3 and 3 patients had grade 1 toxicity.

Conclusions: SRS and SBRT yielded high rates of local control with low toxicity. Compared to EC, patients with C oligometastases had higher distant relapses, poorer PFS, and a trend towards worse survival. More studies with separate enrolment of patients with C and EC oligometastases are needed.

Advances in knowledge: Outcome of patients with C oligometastases is poorer than EC metastases and hence the studies should be separately done in these 2 groups to assess the benefit of SRS/SBRT.

Objective: There is a lack of recent meta-analyses and systematic reviews on the use of ultra-low-dose CT (ULDCT) for the detection, measurement, and diagnosis of lung nodules. This review aims to summarize the latest advances of ULDCT in these areas.

Methods: A systematic review of studies in PubMed and Web of Science was conducted, using search terms specific to ULDCT and lung nodules. The included studies were published in the last 5 years (January 2019-August 2024). Two reviewers independently selected articles, extracted data, and assessed the risk of bias and concerns using the Quality Assessment of Diagnostic Accuracy Studies-II (QUADAS-II) tool. The standard-dose, low-dose, or contrast-enhanced CT served as the reference-standard CT to evaluate ULDCT.

Results: The literature search yielded 15 high-quality articles on a total of 1889 patients, of which 10, 3, and 2 dealt with the detection, measurement, and diagnosis of lung nodules. QUADAS-II showed a generally low risk of bias. The mean radiation dose for ULDCT was 0.22 ± 0.10 mSv (7.7%) against 2.84 ± 1.80 mSv for reference-standard CT. Nodule detection rates ranged from 86.1% to 100%. The variability of diameter measurements ranged from 2.1% to 14.4% against contrast-enhanced CT and from 3.1% to 8.29% against standard CT. The diagnosis rate of malignant nodules ranged from 75% to 91%.

Conclusions: ULDCT proves effective in detecting lung nodules while substantially reducing radiation exposure. However, the use of ULDCT for the measurement and diagnosis of lung nodules remains challenging and requires further research.

Advances in knowledge: When ULDCT reduces radiation exposure to 7.7%, it detects lung nodules at a rate of 86.1%-100%, with a measurement variance of 2.1%-14.4% and a diagnostic accuracy for malignancy of 75%-91%, suggesting the potential for safe and effective lung cancer screening.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a "black box" with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, that is, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.

Objectives: To quantify the stage-shift with prostate-specific membrane antigen (PSMA) PET/CT imaging in metastatic prostate cancer and explore treatment implications.

Methods: Single-centre, retrospective analysis of patients with newly diagnosed [¹⁸F]PSMA-1007 or [⁶⁸Ga]Ga-PSMA-11 PET/CT-detected metastatic prostate cancer who had baseline bone scintigraphy between January 2015 and May 2021. Patients were subclassified into oligometastatic and polymetastatic disease utilizing the STAMPEDE2 trial (ISRCTN66357938/NCT06320067) definition. Patient, tumour, and treatment characteristics were collected. PSMA PET/CT concordance with conventional imaging (bone scintigraphy and low-dose CT of PET) was identified by number and site of metastases, and subgroup assigned. Spearman's rank correlation and linear regression modelling determined the association between the imaging modalities.

Results: We analysed 62 patients with a median age was 72 years (range 48-86). On PSMA PET/CT, 31/62 (50%) patients had oligometastatic disease, and 31/62 (50%) had polymetastatic disease. Prostate radiotherapy was delivered in 20/31 (65%) patients with oligometastatic disease and 17/31 (55%) with polymetastatic disease. 23/62 (37%) patients were reclassified as M0 on conventional imaging. PSMA PET/CT had a 2.9-fold increase in detecting bone metastases. Bone metastases concordance was found in 10/50 (20%) by number and 30/33 (91%) by site. PSMA PET/CT had a 2.2-fold increase in detecting nodal metastases. Nodal metastases concordance was found in 5/46 (11%) by number and 25/26 (96%) by site. There was significant positive correlation between PSMA PET/CT and conventional imaging for detecting bone [R ² = 0.25 (P < 0.001)] and nodal metastases [R ² = 0.19 (P < 0.001)]. 16/31 (52%) had oligometastatic disease concordance.

Conclusion: The magnitude of PSMA PET/CT-driven stage-shift is highly variable and unpredictable with implications on treatment decisions, future trial design, and potentially clinical outcomes.

Advances in knowledge: The magnitude of "frame-shift" with PSMA PET/CT imaging is highly variable and unpredictable which may unreliably change treatment decisions dependent on image-defined disease extent. Prospective randomized trials are required to determine the relationship between PSMA PET/CT-guided treatment choices and outcomes.

Objectives: To apply CT-based foundational artificial intelligence (AI) and radiomics models for predicting overall survival (OS) for patients with locally advanced non-small cell lung cancer (NSCLC).

Methods: Data for 449 patients retrospectively treated on the NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 clinical trial were analyzed. Foundational AI, radiomics, and clinical features were evaluated using univariate cox regression and correlational analyses to determine independent predictors of survival. Several models were fit using these predictors and model performance was evaluated using nested cross-validation and unseen independent test datasets via area under receiver-operator-characteristic curves, AUCs.

Results: For all patients, the combined foundational AI and clinical models achieved AUCs of 0.67 for the Random Forest (RF) model. The combined radiomics and clinical models achieved RF AUCs of 0.66. In the low-dose arm, foundational AI alone achieved AUC of 0.67, while AUC for the ensemble radiomics and clinical models was 0.65 for the support vector machine (SVM). In the high-dose arm, AUC values were 0.67 for combined radiomics and clinical models and 0.66 for the foundational AI model.

