Pituitary adenomas, ranging from subtle microadenomas to mass-effect macroadenomas, pose diagnostic challenges for radiologists due to increasing scan volumes and the complexity of dynamic contrast-enhanced MRI interpretation. A hybrid CNN-LSTM model was trained and validated on a multi-center dataset of 2,163 samples from Tehran and Babolsar, Iran. Transfer learning and preprocessing techniques (e.g., Wiener filters) were utilized to improve classification performance for microadenomas (< 10 mm) and macroadenomas (> 10 mm). The model achieved 90.5% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.92, and 89.6% sensitivity (93.5% for microadenomas, 88.3% for macroadenomas), outperforming standard CNNs by 5-18% across metrics. With a processing time of 0.17 s per scan, the model demonstrated robustness to variations in imaging conditions, including scanner differences and contrast variations, excelling in real-time detection and differentiation of adenoma subtypes. This dual-path approach, the first to synergize spatial and temporal MRI features for pituitary diagnostics, offers high precision and efficiency. Supported by comparisons with existing models, it provides a scalable, reproducible tool to improve patient outcomes, with potential adaptability to broader neuroimaging challenges.

This study aimed to develop and validate deep learning models using [¹⁸F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [¹⁸F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [¹⁸F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.

Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, MRI-based advanced biomarkers like total cyst number (TCN) and cyst parenchyma surface area (CPSA) have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study is to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in CT images. Abdominal CTs of ADPKD patients were gathered retrospectively between 2001-2018. A 3D deep-learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the non-cystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in non-contrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Two-hundred CT scans from 148 patients (mean age, 51.2 ± 14.1 years; 48% male) were utilized for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model vs reader: TCNkidney/liver r=0.96/0.97 and CPSAkidney r=0.98), inter-reader: TCNkidney/liver r=0.96/0.98 and CPSAkidney r=0.99). Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

This study aimed to develop and validate a deep learning (DL) model to detect obstructive coronary artery disease (CAD, ≥ 50% stenosis) in coronary CT angiography (CCTA) among patients presenting to the emergency department (ED) with acute chest pain. The training dataset included 378 patients with acute chest pain who underwent CCTA (10,060 curved multiplanar reconstruction [MPR] images) from a single-center ED between January 2015 and December 2022. The external validation dataset included 298 patients from 3 ED centers between January 2021 and December 2022. A DL model based on You Only Look Once v4, requires manual preprocessing for curved MPR extraction and was developed using 15 manually preprocessed MPR images per major coronary artery. Model performance was evaluated per artery and per patient. The training dataset included 378 patients (mean age 61.3 ± 12.2 years, 58.2% men); the external dataset included 298 patients (mean age 58.3 ± 13.8 years, 54.6% men). Obstructive CAD prevalence in the external dataset was 27.5% (82/298). The DL model achieved per-artery sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC) of 92.7%, 89.9%, 62.6%, 98.5%, and 0.919, respectively; and per-patient values of 93.3%, 80.7%, 67.7%, 96.6%, and 0.871, respectively. The DL model demonstrated high sensitivity and NPV for identifying obstructive CAD in patients with acute chest pain undergoing CCTA, indicating its potential utility in aiding ED physicians in CAD detection.

Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.

Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them. A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.

Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language models (LLMs) have enabled visual question answering (VQA) approaches that integrate image interpretation with natural language reasoning. In this study, we evaluated GPT-4o, GPT-5-nano, GPT-5-mini, and GPT-5 on a curated brain tumor VQA benchmark derived from 3 Brain Tumor Segmentation (BraTS) datasets - glioblastoma (GLI), meningioma (MEN), and brain metastases (MET). Each case included multi-sequence MRI triplanar mosaics and structured clinical features transformed into standardized VQA items. Models were assessed in a zero-shot chain-of-thought setting for accuracy on both visual and reasoning tasks. Results showed that GPT-5-mini achieved the highest macro-average accuracy (44.19%), followed by GPT-5 (43.71%), GPT-4o (41.49%), and GPT-5-nano (35.85%). Performance varied by tumor subtype, with no single model dominating across all cohorts. These findings suggest that GPT-5 family models can achieve moderate accuracy in structured neuro-oncological VQA tasks, but not at a level acceptable for clinical use.

The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on developing Explainable Artificial Intelligence (XAI) models that answer clinically relevant questions based on GI endoscopy images while providing interpretable justifications aligned with medical reasoning. It introduces two subtasks: (1) answering diverse types of visual questions using the Kvasir-VQA-x1 dataset, and (2) generating multimodal explanations to support clinical decision-making. The Kvasir-VQA-x1 dataset, created from 6,500 images and 159,549 complex question-answer (QA) pairs, serves as the benchmark for the challenge. By combining quantitative performance metrics and expert-reviewed explainability assessments, this task aims to advance trustworthy Artificial Intelligence (AI) in medical image analysis. Instructions, data access, and an updated guide for participation are available in the official competition repository: https://github.com/simula/MediaEval-Medico-2025

Soft X-ray tomography (SXT) provides detailed structural insight into whole cells but is hindered by experimental artifacts such as the missing wedge and by limited availability of annotated datasets. We present \method, a simulation pipeline that generates realistic cellular phantoms and applies synthetic artifacts to produce paired noisy volumes, sinograms, and reconstructions. We validate our approach by training a neural network primarily on synthetic data and demonstrate effective few-shot and zero-shot transfer learning on real SXT tomograms. Our model delivers accurate segmentations, enabling quantitative analysis of noisy tomograms without relying on large labeled datasets or complex reconstruction methods.

Dark-field radiography is a novel X-ray imaging modality that enables complementary diagnostic information by visualizing the microstructural properties of lung tissue. Implemented via a Talbot-Lau interferometer integrated into a conventional X-ray system, it allows simultaneous acquisition of perfectly temporally and spatially registered attenuation-based conventional and dark-field radiographs. Recent clinical studies have demonstrated that dark-field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases. However, the polychromatic nature of medical X-ray sources leads to beam-hardening, which introduces structured artifacts in the dark-field radiographs, particularly from osseous structures. This so-called beam-hardening-induced dark-field signal is an artificial dark-field signal and causes undesired cross-talk between attenuation and dark-field channels. This work presents a segmentation-based beam-hardening correction method using deep learning to segment ribs and clavicles. Attenuation contribution masks derived from dual-layer detector computed tomography data, decomposed into aluminum and water, were used to refine the material distribution estimation. The method was evaluated both qualitatively and quantitatively on clinical data from healthy subjects and patients with chronic obstructive pulmonary disease and COVID-19. The proposed approach reduces bone-induced artifacts and improves the homogeneity of the lung dark-field signal, supporting more reliable visual and quantitative assessment in clinical dark-field chest radiography.