Explore the vast range of titles available.

Background Implant malalignment in total knee arthroplasty (TKA) correlates with poor outcomes, and robotic-assisted systems aim to improve precision. While closed-platform robotic systems dominate the market, their restriction to proprietary implants limits surgical flexibility. This study evaluates the radiological accuracy of an open-platform robotic system (Yuanhua KUNWU) across four TKA implant designs. Methods A multi-centre retrospective analysis of 129 robotic-assisted TKAs (Zhengtian Irene, n  = 60; DePuy Synthes Attune, n  = 32; Zimmer Biomet Persona, n  = 20; Smith & Nephew Legion, n  = 17) was conducted. Patients with end-stage osteoarthritis (Kellgren-Lawrence grade 3–4) were included, while those with prior knee surgery or complex anatomy were excluded ( n  = 15). A total of 114 pre-operative and post-operative alignment (hip-knee-ankle angle [HKA], femoral and tibial component coronal angles [FCCA, TCCA], posterior tibial slope [PTS]) were measured on radiographs by two independent reviewers. Interobserver reliability (intra-class correlation [ICC], Cronbach’s α) and deviations from planned alignment (paired t -tests) were analysed. Acceptability was defined as ≤ 3° deviation. Results Interobserver reliability was excellent (ICC > 0.77, Cronbach’s α > 0.87 for all parameters). Mean post-operative deviations from planned alignment were clinically small: HKA (+1.32°, P  < 0.001), FCCA (−0.55°, P  < 0.001), TCCA (+0.19°, P  = 0.097), and PTS (−0.42°, P  = 0.018). All mean differences were within the 3° acceptability threshold. Subgroup analysis of pre- and post-operative alignment between implant types also showed deviations of < 3°. Conclusions The KUNWU open-platform robotic system achieved high radiological accuracy across four implant designs, with alignment deviations < 1.5°. This suggests open-platform robotics can provide implant versatility without compromising precision. Further studies regarding the assessment of long-term clinical and patient-reported outcomes and comparison with closed-platform systems are warranted.

Automatic radiological report generation (ARRG) smoothens the clinical workflow by speeding the report generation task. Recently, various deep neural networks (DNNs) have been used for report generation and have achieved promising results. Despite the impressive results, their deployment remains challenging because of their size and complexity. Researchers have proposed several pruning methods to reduce the size of DNNs. Inspired by the one-shot weight pruning methods, we present , a multi-attention based sparse radiology report generation method. It uses encoder-decoder based architecture equipped with a visual and semantic attention mechanism. The model is 70% pruned during the training to achieve 3.33 compression without sacrificing its accuracy. The empirical results evaluated on the OpenI dataset using BLEU, ROUGE, and CIDEr metrics confirm the accuracy of the sparse model viz- -viz the dense model.

BackgroundAs the predominant subtype of non-small cell lung cancer, lung adenocarcinoma exhibits a pathogenesis closely associated with molecular characteristics. Tumor mutational burden (TMB) has emerged as a critical biomarker for predicting responses to immunotherapy. Although computed tomography (CT) imaging is widely utilized in diagnosing lung adenocarcinoma and its morphological features may reflect genomic attributes, the precise relationship between TMB and CT radiographic features remains inadequately elucidated.ObjectiveThis study aimed to investigate the correlation between TMB and CT radiographic features in lung adenocarcinoma and to evaluate the diagnostic value of these features in identifying high TMB, thereby providing a non-invasive approach for TMB assessment.MethodsA total of 156 treatment-naïve lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) exon 19 deletion mutations, admitted to Funan County People’s Hospital between January 2022 and August 2025, were enrolled. Based on TMB levels, patients were stratified into high-TMB (TMB ≥ 10 mut/Mb, n = 52) and low-TMB (TMB < 10 mut/Mb, n = 104) groups. All participants underwent non-contrast and contrast-enhanced chest CT scans, and TMB was quantified via next-generation sequencing (NGS). Two experienced radiologists, blinded to TMB status, independently evaluated CT morphological features, including maximum tumor diameter, spiculation, lobulation, pleural indentation, cavity formation, vascular convergence, and mediastinal lymph node enlargement.ResultsThe high-TMB group exhibited a significantly larger maximum tumor diameter compared to the low-TMB group (t = 3.456, p < 0.05). The incidences of spiculation, lobulation, and vascular convergence were also significantly higher in the high-TMB group (spiculation: χ2 = 5.678, p = 0.017; lobulation: χ2 = 4.567, p = 0.033; vascular convergence: χ2 = 4.789, p = 0.029). pleural indentation showed a borderline intergroup difference (χ2 = 3.289, p = 0.07). Spearman correlation analysis revealed positive correlations between TMB levels and maximum tumor diameter, spiculation, lobulation, and vascular convergence (ρ = 0.312, 0.234, 0.198, 0.216; p < 0.05). Univariate logistic regression identified these features as significant predictors of high TMB (Wald = 11.678, 5.672, 4.543, 4.752; p < 0.05), and multivariate analysis confirmed their independent predictive value (Wald = 10.175, 5.231, 4.134, 4.365; p < 0.05). In diagnostic performance evaluation, a combined model of these features achieved an area under the curve (AUC) of 0.829 for predicting high TMB.ConclusionCT-based radiological features are significantly correlated with TMB status in lung adenocarcinoma. A composite model incorporating these features demonstrates high diagnostic accuracy for identifying high TMB, offering a valuable non-invasive tool for guiding personalized treatment strategies.

