Polish journal of radiology最新文献

Purpose: Ovarian cancer is the fifth fatal cancer among women. Positron emission tomography (PET), which offers detailed metabolic data, can be effectively used for early cancer screening. However, proper attenuation correction is essential for interpreting the data obtained by this imaging modality. Computed tomography (CT) imaging is commonly performed alongside PET imaging for attenuation correction. This approach may introduce some issues in spatial alignment and registration of the images obtained by the two modalities. This study aims to perform PET image attenuation correction by using generative adversarial networks (GANs), without additional CT imaging.

Material and methods: The PET/CT data from 55 ovarian cancer patients were used in this study. Three GAN architectures: Conditional GAN, Wasserstein GAN, and CycleGAN, were evaluated for attenuation correction. The statistical performance of each model was assessed by calculating the mean squared error (MSE) and mean absolute error (MAE). The radiological performance assessments of the models were performed by comparing the standardised uptake value and the Hounsfield unit values of the whole body and selected organs, in the synthetic and real PET and CT images.

Results: Based on the results, CycleGAN demonstrated effective attenuation correction and pseudo-CT generation, with high accuracy. The MAE and MSE for all images were 2.15 ± 0.34 and 3.14 ± 0.56, respectively. For CT reconstruction, such values were found to be 4.17 ± 0.96 and 5.66 ± 1.01, respectively.

Conclusions: The results showed the potential of deep learning in reducing radiation exposure and improving the quality of PET imaging. Further refinement and clinical validation are needed for full clinical applicability.

Purpose: To evaluate the diagnostic accuracy of different abbreviated magnetic resonance imaging (AMRI) protocols consisting of dynamic enhanced + T2-weighted imaging (T2W) and diffusion-weighted imaging (DWI) + T2W for the detection and characterization of liver metastases in a patient group with known colorectal cancer.

Material and methods: A total of 197 hepatic lesions were retrospectively analyzed across 3 different MRI sets: AMRI-1 (dynamic enhanced + T2W), AMRI-2 (DWI + T2W), and a standard MRI protocol. The patient cohort included 100 individuals (63 males, 37 females) with a mean age of 62.6 years (SD: 11.1 years). Lesions were characterized as benign, malignant, or indeterminate based on histopathology, positron emission tomography-computed tomography (PET-CT), and follow-up imaging.

Results: The standard MRI protocol identified 197 liver lesions (175 metastatic, 18 benign, and 4 indeterminate); 142 lesions (72.1%) were larger than 10 mm, with the majority being metastatic (140/142). Radiologist 1 identified 195 lesions using the AMRI-1 protocol (175 metastatic, 15 benign, and 5 indeterminate). The sensitivity per lesion was 89.7% (95% CI: 0.85-0.93). Radiologist 2 identified 183 lesions using the AMRI-2 protocol (169 metastatic, 6 benign, and 8 indeterminate). The sensitivity per lesion was 92.3% (95% CI: 0.88-0.95). No statistically significant difference was found in sensitivity between the AMRI-1 and AMRI-2 and standard MRI protocols (p > 0.05).

Conclusions: The standard MRI protocol demonstrated the highest sensitivity and specificity for detecting and characterizing liver metastases. However, differences between the protocols were not statistically significant. Abbreviated MRI protocols, particularly the AMRI-2 protocol incorporating diffusion-weighted imaging, could serve as an effective alternative for routine clinical practice.

Purpose: This study aimed to compare the findings of magnetic resonance imaging (MRI) and ¹⁸F-fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) to differentiate reactive lymphadenitis from nodal lymphoma of the head and neck.

Material and methods: This study included 138 patients with histopathologically confirmed cervical lymphadenopathy, including 35 patients with reactive lymphadenitis and 103 patients with nodal lymphoma, who had neck MRI (n = 63) and/or ¹⁸F-FDG-PET/CT (n = 123) before biopsy. The quantitative and qualitative MRI results and maximum standardised uptake value (SUV_max) were retrospectively analysed and compared between the 2 pathologies.

Results: The maximum diameter (22.4 ± 6.9 vs. 33.3 ± 16.0 mm, p < 0.01), minimum diameter (15.8 ± 3.6 vs. 22.3 ± 8.5 mm, p < 0.01), and SUV_max (6.9 ± 2.7 vs. 12.8 ± 8.0, p < 0.01) of the lesion were lower in reactive lymphadenitis than in nodal lymphoma, respectively. T2-hypointense-thickened capsules > 2 mm (46% vs. 14%, p < 0.05) and T2-hypointense areas converging to the periphery (15% vs. 0%, p < 0.05) were more frequently observed in reactive lymphadenitis than in nodal lymphoma, respectively. Hilum of nodes on T2-weighted images (54% vs. 22%, p < 0.05) and diffusion-weighted images (69% vs. 30%, p < 0.05) were more frequently demonstrated in reactive lymphadenitis than in nodal lymphoma, respectively.

