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Background: Technological progress in the acquisition of medical images and the extraction of underlying quantitative imaging data has introduced exciting prospects for the diagnostic assessment of a wide range of conditions. This study aims to investigate the diagnostic utility of a machine learning classifier based on dual‐energy computed tomography (DECT) radiomics for classifying pulmonary embolism (PE) severity and assessing the risk for early death. Methods: Patients who underwent CT pulmonary angiogram (CTPA) between January 2015 and March 2022 were considered for inclusion in this study. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient‐boosted tree models. Results: The trained machine learning classifier achieved a classification accuracy of.90 for identifying high‐risk PE patients with an area under the receiver operating characteristic curve of.59. This CT‐based radiomics signature showed good diagnostic accuracy for risk stratification in individuals presenting with central PE, particularly within higher risk groups. Conclusion: Models utilizing DECT‐derived radiomics features can accurately stratify patients with pulmonary embolism into established clinical risk scores. This approach holds the potential to enhance patient management and optimize patient flow by assisting in the clinical decision‐making process. It also offers the advantage of saving time and resources by leveraging existing imaging to eliminate the necessity for manual clinical scoring. [ABSTRACT FROM AUTHOR]

Background: Cancer is a well‐known risk factor for venous thromboembolism (VTE). A combined strategy of D‐dimer testing and clinical pre‐test probability is usually used to exclude VTE. However, its effectiveness is diminished in cancer patients due to reduced specificity, ultimately leading to a decreased clinical utility. This review article seeks to provide a comprehensive summary of how to interpret D‐dimer testing in cancer patients. Methods: In accordance with PRISMA standards, literature pertaining to the diagnostic and prognostic significance of D‐dimer testing in cancer patients was carefully chosen from reputable sources such as PubMed and the Cochrane databases. Results: D‐dimers have not only a diagnostic value in ruling out VTE but can also serve as an aid for rule‐in if their values exceed 10‐times the upper limit of normal. This threshold allows a diagnosis of VTE in cancer patients with a positive predictive value of more than 80%. Moreover, elevated D‐dimers carry important prognostic information and are associated with VTE reoccurrence. A gradual increase in risk for all‐cause death suggests that VTE is also an indicator of biologically more aggressive cancer types and advanced cancer stages. Considering the lack of standardization for D‐dimer assays, it is essential for clinicians to carefully consider the variations in assay performance and the specific test characteristics of their institution. Conclusions: Standardizing D‐dimer assays and developing modified pretest probability models specifically for cancer patients, along with adjusted cut‐off values for D‐dimer testing, could significantly enhance the accuracy and effectiveness of VTE diagnosis in this population. [ABSTRACT FROM AUTHOR]

The evolving field of medical education is being shaped by technological advancements, including the integration of Large Language Models (LLMs) like ChatGPT. These models could be invaluable resources for medical students, by simplifying complex concepts and enhancing interactive learning by providing personalized support. LLMs have shown impressive performance in professional examinations, even without specific domain training, making them particularly relevant in the medical field. This study aims to assess the performance of LLMs in radiology examinations for medical students, thereby shedding light on their current capabilities and implications.This study was conducted using 151 multiple-choice questions, which were used for radiology exams for medical students. The questions were categorized by type and topic and were then processed using OpenAI's GPT-3.5 and GPT- 4 via their API, or manually put into Perplexity AI with GPT-3.5 and Bing. LLM performance was evaluated overall, by question type and by topic.GPT-3.5 achieved a 67.6% overall accuracy on all 151 questions, while GPT-4 outperformed it significantly with an 88.1% overall accuracy (p<0.001). GPT-4 demonstrated superior performance in both lower-order and higher-order questions compared to GPT-3.5, Perplexity AI, and medical students, with GPT-4 particularly excelling in higher-order questions. All GPT models would have successfully passed the radiology exam for medical students at our university.In conclusion, our study highlights the potential of LLMs as accessible knowledge resources for medical students. GPT-4 performed well on lower-order as well as higher-order questions, making ChatGPT-4 a potentially very useful tool for reviewing radiology exam questions. Radiologists should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses. · ChatGPT demonstrated remarkable performance, achieving a passing grade on a radiology examination for medical students that did not include image questions.. · GPT-4 exhibits significantly improved performance compared to its predecessors GPT-3.5 and Perplexity AI with 88% of questions answered correctly.. · Radiologists as well as medical students should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses.. · Gotta J, Le Hong QA, Koch V et al. Large language models (LLMs) in radiology exams for medical students: Performance and consequences. Fortschr Röntgenstr 2024; DOI 10.1055/a-2437-2067., Competing Interests: C.B. received speaking fees from Siemens Healthineers. The other authors have no potential conflict of interest to disclose., (Thieme. All rights reserved.)

