Polish journal of radiology最新文献

Purpose: The aim of the study was to analyse the usefulness of core biopsy in the diagnosis of malignant neoplasms of the pancreas - sensitivity and accuracy of diagnosis, safety of the procedure, indication of factors that may increase the risk of complications after biopsy.

Material and methods: A retrospective analysis of data was performed in a group of 100 patients diagnosed with a focal lesion of the pancreas, qualified for a core biopsy.

Results: The results are a sensitivity of 92%, a specificity of 100%, and an accuracy of 93.3%. The incidence of more severe complications according to the Clavien-Dindo classification was 1% (one case in the material studied). The results of the analysis were compared with the results of other authors, showing similar values for the sensitivity and specificity of the method and low rates of serious complications; it also seems that the tissue material obtained by core biopsy has higher diagnostic potential than that obtained by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), currently considered the method of choice. In addition, the costs of transabdominal biopsy and endoscopic biopsy were compared; the lower cost of the former may be an important economic issue when choosing the biopsy method.

Conclusions: The results show core biopsy to be a sensitive, accurate, and safe method for obtaining the tissue necessary to plan treatment in patients with pancreatic cancer.

Purpose: Our aim was to distinguish between low-grade and high-grade brain tumours on the basis of dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion and arterial spin labelling (ASL) perfusion and to compare DSC and ASL techniques.

Material and methods: Forty-one patients with brain tumours were evaluated by 3-Tesla MRI. Conventional and perfusion MRI imaging with a 3D pseudo-continuous ASL (PCASL) and DSC perfusion maps were evaluated. Three ROIs were placed to obtain cerebral blood value (CBV) and cerebral blood flow (CBF) in areas of maximum perfusion in brain tumour and normal grey matter. Histopathological diagnosis was considered as the reference. ROC analysis was performed to compare the diagnostic performance and to obtain a feasible cut-off value of perfusion parameters to differentiate low-grade and high-grade brain tumours.

Results: Normalised perfusion parameters with grey matter (rCBF or rCBV lesion/NGM) of malignant lesions were significantly higher than those of benign lesions in both DSC (normalised rCBF of 2.16 and normalised rCBV of 2.63) and ASL (normalised rCBF of 2.22) perfusion imaging. The normalised cut-off values of DSC (rCBF of 1.1 and rCBV of 1.4) and ASL (rCBF of 1.3) showed similar specificity and near similar sensitivity in distinguishing low-grade and high-grade brain tumours.

Conclusions: Quantitative analysis of perfusion parameters obtained by both DSC and ASL perfusion techniques can be reliably used to distinguish low-grade and high-grade brain tumours. Normalisation of these values by grey matter gives us more reliable parameters, eliminating the different technical parameters involved in both the techniques.

Colorectal cancer (CRC) is one of the most common malignancies in the world. Nowadays many treatments are available to help control CRC, including surgery, radiation therapy, interventional radiology, and drug treatments. A multidisciplinary approach and the role of radiologists is needed to assist the surgeon in the management thanks to emerging technology and strategies. The Response Evaluation Criteria in Solid Tumours (RECIST) has been created to objectify and standardize cancer response assessment. Thus, in this article specific presumptions and practical aspects of evaluating responses according to the RECIST 1.1 are discussed. Furthermore, examples of possible response to systemic treatment of colorectal liver metastases (CRLM), including tumour necrosis, apparent diffusion coefficient (ADC) values, tumour calcification, tumour fibrosis and intratumoural fat deposition observed on cross-sectional imaging, are described. Disappearing liver metastases (DLM) presents a therapeutic dilemma. The optimal management of DLM remains controversial due to the uncertainty of residual microscopic disease and effective long-term outcomes. The article provides an overview of the CRLM phenomenon and current possible assessment methods of the response to systemic treatment.