Conclusions: This study demonstrated encouraging results for application of foundational AI and radiomics models for prediction of outcomes. More research is warranted to understand the value of ensemble models toward improving performance via complementary information.

Advances in knowledge: Using foundational AI and radiomics-based models we were able to identify significant signatures of outcomes for NSCLC patients retrospectively treated on a national cooperative group clinical trial. Associated models will be important for application toward prospective patients.

Objectives: To gather and synthesize evidence regarding diagnostic accuracy of perfusion imaging by CT (CTP) or MR (MRP) for brain death (BD) diagnosis.

Methods: A systematic review and meta-analysis was prospectively registered with PROSPERO (CRD42022336353) and conducted in accordance with the PRISMA guidelines and independently by 3 reviewers. PubMed/MEDLINE, EMBASE and Cochrane Database were searched for relevant studies. Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess studies' quality. Meta-analysis was performed using univariate random-effects models.

Results: Ten studies (328 patients) were included. Perfusion imaging (most commonly CTP, n = 8 studies) demonstrated a high sensitivity of 96.1% (95% CI, 89.5-98.6) for BD, consistent in subgroup analysis at 95.5% (95% CI, 86.5-98.6). Unfortunately, it was not feasible to calculate other metrics. Additionally, evidence of publication bias was identified in our findings.

Conclusions: The sensitivity of CTP or MRP for BD diagnosis is very high, comparable to CTA and TCD. However, considering most studies were retrospective, and lacked control groups and unambiguous criteria for perfusion imaging in BD assessment, results should be interpreted with caution. Future studies, ideally prospective, multi-centre, and with control groups are of utmost importance for validation of these methods, particularly with standardized technical parameters.

Advances in knowledge: Cerebral perfusion imaging using CT or MRI demonstrates high sensitivity in diagnosing BD, on par with CTA and TCD. Recommended by the World Brain Death group, this method holds promise for further investigation in this area.

Prospero registration number: CRD42022336353.

Post-mortem CT (PMCT) is increasingly used in adult post-mortem investigations as a non-invasive alternative to traditional autopsies. Using PMCT supports death investigations in the face of severe pathologist workforce shortages and the less invasive nature maintains respect for cultural sensitivities. This article reviews the diverse service structures of PMCT, highlighting the importance of customizing these structures to meet the specific needs of various coronial jurisdictions. These jurisdictions often face challenges such as limited access to imaging facilities and logistical issues with geographically dispersed mortuaries. We outline options for leading and operating PMCT services, including models led by pathologists, radiologist, or a hybrid of the two; use of static, relocatable, or mobile CT scanning units; as well as making the most of existing resources such as NHS or private scanning facility scanners already in place. We also explore different PMCT reporting structures through in-house NHS radiologists, combined in-house and teleradiology, or fully outsourced teleradiology services. Each of these offerings provides different levels of efficiency, cost-effectiveness, data security and challenges to set-up. Where applicable, we present and describe real-world examples as case studies for readers interested in replicating existing models.

Objectives: Coronary CT angiography (CCTA) is becoming increasingly important in the workup of coronary artery disease. Imaging of stents and in-stent stenoses remains a challenge. This work investigates the assessability of in-stent stenoses in photon counting CT (PCCT) using ultra-high-resolution (UHR) imaging and optimized reconstruction kernels.

Methods: In an established phantom, 6 stents with inserted hypodense stenoses were scanned in both standard resolution (SRM) and UHR in a clinical PCCT scanner (NAEOTOM Alpha, Siemens Healthineers, Germany). Reconstructions were made both with the clinically established and optimized kernels. The visible stent lumen and the extent of stenosis were quantitatively measured and compared with the angiographic reference standard. Also, region-of-interest (ROI)-based measurements and a qualitative assessment of image quality were performed.

Results: The visible stent lumen and the extent of stenosis were measured more precisely in UHR compared to SRM (0.11 ± 0.19 vs 0.41 ± 0.22 mm, P < .001). The optimized kernel further improved the accuracy of the measurements and image quality in UHR (0.35 ± 0.23 vs 0.47 ± 0.19 mm, P < .001). Compared to angiography, stenoses were overestimated in PCCT, on average with an absolute difference of 18.20% ± 4.11%.

Conclusions: Photon counting CCTA allows improved imaging of in-stent stenoses in a phantom using UHR imaging and optimized kernels. These results support the use of UHR and optimized kernels in clinical practice and further studies.

Advances in knowledge: UHR imaging and optimized reconstruction kernels should be used in CCTA in the presence of cardiac stents.

Objectives: The aim of this study was to systematically review the literature to assess the application of AI-based interventions in lung cancer screening, and its future implications.

Methods: Relevant published literature was screened using PRISMA guidelines across three databases: PubMed, Scopus, and Web of Science. Search terms for article selection included "artificial intelligence," "radiology," "lung cancer," "screening," and "diagnostic." Included studies evaluated the use of AI in lung cancer screening and diagnosis.

Results: Twelve studies met the inclusion criteria. All studies concerned the role of AI in lung cancer screening and diagnosis. The AIs demonstrated promising ability across four domains: (1) detection, (2) characterization and differentiation, (3) augmentation of the work of human radiologists, (4) AI implementation of the LUNG-RADS framework and its ability to augment this framework. All studies reported positive results, demonstrating in some cases AI's ability to perform these tasks to a level close to that of human radiologists.

Conclusions: The AI systems included in this review were found to be effective screening tools for lung cancer. These findings hold important implications for the future use of AI in lung cancer screening programmes as they may see use as an adjunctive tool for lung cancer screening that would aid in making early and accurate diagnosis.

Advances in knowledge: AI-based systems appear to be powerful tools that can assist radiologists with lung cancer screening and diagnosis.