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.

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.

The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices and manufacturers. The lack of standardization hinders clinical interoperability, collaborative research, and the development of artificial intelligence (AI) models that depend on large, high-quality datasets. The Digital Imaging and Communication in Medicine (DICOM) standard offers a robust solution for ensuring interoperability in medical imaging. Although DICOM is widely utilized in radiology and cardiology, its adoption in ophthalmology remains limited. Retinal imaging modalities such as optical coherence tomography (OCT), fundus photography, and OCT angiography (OCTA) have revolutionized retinal disease management but are constrained by proprietary and non-standardized formats. This review underscores the necessity for harmonized imaging standards in ophthalmology, detailing DICOM standards for retinal imaging including ophthalmic photography (OP), OCT, and OCTA, and their requisite metadata information. Additionally, the potential of DICOM standardization for advancing AI applications in ophthalmology is explored. A notable example is the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) dataset, the first publicly available standards-compliant DICOM retinal imaging dataset. This dataset encompasses diverse retinal imaging modalities, including color fundus photography, infrared, autofluorescence, OCT, and OCTA. By leveraging multimodal retinal imaging, AI-READI provides a transformative resource for studying diabetes and its complications, setting a blueprint for future datasets aimed at harmonizing imaging formats and enabling AI-driven breakthroughs in ophthalmology. Our manuscript also addresses challenges in retinal imaging for diabetic patients, retinal imaging-based AI applications for studying diabetes, and potential advancements in retinal imaging standardization.

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.

BackgroundLung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray and similar performance to CT at lower cost. Despite these benefits, widespread adoption is limited by operator dependency, moderate interrater reliability, and training requirements. Deep learning (DL) could potentially address these challenges, but development of effective algorithms is hindered by the scarcity of comprehensive image repositories with proper metadata. MethodsWe created an open-source dataset of LUS images derived a multi-center study involving N=226 adult patients presenting with respiratory symptoms to emergency departments between March 2020 and April 2022. Images were acquired using a standardized scanning protocol (12-zone or modified 8-zone) with various point-of-care ultrasound devices. Three blinded researchers independently analyzed each image following consensus guidelines, with disagreements adjudicated to provide definitive interpretations. Videos were pre-processed to remove identifiers, and frames were extracted and resized to 128x128 pixels. ResultsThe dataset contains 1,874 video clips comprising 303,977 frames. Half of the participants (50%) had COVID-19 pneumonia. Among all clips, 66% contained no abnormalities, 18% contained B-lines, 4.5% contained consolidations, 6.4% contained both B-lines and consolidations, and 5.2% had indeterminate findings. Pathological findings varied significantly by lung zone, with anterior zones more frequently normal and less likely to show consolidations compared to lateral and posterior zones. DiscussionThis dataset represents one of the largest annotated LUS repositories to date, including both COVID-19 and non-COVID-19 patients. The comprehensive metadata and expert interpretations enhance its utility for DL applications. Despite limitations including potential device-specific characteristics and COVID-19 predominance, this repository provides a valuable resource for developing AI tools to improve LUS acquisition and interpretation.

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities for improving stroke management by informing acute interventions and guiding personalized rehabilitation strategies. The location of the stroke is a key predictor of outcomes, yet no clinically deployed tools incorporate lesion location information for outcome prognostication. This study responds to this critical need by introducing a fully automated, three-stage neuroimaging processing and machine learning pipeline that predicts personalized outcomes from clinical imaging in adult ischemic stroke patients. In the first stage, our system automatically processes raw DICOM inputs, registers the brain to a standard template, and uses deep learning models to segment the stroke lesion. In the second stage, lesion location and automatically derived network features are input into statistical models trained to predict long-term impairments from a large independent cohort of lesion patients. In the third stage, a structured PDF report is generated using a large language model that describes the strokes location, the arterial distribution, and personalized prognostic information. We demonstrate the viability of this approach in a proof-of-concept application predicting select cognitive outcomes in a stroke cohort. Brain-behavior models were pre-trained to predict chronic impairment on 28 different cognitive outcomes in a large cohort of patients with focal brain lesions (N=604). The automated pipeline used these models to predict outcomes from clinically acquired MRIs in an independent ischemic stroke cohort (N=153). Starting from raw clinical DICOM images, we show that our pipeline can generate outcome predictions for individual patients in less than 3 minutes with 96% concordance relative to methods requiring manual processing. We also show that prediction accuracy is enhanced using models that incorporate lesion location, lesion-associated network information, and demographics. Our results provide a strong proof-of-concept and lay the groundwork for developing imaging-based clinical tools for stroke outcome prognostication.