Nowadays, 3D printing technologies are among the rapidly developing technologies applied to manufacture even the most geometrically complex models, however no techniques dominate in the area of craniofacial applications. This study included 12 different anatomical structures of the mandible, which were obtained during the process of reconstructing data from the Siemens Somatom Sensation Open 40 system. The manufacturing process used for the 12 structures involved the use of 8 3D printers and 12 different polymer materials. Verification of the accuracy and radiological density was performed with the CT160Xi Benchtop tomography system. The most accurate results were obtained in the case of models manufactured using the following materials: E-Model (Standard Deviation (SD) = 0.145 mm), FullCure 830 (SD = 0.188 mm), VeroClear (SD = 0.128 mm), Digital ABS-Ivory (SD = 0.117 mm), and E-Partial (SD = 0.129 mm). In the case of radiological density, ABS-M30 was similar to spongious bone, PC-10 was similar to the liver, and Polylactic acid (PLA) and Polyethylene terephthalate (PET) were similar to the spleen. Acrylic resin materials were able to imitate the pancreas, kidney, brain, and heart. The presented results constitute valuable guidelines that may improve currently used radiological phantoms and may provide support to surgeons in the process of performing more precise treatments within the mandible area.

Objectives To investigate whether encouraging authors to follow the Standards for Reporting Diagnostic Accuracy (STARD) guidelines improves the quality of reporting of diagnostic accuracy studies. Methods In mid-2017, European Radiology started encouraging its authors to follow the STARD guidelines. Our MEDLINE search identified 114 diagnostic accuracy studies published in European Radiology in 2015 and 2019. The quality of reporting was evaluated by two independent reviewers using the revised STARD statement. Item 11 was excluded because a meaningful decision about adherence was not possible. Student’s t test for independent samples was used to analyze differences in the mean number of reported STARD items between studies published in 2015 and in 2019. In addition, we calculated differences related to the study design, data collection, and citation rate. Results The mean total number of reported STARD items for all 114 diagnostic accuracy studies analyzed was 15.9 ± 2.6 (54.8%) of 29 items (range 9.5–22.5). The quality of reporting of diagnostic accuracy studies was significantly better in 2019 (mean ± standard deviation (SD), 16.3 ± 2.7) than in 2015 (mean ± SD, 15.1 ± 2.3; p < 0.02). No significant differences in the reported STARD items were identified in relation to study design ( p = 0.13), data collection ( p = 0.87), and citation rate ( p = 0.09). Conclusion The quality of reporting of diagnostic accuracy studies according to the STARD statement was moderate with a slight improvement since European Radiology started to recommend its authors to follow the STARD guidelines. Key Points • The quality of reporting of diagnostic accuracy studies was moderate with a mean total number of reported STARD items of 15.9 ± 2.6. • The adherence to STARD was significantly better in 2019 than in 2015 (16.3 ± 2.7 vs. 15.1 ± 2.3; p = 0.016). • No significant differences in the reported STARD items were identified in relation to study design (p = 0.13), data collection (p = 0.87), and citation rate (p = 0.09).

Background and purpose National quality registries for stroke care operate under the assumption that the included patients are correctly diagnosed. We aimed to validate the clinical diagnosis of spontaneous intracerebral hemorrhage (ICH) in Riksstroke (RS) by evaluating radiological data from a large, unselected ICH population. Methods We conducted a retrospective, multicenter study including all ICH patients registered in RS between 2016 and 2020 residing in Skåne County in Sweden (1.41 million inhabitants). Radiological data from first imaging were evaluated for the presence of spontaneous ICH. Other types of bleeds were registered if a spontaneous ICH was not identified on imaging. The radiological evaluation was independently performed by one radiology fellow and one senior neuroradiologist. Results Between 2016 and 2020, 1784 ICH cases were registered in RS, of which 1655 (92.8%) had a radiological diagnosis consistent with spontaneous ICH. In the 129 (7.2%) remaining cases, the radiological diagnosis was instead traumatic bleed (n = 80), subarachnoid hemorrhage (n = 15), brain tumor bleed (n = 14), ischemic lesion with hemorrhagic transformation (n = 14), ischemic lesion (n = 3), or no bleed at all (n = 3). There was a higher degree of incorrect coding in the older age groups. Conclusion At radiological evaluation, 92.8% of ICH diagnoses in RS were consistent with spontaneous ICH, yielding a high rate of agreement that strengthens the validity of the diagnostic accuracy in the register, justifying the use of high coverage quality register data for epidemiological purposes. The most common coding error was traumatic bleeds that were classified as spontaneous ICH.