Conclusions: Reactive lymphadenitis had a smaller size and lower SUV_max. The presence of T2-hypointense-thickened capsules, T2-hypointense areas converging to the periphery, and hilum of nodes were signs of reactive lymphadenitis.

Purpose: Early detection of breast cancer is crucial for improving patient outcomes. With advancements in artificial intelligence (AI), there is growing interest in its potential to assist radiologists in interpreting mammograms for early cancer detection. AI algorithms offer the promise of increased accuracy and efficiency in identifying subtle signs of breast cancer, potentially complementing the expertise of radiologists and enhancing the screening process for early-stage breast cancer detection.

Material and methods: A systematic literature review was conducted to identify and select original research reports on breast cancer diagnosis by artificial intelligence versus conventional radiologists in using mammograms in accordance with the PRISMA guidelines. Data were analysed with Review Manager version 5.4. P-value and I² were used to test the significance of differences.

Results: This systematic review and meta-analysis included 8 studies with data from a total of 120,950 patients. Regarding the sensitivity of AI, the pooled analysis of 6 studies with sensitivities ranging from 0.70 to 0.89 yielded a sensitivity of 0.85. However, the sensitivity of the radiologists ranged from 0.63 to 0.85, with an overall sensitivity of 0.77. As for specificity, both radiologists and AI groups had closer results.

Conclusions: The comparison between AI systems and radiologists in detecting early-stage breast cancer from mammograms highlights the potential of AI as a valuable tool in breast cancer screening. While AI algorithms have shown promising results in terms of accuracy and efficiency, they should be viewed as complementary to radiologists rather than replacements.

Purpose: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.

Material and methods: The study utilised the mini-MIAS database of mammography images and examined the impact of applying various preprocessing method combinations to differentiate between malignant and benign breast lesions. The preprocessing steps included removing label information and pectoral muscle, followed by applying algorithms such as contrast-limited adaptive histogram equalisation (CLAHE), unsharp masking (USM), and median filtering (MF) to enhance image resolution and visibility. After preprocessing, a k-means clustering technique was used to extract potentially suspicious regions, and features were then extracted from these regions of interest (ROIs). The extracted feature datasets were classified using various machine learning algorithms, including artificial neural networks, random forest, and support vector machines.

Results: The findings showed that the combination of CLAHE, USM, and MF preprocessing algorithms resulted in the highest classification performance, outperforming the use of CLAHE alone.

Conclusions: The integration of advanced preprocessing techniques with machine learning significantly enhances the accuracy of mammography analysis, facilitating more precise differentiation between malignant and benign breast lesions.

Purpose: Isocitrate dehydrogenase (IDH) mutation status serves as a crucial prognostic indicator for glioma, typically assessed via immunohistochemical analysis post-surgery. Given the invasiveness of this approach, perhaps we can utilise convenient and noninvasive magnetic resonance imaging (MRI) methods to predict IDH mutation status. However, the current landscape lacks a standardised MRI technique for accurately predicting IDH mutations. In this study, we explore the potential of MRI diffusion kurtosis imaging (DKI) in forecasting the IDH mutation status of WHO grade II brain gliomas.

Material and methods: Twenty-five patients with WHO grade II gliomas were retrospectively included. Patients underwent routine MRI and DKI scanning before surgery, measuring tumoural solid portion, peritumoral oedema, and normal-appearing white matter (NAWM) DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (Ka), and axial radial kurtosis (Kr). The DKI parameter corrections were made (tumour or oedema parameters values divided by the NAWM value) to obtain the rFA (ratio of FA), rMD (ratio of MD), rMK (ratio of MK), rKA (ratio of KA), and rKr (ratio of Kr) values. Postoperative specimens were made of wax blocks and analysed by Sanger gene sequencing. DKI parameters between the 2 groups were compared by independent sample t-tests. The ROC curve was used to analyse the diagnostic value of each parameter.

Results: Twenty-five patients were diagnosed with IDH-mutant (16 cases) and IDH-wild type (9 cases). The rFA and rMK values in the parenchymal region of IDH wild-type tumour were higher than those of IDH mutant, while the rMD values were lower than those of IDH mutant, and the difference between them was statistically significant (p < 0.05). The values of DKI parameters of peritumoral oedema in the 2 groups were not statistically significant.

