Differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) is essential for selecting the most effective treatment strategies. However, traditional imaging modalities and serum biomarkers often lack sufficient specificity. Radiomics, a sophisticated image analysis approach that derives quantitative data from medical imaging, has emerged as a promising non-invasive tool. To systematically review and meta-analyze the radiomics diagnostic accuracy in differentiating ICC from HCC. PubMed, EMBASE, and Web of Science databases were systematically searched through January 24, 2025. Studies evaluating radiomics models for distinguishing ICC from HCC were included. Assessing the quality of included studies was done by using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and METhodological RadiomICs Score tools. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using a bivariate random-effects model. Subgroup and publication bias analyses were also performed. 12 studies with 2541 patients were included, with 14 validation cohorts entered into meta-analysis. The pooled sensitivity and specificity of radiomics models were 0.82 (95% CI: 0.76-0.86) and 0.90 (95% CI: 0.85-0.93), respectively, with an AUC of 0.88 (95% CI: 0.85-0.91). Subgroup analyses revealed variations based on segmentation method, software used, and sample size, though not all differences were statistically significant. Publication bias was not detected. Radiomics demonstrates high diagnostic accuracy in distinguishing ICC from HCC and offers a non-invasive adjunct to conventional diagnostics. Further prospective, multicenter studies with standardized workflows are needed to enhance clinical applicability and reproducibility.

Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with pancreatic cancer, particularly those with borderline resectable pancreatic cancer (BRPC). Although body composition can be extracted from routine CT imaging, sarcopenia assessment remains underused in clinical practice. Recent advances in artificial intelligence (AI) offer the potential to automate and standardize this process, but their clinical translation remains limited. This narrative review aims to critically evaluate (1) the clinical impact of CT-defined sarcopenia in BRPC, and (2) the performance and maturity of AI-based methods for automated muscle and fat segmentation on CT images. A dual-axis literature search was conducted to identify clinical studies assessing the prognostic role of sarcopenia in BRPC, and technical studies developing AI-based segmentation models for body composition analysis. Structured data extraction was applied to 13 clinical and 71 technical studies. A PRISMA-inspired flow diagram was included to ensure methodological transparency. Sarcopenia was consistently associated with worse survival and treatment tolerance in BRPC, yet clinical definitions and cut-offs varied widely. AI models-mostly 2D U-Nets trained on L3-level CT slices-achieved high segmentation accuracy (mean DSC >0.93), but external validation and standardization were often lacking. CT-based AI assessment of sarcopenia holds promise for improving patient stratification in BRPC. However, its clinical adoption will require standardization, integration into decision-support frameworks, and prospective validation across diverse populations.

Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links, channel-attention, and spatial attention, together with edge-detection based skip-connections for an accurate fully-automated semantic segmentation of cardiac magnetic resonance (CMR) images. The model extracts local-features and their interrelationships using a stack of combination convolution blocks, with embedded channel and spatial attention in the convolution block, and vision transformers. Deep embedding of channel and spatial attention in the convolution block identifies important features and their spatial localization. The combined edge information with channel and spatial attention as skip connection reduces information-loss during convolution transformations. The overall model significantly improves the semantic segmentation of CMR images necessary for improved medical image analysis. An algorithm for the dual attention module (channel and spatial attention) has been presented. Performance results show that U-R-Veda achieves an average accuracy of 95.2%, based on DSC metrics. The model outperforms the accuracy attained by other models, based on DSC and HD metrics, especially for the delineation of right-ventricle and left-ventricle-myocardium.

Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus derived from DaT-single-photon emission computed tomography (SPECT) images are being investigated as biomarkers to diagnose, assess disease status, and track the progression of Parkinsonism. Reliable quantification from DaT-SPECT images requires performing attenuation compensation (AC), typically with a separate X-ray CT scan. Such CT-based AC (CTAC) has multiple challenges, a key one being the non-availability of X-ray CT component on many clinical SPECT systems. Even when a CT is available, the additional CT scan leads to increased radiation dose, costs, and complexity, potential quantification errors due to SPECT-CT misalignment, and higher training and regulatory requirements. To overcome the challenges with the requirement of a CT scan for AC in DaT SPECT, we propose a deep learning (DL)-based transmission-less AC method for DaT-SPECT (DaT-CTLESS). An in silico imaging trial, titled ISIT-DaT, was designed to evaluate the performance of DaT-CTLESS on the regional uptake quantification task. We observed that DaT-CTLESS yielded a significantly higher correlation with CTAC than that between UAC and CTAC on the regional DaT uptake quantification task. Further, DaT-CLTESS had an excellent agreement with CTAC on this task, significantly outperformed UAC in distinguishing patients with normal versus reduced putamen SBR, yielded good generalizability across two scanners, was generally insensitive to intra-regional uptake heterogeneity, demonstrated good repeatability, exhibited robust performance even as the size of the training data was reduced, and generally outperformed the other considered DL methods on the task of quantifying regional uptake across different training dataset sizes. These results provide a strong motivation for further clinical evaluation of DaT-CTLESS.

Elucidating the biomechanical behavior of the myocardium is crucial for understanding cardiac physiology, but cannot be directly inferred from clinical imaging and typically requires finite element (FE) simulations. However, conventional FE methods are computationally expensive and often fail to reproduce observed cardiac motions. We propose IMC-PINN-FE, a physics-informed neural network (PINN) framework that integrates imaged motion consistency (IMC) with FE modeling for patient-specific left ventricular (LV) biomechanics. Cardiac motion is first estimated from MRI or echocardiography using either a pre-trained attention-based network or an unsupervised cyclic-regularized network, followed by extraction of motion modes. IMC-PINN-FE then rapidly estimates myocardial stiffness and active tension by fitting clinical pressure measurements, accelerating computation from hours to seconds compared to traditional inverse FE. Based on these parameters, it performs FE modeling across the cardiac cycle at 75x speedup. Through motion constraints, it matches imaged displacements more accurately, improving average Dice from 0.849 to 0.927, while preserving realistic pressure-volume behavior. IMC-PINN-FE advances previous PINN-FE models by introducing back-computation of material properties and better motion fidelity. Using motion from a single subject to reconstruct shape modes also avoids the need for large datasets and improves patient specificity. IMC-PINN-FE offers a robust and efficient approach for rapid, personalized, and image-consistent cardiac biomechanical modeling.

Low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising methods require supervised learning with paired training data that is difficult to obtain. To address this limitation, we aim to develop a denoising method that does not rely on paired normal-dose CT (NDCT) data.
Approach: We propose a self-supervised denoising method based on guided image filtering (GIF)
that requires only LDCT images for training. The method first applies GIF to generate pseudo-labels from LDCT images, enabling the network to learn noise distributions between inputs and pseudo-labels for denoising, without paired data. Then, an attention gate mechanism is embedded in the decoder stage of a residual network to further enhance denoising performance.
Main Results: Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art unsupervised denoising networks, transformer-based denoising model and post-processing methods, in terms of both visual quality and quantitative metrics. Furthermore, ablation studies are conducted to analyze the impact of different attention mechanisms and the number of attention gate mechanisms, showing that the proposed network architecture achieves optimal performance.
Significance: This work leverages self-supervised learning with GIF to generate pseudo-labels, enabling LDCT denoising without paired data. The embedded attention gate mechanism, supported by detailed ablation analysis, further enhances denoising performance by improving feature focus and structural preservation.

