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Cardiac imaging for the detection of ischemia: current status and future perspectives.

Rodriguez C, Pappas L, Le Hong Q, Baquero L, Nagel E

pubmed logopapersMay 12 2025
Coronary artery disease is the main cause of mortality worldwide mandating early detection, appropriate treatment, and follow-up. Noninvasive cardiac imaging techniques allow detection of obstructive coronary heart disease by direct visualization of the arteries or myocardial blood flow reduction. These techniques have made remarkable progress since their introduction, achieving high diagnostic precision. This review aims at evaluating these noninvasive cardiac imaging techniques, rendering a thorough overview of diagnostic decision-making for detection of ischemia. We discuss the latest advances in the field such as computed tomography angiography, single-photon emission tomography, positron emission tomography, and cardiac magnetic resonance; their main advantages and disadvantages, their most appropriate use and prospects. For the review, we analyzed the literature from 2009 to 2024 on noninvasive cardiac imaging in the diagnosis of coronary artery disease. The review included the 78 publications considered most relevant, including landmark trials, review articles and guidelines. The progress in cardiac imaging is anticipated to overcome various limitations such as high costs, radiation exposure, artifacts, and differences in interpretation among observers. It is expected to lead to more automated scanning processes, and with the assistance of artificial intelligence-driven post-processing software, higher accuracy and reproducibility may be attained.

Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data

Badhan Kumar Das, Gengyan Zhao, Boris Mailhe, Thomas J. Re, Dorin Comaniciu, Eli Gibson, Andreas Maier

arxiv logopreprintMay 12 2025
Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisitions with varying contrasts and orientation introduce complexity in identifying hemorrhage using neural networks. For acquisitions with varying orientations, traditional methods often involve resampling images to a fixed plane, which can lead to information loss. To address this, we propose a 3D multi-plane vision transformer (MP-ViT) for hemorrhage classification with varying orientation data. It employs two separate transformer encoders for axial and sagittal contrasts, using cross-attention to integrate information across orientations. MP-ViT also includes a modality indication vector to provide missing contrast information to the model. The effectiveness of the proposed model is demonstrated with extensive experiments on real world clinical dataset consists of 10,084 training, 1,289 validation and 1,496 test subjects. MP-ViT achieved substantial improvement in area under the curve (AUC), outperforming the vision transformer (ViT) by 5.5% and CNN-based architectures by 1.8%. These results highlight the potential of MP-ViT in improving performance for hemorrhage detection when different orientation contrasts are needed.

Paradigm-Shifting Attention-based Hybrid View Learning for Enhanced Mammography Breast Cancer Classification with Multi-Scale and Multi-View Fusion.

Zhao H, Zhang C, Wang F, Li Z, Gao S

pubmed logopapersMay 12 2025
Breast cancer poses a serious threat to women's health, and its early detection is crucial for enhancing patient survival rates. While deep learning has significantly advanced mammographic image analysis, existing methods struggle to balance between view consistency with input adaptability. Furthermore, current models face challenges in accurately capturing multi-scale features, especially when subtle lesion variations across different scales are involved. To address this challenge, this paper proposes a Hybrid View Learning (HVL) paradigm that unifies traditional Single-View and Multi-View Learning approaches. The core component of this paradigm, our Attention-based Hybrid View Learning (AHVL) framework, incorporates two essential attention mechanisms: Contrastive Switch Attention (CSA) and Selective Pooling Attention (SPA). The CSA mechanism flexibly alternates between self-attention and cross-attention based on data integrity, integrating a pre-trained language model for contrastive learning to enhance model stability. Meanwhile, the SPA module employs multi-scale feature pooling and selection to capture critical features from mammographic images, overcoming the limitations of traditional models that struggle with fine-grained lesion detection. Experimental validation on the INbreast and CBIS-DDSM datasets shows that the AHVL framework outperforms both single-view and multi-view methods, especially under extreme view missing conditions. Even with an 80% missing rate on both datasets, AHVL maintains the highest accuracy and experiences the smallest performance decline in metrics like F1 score and AUC-PR, demonstrating its robustness and stability. This study redefines mammographic image analysis by leveraging attention-based hybrid view processing, setting a new standard for precise and efficient breast cancer diagnosis.

Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on <sup>18</sup>F-FDG PET/CT radiomics.

Hou X, Chen K, Luo H, Xu W, Li X

pubmed logopapersMay 12 2025
According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET/CT radiomics and clinicopathological characteristics. A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model's predictive power. According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774-0.897), 0.785 (95%CI: 0.665-0.877), and 0.788 (95%CI: 0.708-0.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708-0.846), 0.756 (95%CI: 0.634-0.854), and 0.779 (95%CI: 0.698-0.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890-0.958), 0.847 (95%CI: 0.764-0.910), and 0.835 (95%CI: 0.762-0.908) in the training set, independent validation set, and external validation set. Combined PET/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.

