Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural arteries and veins without intervening capillary beds. Cognitive impairment is a common symptom in DAVFs, often linked to disrupted brain network connectivity. Resting-state functional MRI (rsfMRI) allows for examining functional connectivity through blood oxygenation level dependent (BOLD) signal analysis. However, rsfMRI signals exhibit fractal behavior that complicates connectivity analysis. This study explores nonfractal connectivity as a potential biomarker for cognitive impairment in DAVF patients by isolating short-memory components in BOLD signals.
Method: 50 DAVF patients and 50 healthy controls underwent neuropsychological assessments and rsfMRI. Preprocessed BOLD signals were decomposed using wavelet transforms to isolate fractal and nonfractal components. Connectivity matrices based on fractal, nonfractal, and Pearson correlation components were generated and used as features for classification. Machine learning classifiers, including SVM and decision trees, were optimized via cross-validation in MATLAB, with performance assessed by accuracy, sensitivity, specificity, and AUC.
Results: Nonfractal connectivity outperformed fractal and Pearson correlation measures, achieving a classification accuracy of 89.82% using SVM, with high sensitivity (86.54%), specificity (92.4%), and an AUC of 0.96. Nonfractal connectivity effectively differentiated cognitive impairment in DAVFs, offering a clearer depiction of neural activity by reducing the influence of fractal patterns.
Conclusion: This study suggests that nonfractal connectivity is a promising biomarker for assessing cognitive impairment in DAVF patients, potentially supporting early diagnosis and intervention. While nonfractal analysis showed promising classification accuracy, further research with larger datasets is needed to validate these findings and explore applicability in other neurological conditions.&#xD.


Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patient motion management methods. The purpose of this paper is to investigate a deep learning-based method to segment H&N tumors in patient kilovoltage (kV) x-ray images to enable real-time H&N tumor tracking during RT.
Approach: An ethics-approved clinical study collected data from 17 H&N cancer patients undergoing conventional H&N RT. For each patient, personalized conditional Generative Adversarial Networks (cGANs) were trained to segment H&N tumors in kV x-ray images. Network training data were derived from each patient's planning CT and contoured gross tumor volumes (GTV). For each training epoch, the planning CT and GTV were deformed and forward projected to create the training dataset. The testing data consisted of kV x-ray images used to reconstruct the pre-treatment CBCT volume for the first, middle and end fractions. The ground truth tumor locations were derived by deformably registering the planning CT to the pre-treatment CBCT and then deforming the GTV and forward projecting the deformed GTV. The generated cGAN segmentations were compared to ground truth tumor segmentations using the absolute magnitude of the centroid error and the mean surface distance (MSD) metrics.
Main Results:
The centroid error for the nasopharynx (n=4), oropharynx (n=9) and larynx (n=4) patients was 1.5±0.9mm, 2.4±1.6mm, 3.5±2.2mm respectively and the MSD was 1.5±0.3mm, 1.9±0.9mm and 2.3±1.0mm respectively. There was a weak correlation between the centroid error and the LR tumor location (r=0.41), which was higher for oropharynx patients (r=0.77).
Significance: The paper reports on markerless H&N tumor detection accuracy using kV images. Accurate tracking of H&N tumors can enable more precise RT leading to mask-free RT enabling better patient outcomes.&#xD.

In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoing reperfusion treatment. However, its immediate availability remains limited, especially in resource-constrained regions. We sought to evaluate the association of initial core volume (ICV) measured on non-contrast computed tomography (NCCT) by a deep learning-based algorithm with outcomes in patients undergoing reperfusion treatment. Consecutive patients who received reperfusion treatments were collected from a prospectively maintained registry in three comprehensive stroke centers from January 2021 to May 2024. ICV on admission was estimated on NCCT by a previously validated deep learning algorithm (Methinks). Outcomes of interest included favorable outcome (modified Rankin Scale score 0-2 at 90 days) and symptomatic intracranial hemorrhage (sICH). The study comprised 658 patients of mean (SD) age 72.7 (14.4) years and median (IQR) baseline National Institutes of Health Stroke Scale (NIHSS) score of 12 (6-19). Primary endovascular treatment was performed in 53.7% of patients and 24.9% received IV thrombolysis only. Patients with favorable outcomes had a lower mean (SD) automated ICV (aICV; 12.9 (26.9) mL vs 34.9 (40) mL, P<0.001). Lower aICV was associated with a favorable outcome (adjusted OR 0.983 (95% CI 0.975 to 0.992), P<0.001) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of a favorable outcome decreased by 1.7%. Patients who experienced sICH had a higher mean (SD) aICV (47.8 (61.1) mL vs 20.5 (32) mL, P=0.001). Higher aICV was independently associated with sICH (adjusted OR 1.014 (95% CI 1.004 to 1.025), P=0.009) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of sICH increased by 1.4%. In patients with stroke undergoing reperfusion therapy, aICV assessment on NCCT predicts long-term outcomes and sICH. Further studies determining the potential role of aICV assessment to safely expand and simplify reperfusion therapies based on AI interpretation of NCCT may be justified.