&#xD;Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) is a promising technique for long-course RT of large-volume brain metastasis (BM), due to the capacity to track tumor changes throughout treatment course. Contrast-enhanced T1-weighted (T1CE) MRI is essential for BM delineation, yet is often unavailable during online treatment concerning the requirement of contrast agent injection. This study aims to develop a synthetic T1CE (sT1CE) generation method to facilitate accurate online adaptive BM delineation.&#xD;Approach:&#xD;We developed a novel ControlNet-coupled latent diffusion model (CTN-LDM) combined with a personalized transfer learning strategy and a denoising diffusion implicit model (DDIM) inversion method to generate high quality sT1CE images from online T2-weighted (T2) or fluid attenuated inversion recovery (FLAIR) images. Visual quality of sT1CE images generated by the CTN-LDM was compared with classical deep learning models. BM delineation results using the combination of our sT1CE images and online T2/FLAIR images were compared with the results solely using online T2/FLAIR images, which is the current clinical method.&#xD;Main results:&#xD;Visual quality of sT1CE images from our CTN-LDM was superior to classical models both quantitatively and qualitatively. Leveraging sT1CE images, radiation oncologists achieved significant higher precision of adaptive BM delineation, with average Dice similarity coefficient of 0.93 ± 0.02 vs. 0.86 ± 0.04 (p < 0.01), compared with only using online T2/FLAIR images. &#xD;Significance:&#xD;The proposed method could generate high quality sT1CE images and significantly improve accuracy of online adaptive tumor delineation for long-course MRIgART of large-volume BM, potentially enhancing treatment outcomes and minimizing toxicity.

&#xD;Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from limited-angle projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training. This work aims to facilitate the clinical applications of fast and low-dose DE-CBCT by developing a practical solution for image reconstruction in limited-angle DE-CBCT.&#xD;Methods:&#xD;An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in limited-angle DE-CBCT. By enforcing the similarity between the DE images, limited-angle artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using two physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging.&#xD;Results:&#xD;In all the studies, the proposed method achieves accurate image reconstruction without visible residual artifacts from limited-angle DE-CBCT projection data. In the digital phantom studies, the proposed method reduces the mean-absolute-error (MAE) from 309/290 HU to 14/20 HU, increases the peak signal-to-noise ratio (PSNR) from 40/39 dB to 70/67 dB, and improves the structural similarity index measurement (SSIM) from 0.74/0.72 to 1.00/1.00.&#xD;Conclusions:&#xD;The proposed method achieves accurate optimization-based image reconstruction in limited-angle DE-CBCT, showing great practical value in clinical implementations of limited-angle DE-CBCT.&#xD.

Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival. CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. An artificial intelligence-based 3D body composition analysis (BCA) algorithm assessed muscle volume, different adipose tissue compartments, and bone mineral density. These parameters were analysed with regard to various clinical, laboratory, functional parameters and survival. Phenotypes were identified performing K-means cluster analysis. Longitudinal evaluation of BCA changes employed regression analyses. A regression model using BCA parameters outperformed models based on Body Mass Index and clinical parameters in predicting survival (area under the curve (AUC)=0.75). Longitudinal development of the cardiac marker enabled prediction of survival with an AUC=0.82. Patients with altered BCA parameters had increased ORs for various complications, including interstitial lung disease (p<0.05). Two distinct BCA phenotypes were identified, showing significant differences in gastrointestinal disease manifestations (p<0.01). This study highlights several parameters with the potential to reshape clinical pathways for SSc patients. Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters. These insights open new avenues for research into the mechanisms driving body composition changes in SSc and for developing enhanced disease management tools, ultimately leading to more personalised and effective patient care.

The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to provide a swift and accurate diagnosis in dedicated chest imaging (computed tomography pulmonary angiogram; CTPA) for suspected PE; however, the accuracy of AI algorithms in the detection of incidental PE in non-dedicated CT imaging studies remains unclear and untested. This study explores the potential for a commercial AI algorithm to identify incidental PE in non-dedicated contrast-enhanced CT chest imaging studies. The Viz PE algorithm was deployed to identify the presence of PE on 130 dedicated and 63 non-dedicated contrast-enhanced CT chest exams. The predictions for non-dedicated contrast-enhanced chest CT imaging studies were 90.48% accurate, with a sensitivity of 0.14 and specificity of 1.00. Our findings reflect that the Viz PE algorithm demonstrated an overall accuracy of 90.16%, with a specificity of 96% and a sensitivity of 41%. Although the high specificity is promising for ruling in PE, the low sensitivity highlights a limitation, as it indicates the algorithm may miss a substantial number of true-positive incidental PEs. This study demonstrates that commercial AI detection tools hold promise as integral support for detecting PE, particularly when there is a strong clinical indication for their use; however, current limitations in sensitivity, especially for incidental cases, underscore the need for ongoing radiologist oversight.