Use of Artificial Intelligence in Recognition of Fetal Open Neural Tube Defect on Prenatal Ultrasound.

Kumar M, Arora U, Sengupta D, Nain S, Meena D, Yadav R, Perez M

pubmed logopapersMay 12 2025
To compare the axial cranial ultrasound images of normal and open neural tube defect (NTD) fetuses using a deep learning (DL) model and to assess its predictive accuracy in identifying open NTD.It was a prospective case-control study. Axial trans-thalamic fetal ultrasound images of participants with open fetal NTD and normal controls between 14 and 28 weeks of gestation were taken after consent. The images were divided into training, testing, and validation datasets randomly in the ratio of 70:15:15. The images were further processed and classified using DL convolutional neural network (CNN) transfer learning (TL) models. The TL models were trained for 50 epochs. The data was analyzed in terms of Cohen kappa score, accuracy score, area under receiver operating curve (AUROC) score, F1 score validity, sensitivity, and specificity of the test.A total of 59 cases and 116 controls were fully followed. Efficient net B0, Visual Geometry Group (VGG), and Inception V3 TL models were used. Both Efficient net B0 and VGG16 models gave similar high training and validation accuracy (100 and 95.83%, respectively). Using inception V3, the training and validation accuracy was 98.28 and 95.83%, respectively. The sensitivity and specificity of Efficient NetB0 was 100 and 89%, respectively, and was the best.The analysis of the changes in axial images of the fetal cranium using the DL model, Efficient Net B0 proved to be an effective model to be used in clinical application for the identification of open NTD. · Open spina bifida is often missed due to the nonrecognition of the lemon sign on ultrasound.. · Image classification using DL identified open spina bifida with excellent accuracy.. · The research is clinically relevant in low- and middle-income countries..

MRI-Based Diagnostic Model for Alzheimer's Disease Using 3D-ResNet.

Chen D, Yang H, Li H, He X, Mu H

pubmed logopapersMay 12 2025
Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the leading cause of dementia worldwide and remains incurable once it begins. Therefore, early and accurate diagnosis is essential for effective intervention. Leveraging recent advances in deep learning, this study proposes a novel diagnostic model based on the 3D-ResNet architecture to classify three cognitive states: AD, mild cognitive impairment (MCI), and cognitively normal (CN) individuals, using MRI data. The model integrates the strengths of ResNet and 3D convolutional neural networks (3D-CNN), and incorporates a special attention mechanism(SAM) within the residual structure to enhance feature representation. The study utilized the ADNI dataset, comprising 800 brain MRI scans. The dataset was split in a 7:3 ratio for training and testing, and the network was trained using data augmentation and cross-validation strategies. The proposed model achieved 92.33% accuracy in the three-class classification task, and 97.61%, 95.83%, and 93.42% accuracy in binary classifications of AD vs. CN, AD vs. MCI, and CN vs. MCI, respectively, outperforming existing state-of-the-art methods. Furthermore, Grad-CAM heatmaps and 3D MRI reconstructions revealed that the cerebral cortex and hippocampus are critical regions for AD classification. These findings demonstrate a robust and interpretable AI-based diagnostic framework for AD, providing valuable technical support for its timely detection and clinical intervention.

Preoperative prediction of malignant transformation in sinonasal inverted papilloma: a novel MRI-based deep learning approach.

Ding C, Wen B, Han Q, Hu N, Kang Y, Wang Y, Wang C, Zhang L, Xian J

pubmed logopapersMay 12 2025
To develop a novel MRI-based deep learning (DL) diagnostic model, utilizing multicenter large-sample data, for the preoperative differentiation of sinonasal inverted papilloma (SIP) from SIP-transformed squamous cell carcinoma (SIP-SCC). This study included 568 patients from four centers with confirmed SIP (n = 421) and SIP-SCC (n = 147). Deep learning models were built using T1WI, T2WI, and CE-T1WI. A combined model was constructed by integrating these features through an attention mechanism. The diagnostic performance of radiologists, both with and without the model's assistance, was compared. Model performance was evaluated through receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). The combined model demonstrated superior performance in differentiating SIP from SIP-SCC, achieving AUCs of 0.954, 0.897, and 0.859 in the training, internal validation, and external validation cohorts, respectively. It showed optimal accuracy, stability, and clinical benefit, as confirmed by Brier scores and calibration curves. The diagnostic performance of radiologists, especially for less experienced ones, was significantly improved with model assistance. The MRI-based deep learning model enhances the capability to predict malignant transformation of sinonasal inverted papilloma before surgery. By facilitating earlier diagnosis and promoting timely pathological examination or surgical intervention, this approach holds the potential to enhance patient prognosis. Questions Sinonasal inverted papilloma (SIP) is prone to malignant transformation locally, leading to poor prognosis; current diagnostic methods are invasive and inaccurate, necessitating effective preoperative differentiation. Findings The MRI-based deep learning model accurately diagnoses malignant transformations of SIP, enabling junior radiologists to achieve greater clinical benefits with the assistance of the model. Clinical relevance A novel MRI-based deep learning model enhances the capability of preoperative diagnosis of malignant transformation in sinonasal inverted papilloma, providing a non-invasive tool for personalized treatment planning.