This study aims to develop an Exploratory deep learning method to quantitatively predict hematoma progression (EXPEDITION in short) after intracerebral hemorrhage (ICH). Patients with primary ICH in the basal ganglia or thalamus were retrospectively enrolled, and their baseline non-contrast CT (NCCT) image, CT perfusion (CTP) images, and subsequent re-examining NCCT images from the 2nd to the 8th day after baseline CTP were collected. The subjects who had received three or more re-examining scans were categorized into the test data set, and others were assigned to the training data set. Hematoma volume was estimated by manually outlining the lesion shown on each NCCT scan. Cerebral venous hemodynamic feature was extracted from CTP images. Then, EXPEDITION was trained. The Bland-Altman analysis was used to assess the prediction performance. A total of 126 patients were enrolled initially, and 73 patients were included in the final analysis. They were then categorized into the training data set (58 patients with 93 scans) and the test data set (15 patients with 50 scans). For the test set, the mean difference [mean ±1.96SD] of hematoma volume between the EXPEDITION prediction and the reference is -0.96 [-9.64, +7.71] mL. Specifically, in the test set, the consistency between the true and the predicted volume values was compared, indicating that the EXPEDITION achieved the needed accuracy for quantitative prediction of hematoma progression. An Exploratory deep learning method, EXPEDITION, was proposed to quantitatively predict hematoma progression after primary ICH in basal ganglia or thalamus.

Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI multicenter datasets for endometriosis and shows the baseline segmentation performance of two auto-segmentation pipelines: the self-configuring nnU-Net and RAovSeg, a custom network. The multi-sequence endometriosis MRI scans from two clinical institutions were collected. A multicenter dataset of 51 subjects with manual labels for multiple pelvic structures from three raters was used to assess interrater agreement. A second single-center dataset of 81 subjects with labels for multiple pelvic structures from one rater was used to develop the ovary auto-segmentation pipelines. Uterus and ovary segmentations are available for all subjects, endometrioma segmentation is available for all subjects where it is detectable in the image. This study highlights the challenges of manual ovary segmentation in endometriosis MRI and emphasizes the need for an auto-segmentation method. The dataset is publicly available for further research in pelvic MRI auto-segmentation to support endometriosis research.

This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound images and clinicopathologic data from 210 female breast cancer patients, employing a Generative Adversarial Network (GAN) to enhance image clarity and segment the region of interest (ROI) for Radiomics feature extraction. Features were optimized through Z-score normalization and various statistical methods. We constructed and compared multiple machine learning models, including Linear Regression, Random Forest, and XGBoost, with deep learning models such as CNNs (ResNet101, VGG19) and Transformer technology. The Grad-CAM technique was used to visualize the decision-making process of the deep learning models. The Deep Learning Radiomics (DLR) model integrated Radiomics features with deep learning features, and a combined model further integrated clinical features to predict HER-2 status. The LightGBM and ResNet101 models showed high performance, but the combined model achieved the highest AUC values in both training and testing, demonstrating the effectiveness of integrating diverse data sources. The study successfully demonstrates that the fusion of deep learning with Radiomics analysis significantly improves the prediction accuracy of HER-2 status, offering a new strategy for personalized breast cancer treatment and prognostic assessments.

To develop two distinct models for predicting microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) based on habitat imaging, and to integrate these models for prognosis assessment. In this multicenter retrospective study, patients from two different institutions were enrolled and categorized for MVI (n=295) and VETC (n=276) prediction. Tumor and peritumoral regions on hepatobiliary phase images were segmented into subregions, from which all relevant features were extracted. The MVI and VETC predictive models were constructed by analyzing these features using various machine learning algorithms, and classifying patients into high-risk and low-risk groups. Cox regression analysis was utilized to identify risk factors for early recurrence. The MVI and VETC prediction models demonstrated excellent performance in both the training and external validation cohorts (AUC: 0.961 and 0.838 for MVI; 0.931 and 0.820 for VETC). Based on model predictions, patients were classified into high-risk group (High-risk MVI/ High-risk VETC), medium-risk group (High-risk MVI/Low-risk VETC or Low-risk MVI/High-risk VETC), and low-risk group (Low-risk MVI/Low-risk VETC). Multivariable Cox regression analysis revealed that risk group, number of tumors, and gender were independent predictors of early recurrence. Models based on habitat imaging can be used for the preoperative, noninvasive prediction of MVI and VETC, offering valuable stratification and diagnostic insights for HCC patients.

A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at a given examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors' knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up radiology report summary. In this study, we propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of report summary generation, we introduce two mechanisms to bestow clinical insight to our model, namely disease probability soft guidance and masked entity modeling loss. The former mechanism employs a pretrained abnormality classifier to guide the presence level of specific abnormalities, while the latter directs the model's attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model exceeded the state-of-the-art.