Purpose: Early depiction of bony erosions in sacroiliac (SI) joints increases the diagnostic accuracy of spondyloarthritis. The new 3D magnetic resonance imaging (MRI) sequence THRIVE (T1-weighted high-resolution isotropic volume examination) can depict cartilage erosions in sacroiliac joints. The aim of the study was to compare the diagnostic capacity of the new MRI sequence 3D THRIVE (T1-weighted high-resolution isotropic volume examination) with the routinely used T1 TSE pulse sequence in the depiction of structural erosions in sacroiliac joints by using MRI sequence zero echo time (zero ET) as a reference standard.

Material and methods: Seventy five adult patients were included in this study. They underwent MRI sacroiliac joints examination using routine T1 TSE and STIR pulse sequences with the addition of the new 3D THRIVE and zero echo time (zero ET) sequences. Images of T1 TSE, 3D THRIVE, and zero ET sequences were evaluated by 2 radiolo-gists separately for the detection of sacroiliac joints erosions, then a comparison between T1 TSE and 3D THRIVE sequences was done using a CT-like image MRI sequence zero ET as a reference standard. Sensitivity, specificity, and accuracy for each sequence were calculated by the 2 readers.

Results: Sensitivity, specificity, and accuracy of 3D THRIVE were higher than those of T1 TSE for reader 1 (sensitivity: 94.5% vs. 86.2%; specificity: 93.4% vs. 85.1%; and accuracy 95.2% vs. 88.5%) and for reader 2 (sensitivity: 93.3% vs. 79.9%; specificity: 94.7% vs. 86.2%; and accuracy 95.8% vs. 82.1%).

Conclusions: Using CT-like image MRI sequence zero ET as the reference standard, 3D THRIVE pulse sequencing of the sacroiliac joints has much better diagnostic value in the depiction of bony erosions in patients suspected having spondyloarthritis as compared to the routinely used T1 TSE sequence.

Purpose: X-ray images are viewed as a vital component in emergency diagnosis. They are often used by deep learning applications for disease prediction, especially for thoracic pathologies. Pneumonia, a fatal thoracic disease induced by bacteria or viruses, generates a pleural effusion where fluids are accumulated inside lungs, leading to breathing difficulty. The utilization of X-ray imaging for pneumonia detection offers several advantages over other modalities such as computed tomography scans or magnetic resonance imaging. X-rays provide a cost-effective and easily accessible method for screening and diagnosing pneumonia, allowing for quicker assessment and timely intervention. However, interpretation of chest X-ray images depends on the radiologist's competency. Within this study, we aim to suggest new elements leading to good interpretation of chest X-ray images for pneumonia detection, especially for distinguishing between viral and bacterial pneumonia.

Material and methods: We proposed an interpretation model based on convolutional neural networks (CNNs) and extreme gradient boosting (XGboost) for pneumonia classification. The experimental study is processed through various scenarios, using Python as a programming language and a public database obtained from Guangzhou Women and Children's Medical Centre.

Results: The results demonstrate an acceptable accuracy of 87% within a mere 7 seconds, thereby endorsing its effectiveness compared to similar existing works.

Conclusions: Our study provides a model based on CNN and XGboost to classify images of viral and bacterial pneumonia. The work is a challenging task due to the lack of appropriate data. The experimental process allows a better accuracy of 87%, a specificity of 89%, and a sensitivity of 85%.

Purpose: To detect tuberculosis (TB) at an early stage by analyzing chest X-ray images using a deep neural network, and to evaluate the efficacy of proposed model by comparing it with existing studies.

Material and methods: For the study, an open-source X-ray images were used. Dataset consisted of two types of images, i.e., standard and tuberculosis. Total number of images in the dataset was 4,200, among which, 3,500 were normal chest X-rays, and the remaining 700 X-ray images were of tuberculosis patients. The study proposed and simulated a deep learning prediction model for early TB diagnosis by combining deep features with hand-engineered features. Gabor filter and Canny edge detection method were applied to enhance the performance and reduce computation cost.

Results: The proposed model simulated two scenarios: without filter and edge detection techniques and only a pre-trained model with automatic feature extraction, and filter and edge detection techniques. The results achieved from both the models were 95.7% and 97.9%, respectively.

