Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a machine learning (ML) approach using pre-treatment CT angiography (CTA) to identify this cohort of patients that may benefit from additional interventions. We identified consecutive large vessel occlusion (LVO) acute ischemic stroke (AIS) subjects who underwent EST with successful reperfusion in a multicenter prospective registry cohort. We included only subjects with FIV<30mL and recorded 90-day outcome (modified Rankin scale, mRS). A deep learning model was pre-trained and then fine-tuned to predict 90-day mRS 0-2 using pre-treatment CTA images (DSN-CTA model). The primary outcome was the predictive performance of the DSNCTA model compared to a logistic regression model with clinical variables, measured by the area under the receiver operating characteristic curve (AUROC). The DSN-CTA model was pre-trained on 1,542 subjects and then fine-tuned and cross-validated with 48 subjects, all of whom underwent EST with TICI 2b-3 reperfusion. Of this cohort, 56.2% of subjects had 90-day mRS 3-6 despite successful EST and FIV<30mL. The DSN-CTA model showed significantly better performance than a model with clinical variables alone when predicting good 90-day mRS (AUROC 0.81 vs 0.492, p=0.006). The CTA-based machine learning model was able to more reliably predict unexpected poor functional outcome after successful EST and small FIV for patients with LVO AIS compared to standard clinical variables. ML models may identify <i>a priori</i> patients in whom EST-based LVO reperfusion alone is insufficient to improve clinical outcomes. AIS＝ acute ischemic stroke; AUROC＝ area under the receiver operating characteristic curve; DSN-CTA＝ DeepSymNet-v3 model; EST＝ endovascular stroke therapy; FIV＝ final infarct volume; LVO＝ large vessel occlusion; ML＝ machine learning.

Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3-8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.

Deep learning (DL) models have shown promise in predicting axillary lymph node (ALN) status. However, most existing DL models were classification-only models and did not consider the practical application scenarios of multi-view joint prediction. Here, we propose a Multi-Task Learning (MTL) and Multi-Instance Learning (MIL) framework that simulates the real-world clinical diagnostic scenario for ALN status prediction in breast cancer. Ultrasound images of the primary tumor and ALN (if available) regions were collected, each annotated with a segmentation label. The model was trained on a training cohort and tested on both internal and external test cohorts. The proposed two-stage DL framework using one of the Transformer models, Segformer, as the network backbone, exhibits the top-performing model. It achieved an AUC of 0.832, a sensitivity of 0.815, and a specificity of 0.854 in the internal test cohort. In the external cohort, this model attained an AUC of 0.918, a sensitivity of 0.851 and a specificity of 0.957. The Class Activation Mapping method demonstrated that the DL model correctly identified the characteristic areas of metastasis within the primary tumor and ALN regions. This framework may serve as an effective second reader to assist clinicians in ALN status assessment.

To assess the methodological quality of radiomics-based studies on bone chondrosarcoma using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS). A literature search was conducted on EMBASE and PubMed databases for research papers published up to July 2024 and focused on radiomics in bone chondrosarcoma, with no restrictions regarding the study aim. Three readers independently evaluated the study quality using METRICS and RQS. Baseline study characteristics were extracted. Inter-reader reliability was calculated using intraclass correlation coefficient (ICC). Out of 68 identified papers, 18 were finally included in the analysis. Radiomics research was aimed at lesion classification (n = 15), outcome prediction (n = 2) or both (n = 1). Study design was retrospective in all papers. Most studies employed MRI (n = 12), CT (n = 3) or both (n = 1). METRICS and RQS adherence rates ranged between 37.3-94.8% and 2.8-44.4%, respectively. Excellent inter-reader reliability was found for both METRICS (ICC = 0.961) and RQS (ICC = 0.975). Among the limitations of the evaluated studies, the absence of prospective studies and deep learning-based analyses was highlighted, along with the limited adherence to radiomics guidelines, use of external testing datasets and open science data. METRICS and RQS are reproducible quality assessment tools, with the former showing higher adherence rates in studies on chondrosarcoma. METRICS is better suited for assessing papers with retrospective design, which is often chosen in musculoskeletal oncology due to the low prevalence of bone sarcomas. Employing quality scoring systems should be promoted in radiomics-based studies to improve methodological quality and facilitate clinical translation. Employing reproducible quality scoring systems, especially METRICS (which shows higher adherence rates than RQS and is better suited for assessing retrospective investigations), is highly recommended to design radiomics-based studies on chondrosarcoma, improve methodological quality and facilitate clinical translation. The low scientific and reporting quality of radiomics studies on chondrosarcoma is the main reason preventing clinical translation. Quality appraisal using METRICS and RQS showed 37.3-94.8% and 2.8-44.4% adherence rates, respectively. Room for improvement was noted in study design, deep learning methods, external testing and open science. Employing reproducible quality scoring systems is recommended to design radiomics studies on bone chondrosarcoma and facilitate clinical translation.