A systematic review and meta-analysis of the utility of quantitative, imaging-based approaches to predict radiation-induced toxicity in lung cancer patients.

Tong D, Midroni J, Avison K, Alnassar S, Chen D, Parsa R, Yariv O, Liu Z, Ye XY, Hope A, Wong P, Raman S

pubmed logopapersMay 11 2025
To conduct a systematic review and meta-analysis of the performance of radiomics, dosiomics and machine learning in generating toxicity prediction in thoracic radiotherapy. An electronic database search was conducted and dual-screened by independent authors to identify eligible studies for systematic review and meta-analysis. Data was extracted and study quality was assessed using TRIPOD for machine learning studies, RQS for Radiomics and RoB for dosiomics. 10,703 studies were identified, and 5252 entered screening. 106 studies including 23,373 patients were eligible for systematic review. Primary toxicity predicted was radiation pneumonitis (81), followed by esophagitis (12) and lymphopenia (4). Fourty-two studies studying radiation pneumonitis were eligible for meta-analysis, with pooled area-under-curve (AUC) of 0.82 (95% CI 0.79-0.85). Studies with machine learning had the best performance, with classical and deep learning models having similar performance. There is a trend towards an improvement of the performance of models with the year of publication. There is variability in study quality among the three study categories and dosiomic studies scored the highest among these. Publication bias was not observed. The majority of existing literature using radiomics, dosiomics and machine learning has focused on radiation pneumonitis prediction. Future research should focus on toxicity prediction of other organs at risk and the adoption of these models into clinical practice.

Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI.

Wang W, Wang Z, Wang L, Li J, Pang Z, Qu Y, Cui S

pubmed logopapersMay 11 2025
To develop a multiparametric breast MRI radiomics and deep learning-based multimodal model for predicting preoperative Ki-67 expression status in breast cancer, with the potential to advance individualized treatment and precision medicine for breast cancer patients. We included 176 invasive breast cancer patients who underwent breast MRI and had Ki-67 results. The dataset was randomly split into training (70 %) and test (30 %) sets. Features from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and dynamic contrast-enhanced MRI (DCE-MRI) were fused. Separate models were created for each sequence: T1, DWI, T2, and DCE. A multiparametric MRI (mp-MRI) model was then developed by combining features from all sequences. Models were trained using five-fold cross-validation and evaluated on the test set with receiver operating characteristic (ROC) curve area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Delong's test compared the mp-MRI model with the other models, with P < 0.05 indicating statistical significance. All five models demonstrated good performance, with AUCs of 0.83 for the T1 model, 0.85 for the DWI model, 0.90 for the T2 model, 0.92 for the DCE model, and 0.96 for the mp-MRI model. Delong's test indicated statistically significant differences between the mp-MRI model and the other four models, with P values < 0.05. The multiparametric breast MRI radiomics and deep learning-based multimodal model performs well in predicting preoperative Ki-67 expression status in breast cancer.

Altered intrinsic ignition dynamics linked to Amyloid-β and tau pathology in Alzheimer's disease

Patow, G. A., Escrichs, A., Martinez-Molina, N., Ritter, P., Deco, G.

biorxiv logopreprintMay 11 2025
Alzheimer's disease (AD) progressively alters brain structure and function, yet the associated changes in large-scale brain network dynamics remain poorly understood. We applied the intrinsic ignition framework to resting-state functional MRI (rs-fMRI) data from AD patients, individuals with mild cognitive impairment (MCI), and cognitively healthy controls (HC) to elucidate how AD shapes intrinsic brain activity. We assessed node-metastability at the whole-brain level and in 7 canonical resting-state networks (RSNs). Our results revealed a progressive decline in dynamical complexity across the disease continuum. HC exhibited the highest node-metastability, whereas it was substantially reduced in MCI and AD patients. The cortical hierarchy of information processing was also disrupted, indicating that rich-club hubs may be selectively affected in AD progression. Furthermore, we used linear mixed-effects models to evaluate the influence of Amyloid-{beta} (A{beta}) and tau pathology on brain dynamics at both regional and whole-brain levels. We found significant associations between both protein burdens and alterations in node metastability. Lastly, a machine learning classifier trained on brain dynamics, A{beta}, and tau burden features achieved high accuracy in discriminating between disease stages. Together, our findings highlight the progressive disruption of intrinsic ignition across whole-brain and RSNs in AD and support the use of node-metastability in conjunction with proteinopathy as a novel framework for tracking disease progression.
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