Conclusions: The proposed study can assist in the detection if a radiologist is not available. Also, the model was tested with real-time images to examine the efficacy, and was better than other available models.

Using coronary computed tomography angiography (CCTA), coronary plaques can be characterized based on both their morphology and composition. Coronary plaques are generally assessed on 2D axial and multiplanar reformatted images. Nevertheless, these visualization tools are limited to observing extraluminal changes in the coronary artery. The presence of plaques prevents them from providing a visual representation of the intraluminal coronary wall. Since its invention in 2000, coronary fly-through or virtual angioscopy (VA) has been extensively studied. However, its application was limited because it required an optimal CT scan and time-consuming post-processing. In recent years, advances in post-processing software have made construction of VA easier, but until recently the quality of the images was insufficient for most patients. Using 3D intravascular endoscopy (3DIE) visualization, we present various intraluminal appearances of the coronary wall and plaque in relation to various types of plaque.

Purpose: Rapid development of artificial intelligence has aroused curiosity regarding its potential applications in medical field. The purpose of this article was to present the performance of ChatGPT, a state-of-the-art language model in relation to pass rate of national specialty examination (PES) in radiology and imaging diagnostics within Polish education system. Additionally, the study aimed to identify the strengths and limitations of the model through a detailed analysis of issues raised by exam questions.

Material and methods: The present study utilized a PES exam consisting of 120 questions, provided by Medical Exami-nations Center in Lodz. Questions were administered using openai.com platform that grants free access to GPT-3.5 model. All questions were categorized according to Bloom's taxonomy to assess their complexity and difficulty. Following the answer to each exam question, ChatGPT was asked to rate its confidence on a scale of 1 to 5 to evaluate the accuracy of its response.

Results: ChatGPT did not reach the pass rate threshold of PES exam (52%); however, it was close in certain question categories. No significant differences were observed in the percentage of correct answers across question types and sub-types.

Conclusions: The performance of the ChatGPT model in the pass rate of PES exam in radiology and imaging diagnostics in Poland is yet to be determined, which requires further research on improved versions of ChatGPT.

Purpose: Left atrial calcification (LAC), a primarily radiologic diagnosis, has been associated with rheumatic heart disease (RHD) and rheumatic fever (RF). However, left atrial calcification continues to be observed despite a significant decrease in the prevalence of rheumatic heart disease. The purpose of this study was to investigate other possible etiologies of left atrial calcification.

Material and methods: This retrospective, observational single-center study included patients from 2017 to 2022 identified as having left atrial calcification as well as age- and sex-matched controls. The prevalence of rheumatic heart disease, atrial ablation, and mitral valve disease was compared, and odds ratios were calculated for each independent variable.

Results: Sixty-two patients with left atrial calcifications were included and compared with 62 controls. 87.1% of patients in the left atrial calcifications cohort had a history of atrial fibrillation compared with 21% in the control cohort (p < 0.001). 16.1% of patients in the calcifications cohort presented a history of rheumatic fever compared with zero in the control cohort (p = 0.004). 66.1% of the left atrial calcifications cohort had a history of atrial ablation compared with 6.5% of the control group (p < 0.001). The odds ratio for left atrial calcification was 19.0 vs. 4.8 for rheumatic fever (comparative odds = 4.0 for atrial ablation vs. rheumatic fever). Multivariable log model found atrial ablation to explain 79.8% of left atrial calcifications identified.

Conclusions: Our study found a 4-fold higher association between history of atrial ablation and left atrial calcification compared with rheumatic heart disease, suggesting a potential shift in etiology.

Cancer, as the second leading cause of death in the world, is one of the major public health concerns today. Accurate diagnosis and prompt initiation of adequate treatment are of key importance for prognosis. Abbreviated magnetic resonance protocols (AMRI) are promising techniques based on magnetic resonance imaging (MRI) protocols that shorten acquisition time without significant loss of examination quality. Faster protocols that focus on detection of suspicious lesions with most precise sequences, can contribute to comparable diagnostic performance of a full MRI protocol. The purpose of this article was to review the current application of AMRI protocols in several oncological diseases.