Conclusions: DKI can provide microstructural changes of diseased tissues and provide more imaging information for preoperative non-invasive judgment of IDH mutation status of WHO grade II gliomas. The values of rMK, rFA, and rMD are helpful in the assessment IDH mutation status, benefiting accurate diagnoses and treatment decisions.

Purpose: Recurrent malignant ascites is a common and challenging condition in cancer patients, often lacking a standardized treatment protocol. Small-scale studies in the literature have been insufficient to establish a treatment standard. The aim of our study was to investigate the effectiveness and safety of pigtail peritoneal catheter application in the treatment of malignant ascites.

Material and methods: We conducted a retrospective analysis of patients who had pigtail catheters inserted between January 2017 and December 2022. The study focused on the success rate of the procedure, catheter dwell time, complication rates, and the factors influencing these outcomes.

Results: A total of 196 patients, 102 of whom were female, with malignant ascites were included in the study. The median age was 65.5 years. The procedure had a 100% success rate, with no procedure-related deaths or major complications. The overall minor complication rate, including bleeding, minor cellulitis, leakage, and blockage, was 52%. Patients with comorbidities experienced statistically significantly more complications (p < 0.05). The median catheter dwell time was 31 days (range: 3-181 days).

Conclusions: Pigtail peritoneal catheters provide effective and safe minimally invasive fluid drainage for the treatment of malignant ascites. The most critical factors for preventing procedure-related complications and ensuring procedural success include proper indication, correct timing, procedural expertise, and the involvement of well-trained patients and caregivers.

Purpose: To study the distinct imaging characteristics of parenchymal neurocysticercosis (NCC) that aid in distinguishing it from other diseases.

Material and methods: Two hundred fifty patients with NCC were selected based on identification of the scolex. T2 weighted, T1 fluid attenuated inversion recovery (FLAIR), T2 FLAIR, susceptibility weighted imaging, constructive interference in steady state, diffusion weighted imaging, and T1 weighted contrast sequences were performed. The imaging characteristics of the cysts at various stages were analyzed.

Results: This study presents previously undocumented imaging findings of NCC, establishing absolute, major, and minor criteria for its diagnosis based on magnetic resonance imaging features.

Conclusions: Utilizing imaging criteria for diagnosing NCC, as per the study's sequences, can effectively narrow down differential diagnoses and lead to accurate identification.

Purpose: Monocyte chemoattractant protein-1 (MCP-1/CCL2) plays a key role for infiltration of monocytes/macrophages and studies have demonstrated that the MCP-1/C-C chemokine receptor 2 (CCR2) axis might be involved in the pathogenesis and progression of abdominal aortic aneurysms (AAA). Molecular imaging has shown potential for human clinical research studies. We evaluated the expression of CCR2 in patients with small AAA using single-photon emission computed tomography (SPECT) with the technetium-99m-6-hydrazinylnicotinoyl-C-C-chemokine receptor-2 ligand (^99mTc-HYNIC-CCR2-L).

Material and methods: A pilot study was performed to evaluate patients with small asymptomatic AAA. The equipment used was a Symbia T2 (Siemens, Germany), with radiolabeled ^99mTc-HYNIC-CCR2-L. The SPECT uptake and activity were assessed and counted based on the region of interest (ROI), with nonparametric statistics being employed to compare the aneurysms site, left ventricle (Control 1) and regions with a nondiseased aorta (Control 2).

Results: The three patients were male (100%) (mean age 81 years, and mean AAA maximum diameter of 40 mm, SD 3 mm). All patients tolerated the studies well. Images were obtained at one, two and four hours. The ROI mean value of the aneurysm site was 37,783 (SD 11,890), compared to the left ventricle (Control 1) 16,779 (SD 4397) (p-value = 0.0001); ROI for the nondiseased aortic region (Control 2) was significantly lower, 12,520 (SD 2141) (p-value = 0.0001).

Conclusions: Significant differences of CCR2 expression SPECT were found in the AAA site compared to the left ventricle and nondiseased aortic segments. The introduction of well-designed longitudinal studies with nuclear imaging modalities may assist in the molecular characterization of aneurysmal and rupture prediction.

Intramedullary tumours (IMTs) are the least common neoplasms of the spinal canal. The majority of them are ependymomas and astrocytomas, the third commonest is haemangioblastoma, while other tumours of the spinal cord are relatively rare. This review presents on update on the imaging of spinal cord tumours. Magnetic resonance imaging (MRI) is the imaging method of choice in diagnosing IMTs, with other modalities playing a supplementary role. The authors discuss the MRI protocol in IMTs including advanced techniques and present the imaging features of particular tumours. The differentiation of IMTs from other spinal cord diseases is also presented.