Objectives To explore the potential impact of a dedicated virtual training course on MRI staging confidence and performance in rectal cancer. Methods Forty-two radiologists completed a stepwise virtual training course on rectal cancer MRI staging composed of a pre-course (baseline) test with 7 test cases (5 staging, 2 restaging), a 1-day online workshop, 1 month of individual case readings ( n  = 70 cases with online feedback), a live online feedback session supervised by two expert faculty members, and a post-course test. The ESGAR structured reporting templates for (re)staging were used throughout the course. Results of the pre-course and post-course test were compared in terms of group interobserver agreement (Krippendorf’s alpha), staging confidence (perceived staging difficulty), and diagnostic accuracy (using an expert reference standard). Results Though results were largely not statistically significant, the majority of staging variables showed a mild increase in diagnostic accuracy after the course, ranging between + 2% and + 17%. A similar trend was observed for IOA which improved for nearly all variables when comparing the pre- and post-course. There was a significant decrease in the perceived difficulty level ( p  = 0.03), indicating an improved diagnostic confidence after completion of the course. Conclusions Though exploratory in nature, our study results suggest that use of a dedicated virtual training course and web platform has potential to enhance staging performance, confidence, and interobserver agreement to assess rectal cancer on MRI virtual training and could thus be a good alternative (or addition) to in-person training. Clinical relevance statement Rectal cancer MRI reporting quality is highly dependent on radiologists’ expertise, stressing the need for dedicated training/teaching. This study shows promising results for a virtual web-based training program, which could be a good alternative (or addition) to in-person training. Key Points • Rectal cancer MRI reporting quality is highly dependent on radiologists’ expertise, stressing the need for dedicated training and teaching. • Using a dedicated virtual training course and web-based platform, encouraging first results were achieved to improve staging accuracy, diagnostic confidence, and interobserver agreement. • These exploratory results suggest that virtual training could thus be a good alternative (or addition) to in-person training.

Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning model for detecting and diagnosing prostate cancer using clinical and radiological data. The dataset consists of 200 patients with prostate cancer and 200 healthy controls and extracted features from their clinical and radiological data. Then, the data trained and evaluated using several machines learning models, including logistic Regression, decision tree, random forest, support vector machine, and neural network models, using 10-fold cross-validation. Our results show that the random forest model achieved the highest accuracy of 0.92, with a sensitivity of 0.95 and a specificity of 0.89. The decision tree model achieved a nearly similar accuracy of 0.91, while the logistic regression, support vector machine, and neural network models achieved lower accuracies of 0.86, 0.87, and 0.88, respectively. Our findings suggest that machine learning models can effectively detect and diagnose prostate cancer using clinical and radiological data. The random forest model may be the most suitable model for this task.

The anatomy of the craniovertebral junction (CVJ) varies considerably across populations, yet comprehensive Turkish-specific morphometric data remain limited. We aim to establish normative CVJ measurements in Turkish males and females using computed tomography (CT). Retrospective morphological study. A retrospective analysis of CT images from 500 patients (250 females and 250 males, aged 25-40 years) was conducted between January and December 2022. CVJ measurements were obtained, and sex-related differences were assessed. The mean atlantodental interval was 1.45 ± 0.01 mm, posterior atlantodental interval 19.35 ± 0.09 mm, McGregor line 79.78 ± 0.21 mm, Chamberlain line 76.95 ± 1.21 mm, McRae line 36.27 ± 0.12 mm, Wachenheim clivus-canal angle 154.65 ± 0.45 , sphenoid angle 121.15 ± 0.39 (no sex difference, p = 0.083), Welcher basal angle 130.02 ± 0.28 (higher in females, p < 0.001), basion-axial interval 7.13 ± 0.07 mm (higher in males, p = 0.011), basion-dental interval 5.32 ± 0.06 mm, and craniocervical tilt angle 123.94 ± 0.44 (higher in males, p < 0.001). This study provides a comprehensive CT-based analysis of CVJ measurements in Turkish adults, establishes normative morphometric values, and shows that most parameters exhibit sex-based differences. These population- and sex-specific reference data may be crucial for improving the accuracy of clinical assessments and surgical planning.

Errors in radiologic interpretation are largely the result of failures of perception. This remains true despite the increasing use of computer-aided detection and diagnosis. We surveyed the literature on visual illusions during the viewing of radiologic images. Misperception of anatomical structures is a potential cause of error that can lead to patient harm if disease is seen when none is present. However, visual illusions can also help enhance the ability of radiologists to detect and characterize abnormalities. Indeed, radiologists have learned to exploit certain perceptual biases in diagnostic findings and as training tools. We propose that further detailed study of radiologic illusions would help clarify the mechanisms underlying radiologic performance and provide additional heuristics to improve radiologist training and reduce medical error.