British Journal of Radiology最新文献

Objectives: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs).

Methods: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation.

Results: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76).

Conclusions: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC.

Advances in knowledge: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Acute ischaemic stroke (AIS) is a debilitating disease for which effective therapies are now available. Effective identification of candidates for therapy relies heavily on noninvasive imaging that must be interpreted accurately in a short timeframe. This review summarizes the evolution of AIS therapies and the implications for noninvasive imaging. The review concludes with consideration of longstanding assumptions about imaging of ischaemic stroke and potential paradigm shifts on the horizon.

Objective: Quantitative comparison of the diagnostic efficacy of conventional diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in differentiating between chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO).

Methods: A total of 48 patients with renal tumours who had undergone DWI and IVIM were divided into two groups-ChRCC (n = 28) and RO (n = 20) groups, and the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and their diagnostic efficacy were compared between the two groups.

Results: The D* values were higher in the ChRCCs group compared to the RO groups (0.019 ± 0.003 mm2/s vs 0.008 ± 0.002 mm2/s, P < .05). Moreover, the ADC, D and f values were higher in ROs compared to ChRCCs (0.61 ± 0.08 × 10-3 mm2/s vs 0.51 ± 0.06 × 10-3 mm2/s, 1.02 ± 0.15 × 10-3 mm2/s vs 0.86 ± 0.07 × 10-3 mm2/s, 0.41 ± 0.05 vs 0.28 ± 0.02, P < .05). The areas of the ADC, D, D* and f values under the ROC curves in differentiating ChRCCs from ROs were 0.713, 0.839, 0.856 and 0.906, respectively. The cut-off values of ADC, D, D* and f were 0.54, 0.91, 0.013 and 0.31, respectively. The AUC, sensitivity, specificity and accuracy of the f values were 0.906, 89.3%, 80.0% and 89.6%, respectively. For pairwise comparisons of ROC curves and diagnostic efficacy, IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCCs from ROs (P = .013, .016, and .008) with f having the highest diagnostic accuracy.

Conclusion: IVIM parameters presented better performance than ADC in differentiating ChRCCs from ROs.

Advances in knowledge: (1) D* values of ChRCCs were higher, while ADC, D and f values were lower than those of RO tumours. (2) f values had the highest diagnostic efficacy in differentiating ChRCC from RO. (3) IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCC from RO (P=.013, .016, and .008).

Small-field dosimetry used in special procedures such as gamma knife, Cyberknife, Tomotherapy, IMRT, and VMAT has been in evolution after several radiation incidences with very significant (70%) errors due to poor understanding of the dosimetry. IAEA-TRS-483 and AAPM-TG-155 have provided comprehensive information on small-fields dosimetry in terms of code of practice and relative dosimetry. Data for various detectors and conditions have been elaborated. It turns out that with a suitable detectors dose measurement accuracy can be reasonably (±3%) achieved for 6 MV beams for fields >1×1 cm2. For grid therapy, even though the treatment is performed with small fields created by either customized blocks, multileaf collimator (MLC), or specialized devices, it is multiple small fields that creates combined treatment. Hence understanding the dosimetry in collection of holes of small field is a separate challenge that needs to be addressed. It is more critical to understand the scattering conditions from multiple holes that form the treatment grid fields. Scattering changes the beam energy (softer) and hence dosimetry protocol needs to be properly examined for having suitable dosimetric parameters. In lieu of beam parameter unavailability in physical grid devices, MLC-based forward and inverse planning is an alternative path for bulky tumours. Selection of detectors in small field measurement is critical and it is more critical in mixed beams created by scattering condition. Ramification of small field concept used in grid therapy along with major consideration of scattering condition is explored. Even though this review article is focussed mainly for dosimetry for low-energy megavoltage photon beam (6 MV) but similar procedures could be adopted for high energy beams. To eliminate small field issues, lattice therapy with the help of MLC is a preferrable choice.