To compare the diagnostic performance of deep learning reconstruction (DLR) of zero echo time (ZTE) MRI for structural lesions in patients with axial spondyloarthritis, against T1WI and ZTE MRI without DLR, using CT as the reference standard. From February 2021 to December 2022, 26 patients (52 sacroiliac joints (SIJ) and 104 quadrants) underwent SIJ MRIs. Three readers assessed overall image quality and structural conspicuity, scoring SIJs for structural lesions on T1WI, ZTE, and ZTE DLR 50%, 75%, and 100%, respectively. Diagnostic performance was evaluated using CT as the reference standard, and inter-reader agreement was assessed using weighted kappa. ZTE DLR 100% showed the highest image quality scores for readers 1 and 2, and the best structural conspicuity scores for all three readers. In readers 2 and 3, ZTE DLR 75% showed the best diagnostic performance for bone sclerosis, outperforming T1WI and ZTE (all p < 0.05). In all readers, ZTE DLR 100% showed superior diagnostic performance for bone erosion compared to T1WI and ZTE (all p < 0.01). For bone sclerosis, ZTE DLR 50% showed the highest kappa coefficients between readers 1 and 2 and between readers 1 and 3. For bone erosion, ZTE DLR 100% showed the highest kappa coefficients between readers. ZTE MRI with DLR outperformed T1WI and ZTE MRI without DLR in diagnosing bone sclerosis and erosion of the SIJ, while offering similar subjective image quality and structural conspicuity. Question With zero echo time (ZTE) alone, small structural lesions, such as bone sclerosis and erosion, are challenging to confirm in axial spondyloarthritis. Findings ZTE deep learning reconstruction (DLR) showed higher diagnostic performance for detecting bone sclerosis and erosion, compared with T1WI and ZTE. Clinical relevance Applying DLR to ZTE enhances diagnostic capability for detecting bone sclerosis and erosion in the sacroiliac joint, aiding in the early diagnosis of axial spondyloarthritis.

Contrast-enhanced magnetic resonance imaging (CE-MRI) monitoring across multiple time points is critical for optimizing hepatocellular carcinoma (HCC) prognosis during transarterial chemoembolization (TACE) treatment. The aim of this retrospective study is to develop and validate an artificial intelligence (AI)-powered models utilizing multi-time-point arterial phase CE-MRI data for HCC prognosis stratification in TACE patients. A total of 543 individual arterial phase CE-MRI scans from 181 HCC patients were retrospectively collected in this study. All patients underwent TACE and longitudinal arterial phase CE-MRI assessments at three time points: prior to treatment, and following the first and second TACE sessions. Among them, 110 patients received TACE monotherapy, while the remaining 71 patients underwent TACE in combination with microwave ablation (MWA). All images were subjected to standardized preprocessing procedures. We developed an end-to-end deep learning model, ProgSwin-UNETR, based on the Swin Transformer architecture, to perform four-class prognosis stratification directly from input imaging data. The model was trained using multi-time-point arterial phase CE-MRI data and evaluated via fourfold cross-validation. Classification performance was assessed using the area under the receiver operating characteristic curve (AUC). For comparative analysis, we benchmarked performance against traditional radiomics-based classifiers and the mRECIST criteria. Prognostic utility was further assessed using Kaplan-Meier (KM) survival curves. Additionally, multivariate Cox proportional hazards regression was performed as a post hoc analysis to evaluate the independent and complementary prognostic value of the model outputs and clinical variables. GradCAM +  + was applied to visualize the imaging regions contributing most to model prediction. The ProgSwin-UNETR model achieved an accuracy of 0.86 and an AUC of 0.92 (95% CI: 0.90-0.95) for the four-class prognosis stratification task, outperforming radiomic models across all risk groups. Furthermore, KM survival analyses were performed using three different approaches-AI model, radiomics-based classifiers, and mRECIST criteria-to stratify patients by risk. Of the three approaches, only the AI-based ProgSwin-UNETR model achieved statistically significant risk stratification across the entire cohort and in both TACE-alone and TACE + MWA subgroups (p < 0.005). In contrast, the mRECIST and radiomics models did not yield significant survival differences across subgroups (p > 0.05). Multivariate Cox regression analysis further demonstrated that the model was a robust independent prognostic factor (p = 0.01), effectively stratifying patients into four distinct risk groups (Class 0 to Class 3) with Log(HR) values of 0.97, 0.51, -0.53, and -0.92, respectively. Additionally, GradCAM +  + visualizations highlighted critical regional features contributing to prognosis prediction, providing interpretability of the model. ProgSwin-UNETR can well predict the various risk groups of HCC patients undergoing TACE therapy and can further be applied for personalized prediction.