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With the development of genome sequencing, the role of molecular targeted therapy in cancer is becoming increasingly important. However, genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. Radiogenomics aims to correlate imaging characteristics with gene expression patterns, gene mutations, and other genome-related characteristics. Due to the noninvasive nature of medical imaging, the field of radiogenomics is rapidly developing and may serve as a substitute tool for genetic testing. In this article, we briefly summarise the current role of radiogenomics in predicting gene mutations in brain, lung, colorectal, breast, and kidney tumours. Key Points • The role of molecular targeted therapy in individual cancer-precision therapy is becoming increasingly important with the development of genetic testing. • Radiogenomics may provide accurate imaging biomarkers as a substitute for genetic testing. • While the field of radiogenomics holds great promise, there are still a number of limitations that need to be overcome.

Objectives To assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors. Methods Adult patients diagnosed with renal sarcomas from 1995 to 2018 were included from 11 European tertiary referral centers (Germany, Belgium, Turkey). Renal sarcomas were 1:4 compared to patients with non-sarcoma renal tumors. CT/MRI findings were assessed using 21 predefined imaging features. A random forest model was trained to predict “renal sarcoma vs. non-sarcoma renal tumors” based on demographics and imaging features. Results n = 34 renal sarcomas were included and compared to n = 136 non-sarcoma renal tumors. Renal sarcomas manifested in younger patients (median 55 vs. 67 years, p < 0.01) and were more complex (high RENAL score complexity 79.4% vs. 25.7%, p < 0.01). Renal sarcomas were larger (median diameter 108 vs. 43 mm, p < 0.01) with irregular shape and ill-defined margins, and more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein, compared to non-sarcoma renal tumors ( p < 0.05, each). The random forest algorithm yielded a median AUC = 93.8% to predict renal sarcoma histology, with sensitivity, specificity, and positive predictive value of 90.4%, 76.5%, and 93.9%, respectively. Tumor diameter and RENAL score were the most relevant imaging features for renal sarcoma identification. Conclusion Renal sarcomas are rare tumors commonly manifesting as large masses in young patients. A random forest model using demographics and imaging features shows good diagnostic accuracy for discrimination of renal sarcomas from non-sarcoma renal tumors, which might aid in clinical decision-making. Key Points • Renal sarcomas commonly manifest in younger patients as large, complex renal masses. • Compared to non-sarcoma renal tumors, renal sarcomas more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein. • Using demographics and standardized imaging features, a random forest showed excellent diagnostic performance for discrimination of sarcoma vs. non-sarcoma renal tumors (AUC = 93.8%, sensitivity = 90.4%, specificity = 76.5%, and PPV = 93.9%).

Finite-element modeling (FEM) is a powerful tool for studying the mechanical behavior of the human ear. This study presents a three-dimensional FEM of the middle and inner ear, developed from high-resolution computed tomography (CT) data. The model includes the external auditory canal, tympanic membrane, and ossicular chain, with the inner ear represented as a viscous damping element. Frequency-domain simulations were performed to analyze tympanic membrane and stapes vibrations. Results show strong agreement with experimental data, demonstrating that the proposed FEM accurately reproduces middle ear dynamics across a wide frequency range. The methodology provides a reliable framework for investigating auditory mechanics and designing biomedical devices.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive “sick-care” approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification.Critical relevance statementThis paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain.Key PointsΑΙ models for health imaging require access to large amounts of data.Access to large imaging datasets requires an appropriate de-identification process.This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