Differentiating between follicular thyroid adenoma (FTA), carcinoma (FTC), and follicular tumor with uncertain malignant potential (FT-UMP) remains challenging due to their overlapping ultrasound characteristics. This retrospective study aimed to enhance preoperative diagnostic accuracy by utilizing intra- and peritumoral radiomics based on ultrasound images. We collected post-thyroidectomy ultrasound images from 774 patients diagnosed with FTA (n = 429), FTC (n = 158), or FT-UMP (n = 187) between January 2018 and December 2023. Six peritumoral regions were expanded by 5%-30% in 5% increments, with the segment-anything model utilizing prompt learning to detect the field of view and constrain the expanded boundaries. A stepwise classification strategy addressing three tasks was implemented: distinguishing FTA from the other types (task 1), differentiating FTC from FT-UMP (task 2), and classifying all three tumors. Diagnostic models were developed by combining radiomic features from tumor and peritumoral regions with clinical characteristics. Clinical characteristics combined with intratumoral and 5% peritumoral radiomic features performed best across all tasks (Test set: area under the curves, 0.93 for task 1 and 0.90 for task 2; diagnostic accuracy, 79.9%). The DeLong test indicated that all peritumoral radiomics significantly improved intratumoral radiomics performance and clinical characteristics (p < 0.04). The 5% peritumoral regions showed the best performance, though not all results were significant (p = 0.01-0.91). Ultrasound-based intratumoral and peritumoral radiomics can significantly enhance preoperative diagnostic accuracy for FTA, FTC, and FT-UMP, leading to improved treatment strategies and patient outcomes. Furthermore, the 5% peritumoral area may indicate regions of potential tumor invasion requiring further investigation.

The surgeries in drug-resistant ulcerative colitis are determined by complex factors. This study evaluated the predictive performance of radiomics analysis on the basis of whether patients with ulcerative colitis in hospital were in the surgical or medical treatment group by discharge from hospital. This single-center retrospective cohort study used CT at admission of patients with US admitted from 2015 to 2022. The target of prediction was whether the patient would undergo surgery by the time of discharge. Radiomics features were extracted using the rectal wall at the level of the tailbone tip of the CT as the region of interest. CT data were randomly classified into a training cohort and a validation cohort, and LASSO regression was performed using the training cohort to create a formula for calculating the radiomics score. A total of 147 patients were selected, and data from 184 CT scans were collected. Data from 157 CT scans matched the selection criteria and were included. Five features were used for the radiomics score. Univariate logistic regression analysis of clinical information detected a significant influence of severity (p < 0.001), number of drugs used until surgery (p < 0.001), Lichtiger score (p = 0.024), and hemoglobin (p = 0.010). Using a nomogram combining these items, we found that the discriminatory power in the surgery and medical treatment groups was AUC 0.822 (95% confidence interval (CI) 0.841-0.951) for the training cohort and AUC 0.868 (95% CI 0.729-1.000) for the validation cohort, indicating a good ability to discriminate the outcomes. Radiomics analysis of CT images of patients with US at the time of admission, combined with clinical data, showed high predictive ability regarding a treatment strategy of surgery or medical treatment.