Brain tumor classification using Magnetic Resonance Imaging (MRI) images is an important and emerging field of medical imaging and artificial intelligence in the current world. With advancements in technology, particularly in deep learning and machine learning, researchers and clinicians are leveraging these tools to create complex models that, using MRI data, can reliably detect and classify tumors in the brain. However, it has a number of drawbacks, including the intricacy of tumor types and grades, intensity variations in MRI data and tumors varying in severity. This paper proposes a Multi-Grade Hierarchical Classification Network Model (MGHCN) for the hierarchical classification of tumor grades in MRI images. The model's distinctive feature lies in its ability to categorize tumors into multiple grades, thereby capturing the hierarchical nature of tumor severity. To address variations in intensity levels across different MRI samples, an Improved Adaptive Intensity Normalization (IAIN) pre-processing step is employed. This step standardizes intensity values, effectively mitigating the impact of intensity variations and ensuring more consistent analyses. The model renders utilization of the Dual Tree Complex Wavelet Transform with Enhanced Trigonometric Features (DTCWT-ETF) for efficient feature extraction. DTCWT-ETF captures both spatial and frequency characteristics, allowing the model to distinguish between different tumor types more effectively. In the classification stage, the framework introduces the Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network (AHOHH-BiLSTM). This multi-grade classification model is designed with a comprehensive architecture, including distinct layers that enhance the learning process and adaptively refine parameters. The purpose of this study is to improve the precision of distinguishing different grades of tumors in MRI images. To evaluate the proposed MGHCN framework, a set of evaluation metrics is incorporated which includes precision, recall, and the F1-score. The structure employs BraTS Challenge 2021, Br35H, and BraTS Challenge 2023 datasets, a significant combination that ensures comprehensive training and evaluation. The MGHCN framework aims to enhance brain tumor classification in MRI images by utilizing these datasets along with a comprehensive set of evaluation metrics, providing a more thorough and sophisticated understanding of its capabilities and performance.

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes. $\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome. $\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery. $\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.

This study aimed to compare three publicly available deep learning models (TotalSegmentator, TotalVibeSegmentator, and PanSegNet) for automated pancreatic segmentation on magnetic resonance images and to evaluate their performance against human annotations in terms of segmentation accuracy, volumetric measurement, and intrapancreatic fat fraction (IPFF) assessment. Twenty upper abdominal T1-weighted magnetic resonance series acquired using the two-point Dixon method were randomly selected. Three radiologists manually segmented the pancreas, and a ground-truth mask was constructed through a majority vote per voxel. Pancreatic segmentation was also performed using the three artificial intelligence models. Performance was evaluated using the Dice similarity coefficient (DSC), 95th-percentile Hausdorff distance, average symmetric surface distance, positive predictive value, sensitivity, Bland-Altman plots, and concordance correlation coefficient (CCC) for pancreatic volume and IPFF. PanSegNet achieved the highest DSC (mean ± standard deviation, 0.883 ± 0.095) and showed no statistically significant difference from the human interobserver DSC (0.896 ± 0.068; p = 0.24). In contrast, TotalVibeSegmentator (0.731 ± 0.105) and TotalSegmentator (0.707 ± 0.142) had significantly lower DSC values compared with the human interobserver average (p < 0.001). For pancreatic volume and IPFF, PanSegNet demonstrated the best agreement with the ground truth (CCC values of 0.958 and 0.993, respectively), followed by TotalSegmentator (0.834 and 0.980) and TotalVibeSegmentator (0.720 and 0.672). PanSegNet demonstrated the highest segmentation accuracy and the best agreement with human measurements for both pancreatic volume and IPFF on T1-weighted Dixon images. This model appears to be the most suitable for large-scale studies requiring automated pancreatic segmentation and intrapancreatic fat evaluation.