In this study, we aimed to investigate the factors affecting spontaneous recovery in cases of acute binocular diplopia. A total of 224 patients presenting with acute binocular diplopia within 7 days were included in this study. The age, gender, etiology, and radiological findings of the cases were retrospectively examined and noted. The status of diplopia in the 6 month was noted. The most commonly identified causes were presumed microvascular (28%), cerebrovascular (14%), and autoimmune-inflammatory (14%) in origin. Spontaneous recovery in diplopia was observed in 153 cases (68.3%) at 6 months. While the recovery rate was high in the presumed microvascular and idiopathic groups, it was low in the neoplastic group. Cranial nerve palsy was detected in 132 patients (58.9%). The most common were 6 , 3 , and 4 nerve palsies, respectively. No difference was found in terms of spontaneous recovery at 6 months among cranial nerve palsies (p=0.952). The spontaneous recovery rate was found to be significantly higher in patients without radiological imaging findings (p<0.001). It is important for the physician to predict whether diplopia will resolve spontaneously. While the underlying etiology and neuronal damage are crucial factors, radiological imaging findings of the patients can also provide valuable insights.

ObjectiveTo investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell cancer (ESCC) who underwent radical radiotherapy using a pairwise machine learning algorithm.MethodsThe 130 datasets enrolled were randomly divided into a training set and a testing set in a 7:3 ratio. Clinical factors were included and radiomics features were extracted from pretreatment CT scans using pyradiomics-based software, and a pairwise naive Bayes (NB) model was developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To facilitate practical application, we attempted to construct an automated esophageal cancer diagnosis system based on trained models.ResultsTo the follow-up date, 64 patients (49.23%) had experienced LR. Ten radiomics features and two clinical factors were selected for modeling. The model demonstrated good prediction performance, with area under the ROC curve of 0.903 (0.829–0.958) for the training cohort and 0.944 (0.849–1.000) for the testing cohort. The corresponding accuracies were 0.852 and 0.914, respectively. Calibration curves showed good agreement, and DCA curve confirmed the clinical validity of the model. The model accurately predicted LR in elderly patients, with a positive predictive value of 85.71% for the testing cohort.ConclusionsThe pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.

Hepatocellular carcinoma (HCC) is the most common primary carcinoma arising from the liver. Although HCC can arise de novo, the vast majority of cases develop in the setting of chronic liver disease. Hepatocarcinogenesis follows a well-studied process during which chronic inflammation and cellular damage precipitate cellular and genetic aberrations, with subsequent propagation of precancerous and cancerous lesions. Surveillance of individuals at high risk of HCC, early diagnosis, and individualized treatment are keys to reducing the mortality associated with this disease. Radiological imaging plays a critical role in the diagnosis and management of these patients. HCC is a unique cancer in that it can be diagnosed with confidence by imaging that meets all radiologic criteria, obviating the risks associated with tissue sampling. This article discusses conventional and emerging imaging techniques for the evaluation of HCC.

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

Purpose This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). Methods This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen’s kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland–Altman plots were used to compare measurements. Results One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model’s assistance, trainees’ agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0–2 or 3–4. Trainees and attendings had the highest accuracy (0.95). The DL model’s performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of − 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of − 3.4 to 6.2. Conclusion While the DL model outperformed trainees in some aspects, attendings’ assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

Backgrounds Preoperative imaging, particularly with magnetic resonance imaging (MRI) and computed tomography (CT) scans, plays a crucial role in distinguishing between benign and malignant parotid gland tumors, while the reliability of Ultrasound-Guided Fine Needle Aspiration (FNA) in diagnosing these masses remains a topic of debate. Methods This two-center retrospective analysis was conducted on 347 patients with parotid gland tumors who had FNA and preoperative imaging (CT or MRI). All patients underwent surgery and final histopathological examination was available, along with complete medical records between January 2008 and May 2023. Results Among the 347 patients, 318 (92%) had benign and 10 (3%) had malignant tumors based on FNA, with 19 (5%) unsatisfactory specimens. Final histological diagnosis revealed 303 (87%) benign and 44 (13%) malignant lesions, with a false-negative rate of 10.6% for FNA. Multivariate analysis identified irregular shape and invasion as independent predictors of malignancy in patient with benign or unsatisfactory FNA results. The odds ratio for irregular shape was 3.06 and for invasion was 12.73. Conclusion Imaging characteristics, such as irregular shape and invasion may indicate towards malignant parotid tumors, even in patients with false-negative benign findings in FNA.