This review aims to serve as a foundational resource for general radiologists, enhancing their understanding of the role of Magnetic Resonance Imaging (MRI) in early prognostication for newborns diagnosed with hypoxic ischaemic encephalopathy (HIE). The article explores the application of MRI as a predictive instrument for determining long-term outcomes in newborns affected by HIE. With HIE constituting a leading cause of neonatal mortality and severe long-term neurodevelopmental impairments, early identification of prognostic indicators is crucial for timely intervention and optimal clinical management. We examine current literature and recent advancements to provide an in-depth overview of MRI predictors, encompassing brain injury patterns, injury scoring systems, spectroscopy, and diffusion imaging. The potential of these MRI biomarkers in predicting long-term neurodevelopmental outcomes and the probability of epilepsy is also discussed.

Objectives: To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast MRI to improve upon conventional risk assessment models in determining culprit carotid artery lesions.

Methods: Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features.

Results: Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone (area under curve [AUC], 0.819 ± 0.002 vs 0.689 ± 0.019, respectively, P = .014). The radiomic model alone also provided value over the conventional model (AUC, 0.805 ± 0.003 vs 0.689 ± 0.019, respectively, P = .031). T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix, Grey Level Dependence Matrix, and Grey Level Size Zone Matrix sub-types were particularly useful in identifying textures which could detect vulnerable lesions.

Conclusions: The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischaemic cerebrovascular events.

Advances in knowledge: The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischaemic stroke, beyond conventional stenosis measurement. This paper shows that in the case of carotid stroke, high-dimensional radiomics features can improve classification capabilities compared with stenosis measurement alone.

Objectives: To establish conversion coefficients (CCs), between mean absorbed dose to the brain and eye lens of the cardiologist and the air kerma-area product, PKA, for a set of projections in cardiac interventional procedures. Furthermore, by taking clinical data into account, a method to estimate the doses per procedure, or annual dose, is presented.

Methods: Thermoluminescence dosimeters were used together with anthropomorphic phantoms, simulating a cardiologist performing an interventional cardiac procedure, to estimate the CCs for the brain and eye lens dose for nine standard projections, and change in patient size and x-ray spectrum. Additionally, a single CC has been estimated, accounting for each projections fraction of use in the clinic and associated PKA using clinical data from the dose monitoring system in our hospital.

Results: The maximum CCs for the eye lens and segment of the brain, is 5.47 μGy/Gycm2 (left eye lens) and 1.71 μGy/Gycm2 (left brain segment). The corresponding weighted CCs: are 3.39 μGy/Gycm2 and 0.89 μGy/Gycm2, respectively.

Conclusions: Conversion coefficients have been established under actual scatter conditions, showing higher doses on the left side of the operator. Using modern interventional x-ray equipment, interventional cardiac procedures will not cause high radiation dose levels to the operator when a ceiling mounted shield is used, otherwise there is a risk that the threshold dose values for cataract will be reached.

Advance in knowledge: In addition to the CCs for the different projections, methods for deriving a single CC per cardiac interventional procedure and dose per year were introduced.

Calcific tendinopathy is characterized by the deposition of calcium hydroxyapatite crystals in various tendons of the body. Terms like calcium tendinitis, tendinosis, and tendinopathy are used interchangeably. Calcific tendinopathy is a common and well-documented ailment in the literature. Although common, the natural history, aetiology, and progression of calcific tendinitis are poorly understood. The treatment options include conservative and interventional measures. However, these measures cannot be applied as a blanket and are often tailored depending on the stage/phase of the disease. Out of the recognized stages of the disease, the resorptive stage causes the utmost symptoms when the calcium is rather soft and unstable. During this stage, the calcium may migrate beyond expected resorption and get deposited in the adjacent tissues contiguous with the calcium focus. The common destinations include bursal migration, intraosseous migration, muscular migration, and other less common migration sites. Such atypical presentations can lead to dilemmas in the diagnosis, prolongation of the diagnostic pathway, unwarranted apprehension, and treatment delay. Radiologists' role in this situation is to correctly recognize the imaging findings of atypical presentations of calcific tendinopathy and prevent unnecessary diagnostic and interventional studies. In this review article, we describe the pathogenic pathway and natural history of calcific tendinopathy from a radiologist's perspective and discuss different migratory patterns of calcium in calcific tendinopathy not only around the shoulder but also in other areas of the body on different imaging modalities. We also show a few examples of mimics and pitfalls on imaging. Finally, we discuss the appropriate management option of this condition.

Objectives: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI.

Methods: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group.

Results: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682).

Conclusion: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC.

Advances in knowledge: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).