Intussusception in adult patients is a rare medical finding, which is accompanied by an underlying tumor in some cases. However, no accepted method has been established to identify patients at risk for tumor-related intussusception. This study aimed to identify imaging features as predictors for tumor-related intussusception.CT images of patients with confirmed intussusception were retrospectively acquired between 01/2008 and 12/2022. Available follow-up images and medical health records were evaluated to identify various imaging features, the cause of intussusception, and treatment strategies. Imaging interpretation was conducted by two blinded radiologists. A third radiologist was consulted in cases of disagreement.A total of 71 consecutive patients were included in this study (42 males, 29 females) with a median age of 56 years (interquartile range: 40.5-73.8 years). Enteroenteric intussusceptions in the small bowel were the most common type observed in adult patients. In contrast, colocolic intussusception was more frequently associated with malignancy, and this association was statistically significant (p < 0.05). Among the malignant tumors, adenocarcinoma was the most common, followed by metastases and lymphoma. Additionally, bowel obstruction and wall thickening were significantly correlated with malignancy (p < 0.05). The high negative predictive values (NPVs) and high specificities for ileus (NPV 88.5%, specificity 82.1%), bowel wall thickening (NPV 90.9%, specificity 71.4%), and acute abdomen (NPV 84.6%, specificity 78.8%) suggest that the absence of these features strongly predicts a low probability of malignancy in cases of adult intussusception.Active surveillance with follow-up exams is suitable for asymptomatic and transient intussusception when imaging features suggest a low likelihood of a neoplasm. Additionally, malignancy predictors such as ileus and thickening of the bowel wall in the affected segment could guide tailored treatment. Surgical interventions are essential for symptomatic cases, with adenocarcinoma being the most common malignancy found in colocolic intussusceptions.Intussusception in adults is rare and is often associated with underlying tumors, particularly in colocolic intussusceptions. Key imaging predictors for malignancy include bowel obstruction, wall thickening in the affected segment, and the presence of acute abdomen, with high NPVs and specificities indicating low malignancy risk when these features are absent. Active surveillance is recommended for asymptomatic cases with low neoplasm probability, while surgical intervention is the method of choice for symptomatic patients. · Reschke P, Le Hong QA, Gruenewald LD et al. Malignancy predictors and treatment strategies for adult intestinal intussusception. Fortschr Röntgenstr 2024; DOI 10.1055/a-2434-7932., Competing Interests: C.B. received speaking fees from Siemens Healthineers. I.Y. received a speaking fee from Siemens Healthineers., (Thieme. All rights reserved.)

Background: Magnetic resonance elastography (MRE) can quantify tissue biomechanics noninvasively, including pathological hepatic states like metabolic dysfunction-associated steatohepatitis., Purpose: To compare the performance of 2D/3D-MRE using the gravitational (GT) transducer concept with the current commercial acoustic (AC) solution utilizing a 2D-MRE approach. Additionally, quality index markers (QIs) were proposed to identify image pixels with sufficient quality for reliably estimating tissue biomechanics., Study Type: Prospective., Population: One hundred seventy participants with suspected or confirmed liver disease (median age, 57 years [interquartile range (IQR), 46-65]; 66 females), and 11 healthy volunteers (median age, 31 years [IQR, 27-34]; 5 females)., Field Strength/sequence: Participants were scanned twice at 1.5 T and 60 Hz vibration frequency: first, using AC-MRE (2D-MRE, spin-echo EPI sequence, 11 seconds breath-hold), and second, using GT-MRE (2D- and 3D-MRE, gradient-echo sequence, 14 seconds breath-hold)., Assessment: Image analysis was performed by four independent radiologists and one biomedical engineer. Additionally, superimposed analytic plane shear waves of known wavelength and attenuation at fixed shear modulus were used to propose pertinent QIs., Statistical Tests: Spearman's correlation coefficient (r) was applied to assess the correlation between modalities. Interreader reproducibility was evaluated using Bland-Altman bias and reproducibility coefficients. P-values <0.05 were considered statistically significant., Results: Liver stiffness quantified via GT-2D/3D correlated well with AC-2D (r ≥ 0.89 [95% CI: 0.85-0.92]) and histopathological grading (r ≥ 0.84 [95% CI: 0.72-0.91]), demonstrating excellent agreement in Bland-Altman plots and between readers (κ ≥ 0.86 [95% CI: 0.81-0.91]). However, GT-2D showed a bias in overestimating stiffness compared to GT-3D. Proposed QIs enabled the identification of pixels deviating beyond 10% from true stiffness based on a combination of total wave amplitude, temporal sinusoidal nonlinearity, and wave signal-to-noise ratio for GT-3D., Conclusion: GT-MRE represents an alternative to AC-MRE for noninvasive liver tissue characterization. Both GT-2D and 3D approaches correlated strongly with the established commercial approach, offering advanced capabilities in abdominal imaging compared to AC-MRE., Evidence Level: 1 TECHNICAL EFFICACY: Stage 2., (© 2024 The Author(s). Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)