Popliteal sciatic nerve block is a widely used technique for lower limb anesthesia. However, despite ultrasound guidance, the complex anatomical structures of the popliteal fossa can present challenges, potentially leading to complications. To accurately identify the bifurcation of the sciatic nerve for nerve blockade, we propose MDEANet, a deep learning-based segmentation network designed for the precise localization of nerves, muscles, and arteries in ultrasound images of the popliteal region. MDEANet incorporates Cascaded Multi-scale Atrous Convolutions (CMAC) to enhance multi-scale feature extraction, Enhanced Spatial Attention Mechanism (ESAM) to focus on key anatomical regions, and Cross-level Feature Fusion (CLFF) to improve contextual representation. This integration markedly improves segmentation of nerves, muscles, and arteries. Experimental results demonstrate that MDEANet achieves an average Intersection over Union (IoU) of 88.60% and a Dice coefficient of 93.95% across all target structures, outperforming state-of-the-art models by 1.68% in IoU and 1.66% in Dice coefficient. Specifically, for nerve segmentation, the Dice coefficient reaches 93.31%, underscoring the effectiveness of our approach. MDEANet has the potential to provide decision-support assistance for anesthesiologists, thereby enhancing the accuracy and efficiency of ultrasound-guided nerve blockade procedures.

Deformable image registration is a fundamental task in medical image analysis, which is crucial in enabling early detection and accurate disease diagnosis. Although transformer-based architectures have demonstrated strong potential through attention mechanisms, challenges remain in ineffective feature extraction and spatial alignment, particularly within hierarchical attention frameworks. To address these limitations, we propose a novel registration framework that integrates hierarchical feature encoding in the encoder and an adaptive cascaded refinement strategy in the decoder. The model employs hierarchical cross-attention between fixed and moving images at multiple scales, enabling more precise alignment and improved registration accuracy. The decoder incorporates the Adaptive Cascaded Module (ACM), facilitating progressive deformation field refinement across multiple resolution levels. This approach captures coarse global transformations and acceptable local variations, resulting in smooth and anatomically consistent alignment. However, rather than relying solely on the final decoder output, our framework leverages intermediate representations at each stage of the network, enhancing the robustness and precision of the registration process. Our method achieves superior accuracy and adaptability by integrating deformations across all scales. Comprehensive experiments on two widely used 3D brain MRI datasets, OASIS and LPBA40, demonstrate that the proposed framework consistently outperforms existing state-of-the-art approaches across multiple evaluation metrics regarding accuracy, robustness, and generalizability.

Recent advancements in diffusion probabilistic models (DPMs) have revolutionized image processing, demonstrating significant potential in medical applications. Accurate segmentation of the left ventricle (LV) in echocardiograms is crucial for diagnostic procedures and necessary treatments. However, ultrasound images are notoriously noisy with low contrast and ambiguous LV boundaries, thereby complicating the segmentation process. To address these challenges, this paper introduces Echo-DND, a novel dual-noise diffusion model specifically designed for this task. Echo-DND leverages a unique combination of Gaussian and Bernoulli noises. It also incorporates a multi-scale fusion conditioning module to improve segmentation precision. Furthermore, it utilizes spatial coherence calibration to maintain spatial integrity in segmentation masks. The model's performance was rigorously validated on the CAMUS and EchoNet-Dynamic datasets. Extensive evaluations demonstrate that the proposed framework outperforms existing SOTA models. It achieves high Dice scores of 0.962 and 0.939 on these datasets, respectively. The proposed Echo-DND model establishes a new standard in echocardiogram segmentation, and its architecture holds promise for broader applicability in other medical imaging tasks, potentially improving diagnostic accuracy across various medical domains. Project page: https://abdur75648.github.io/Echo-DND