This study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumor analysis. This retrospective study used data from 2,560 esophageal cancer patients across multiple clinical centers, collected from 2018 to 2023. The dataset included CT scan images and clinical information, representing a variety of cancer grades and types. Standardized CT imaging protocols were followed, and experienced radiologists manually segmented the tumor regions. Only high-quality data were used in the study. A total of 215 radiomic features were extracted using the SERA platform. The study used two deep learning models-DenseNet121 and EfficientNet-B0-enhanced with attention mechanisms to improve accuracy. A combined classification approach used both radiomic and deep learning features, and machine learning models like Random Forest, XGBoost, and CatBoost were applied. These models were validated with strict training and testing procedures to ensure effective cancer grading. This study analyzed the reliability and performance of radiomic and deep learning features for grading esophageal cancer. Radiomic features were classified into four reliability levels based on their ICC (Intraclass Correlation) values. Most of the features had excellent (ICC > 0.90) or good (0.75 < ICC ≤ 0.90) reliability. Deep learning features extracted from DenseNet121 and EfficientNet-B0 were also categorized, and some of them showed poor reliability. The machine learning models, including XGBoost and CatBoost, were tested for their ability to grade cancer. XGBoost with Recursive Feature Elimination (RFE) gave the best results for radiomic features, with an AUC (Area Under the Curve) of 91.36%. For deep learning features, XGBoost with Principal Component Analysis (PCA) gave the best results using DenseNet121, while CatBoost with RFE performed best with EfficientNet-B0, achieving an AUC of 94.20%. Combining radiomic and deep features led to significant improvements, with XGBoost achieving the highest AUC of 96.70%, accuracy of 96.71%, and sensitivity of 95.44%. The combination of both DenseNet121 and EfficientNet-B0 models in ensemble models achieved the best overall performance, with an AUC of 95.14% and accuracy of 94.88%. This study improves esophageal cancer grading by combining radiomics and deep learning. It enhances diagnostic accuracy, reproducibility, and interpretability, while also helping in personalized treatment planning through better tumor characterization. Not applicable.

This study aimed to validate the agreement and diagnostic performance of a deep-learning-based coronary artery calcium scoring (DL-CACS) system for ECG-gated and non-gated low-dose chest CT (LDCT) across multivendor datasets. In this retrospective study, datasets from Seoul National University Hospital (SNUH, 652 paired ECG-gated and non-gated CT scans) and the Stanford public dataset (425 ECG-gated and 199 non-gated CT scans) were analyzed. Agreement metrics included intraclass correlation coefficient (ICC), coefficient of determination (R²), and categorical agreement (κ). Diagnostic performance was assessed using categorical accuracy and the area under the receiver operating characteristic curve (AUROC). DL-CACS demonstrated excellent performance for ECG-gated CT in both datasets (SNUH: R² = 0.995, ICC = 0.997, κ = 0.97, AUROC = 0.99; Stanford: R² = 0.989, ICC = 0.990, κ = 0.97, AUROC = 0.99). For non-gated CT using manual LDCT CAC scores as a reference, performance was similarly high (R² = 0.988, ICC = 0.994, κ = 0.96, AUROC = 0.98-0.99). When using ECG-gated CT scores as the reference, performance for non-gated CT was slightly lower but remained robust (SNUH: R² = 0.948, ICC = 0.968, κ = 0.88, AUROC = 0.98-0.99; Stanford: R² = 0.949, ICC = 0.948, κ = 0.71, AUROC = 0.89-0.98). DL-CACS provides a reliable and automated solution for CACS, potentially reducing workload while maintaining robust performance in both ECG-gated and non-gated CT settings. Question How accurate and reliable is deep-learning-based coronary artery calcium scoring (DL-CACS) in ECG-gated CT and non-gated low-dose chest CT (LDCT) across multivendor datasets? Findings DL-CACS showed near-perfect performance for ECG-gated CT. For non-gated LDCT, performance was excellent using manual scores as the reference and lower but reliable when using ECG-gated CT scores. Clinical relevance DL-CACS provides a reliable and automated solution for CACS, potentially reducing workload and improving diagnostic workflow. It supports cardiovascular risk stratification and broader clinical adoption, especially in settings where ECG-gated CT is unavailable.