This paper introduces a Content-Based Medical Image Retrieval (CBMIR) system for detecting and retrieving lung disease cases to assist doctors and radiologists in clinical decision-making. The system combines texture-based features using Local Binary Patterns (LBP) with deep learning-based features extracted from pretrained CNN models, including VGG-16, DenseNet121, and InceptionV3. The objective is to identify the optimal fusion of texture and deep features to enhance the image retrieval performance. Various similarity measures, including Euclidean, Manhattan, and cosine similarities, were evaluated, with Cosine Similarity demonstrating the best performance, achieving an average precision of 65.5%. For COVID-19 cases, VGG-16 achieved a precision of 52.5%, while LBP performed best for the normal class with 85% precision. The fusion of LBP, VGG-16, and DenseNet121 excelled in pneumonia cases, with a precision of 93.5%. Overall, VGG-16 delivered the highest average precision of 74.0% across all classes, followed by LBP at 72.0%. The fusion of texture (LBP) and deep features from all CNN models achieved 86% accuracy for the retrieval of the top 10 images, supporting healthcare professionals in making more informed clinical decisions.

When adrenal incidentalomas are detected, diagnostic procedures are complicated by the need for endocrine-stimulating tests and imaging using various modalities to evaluate whether the tumor is a hormone-producing adrenal tumor. This study aimed to develop a machine-learning-based clinical model that combines computed tomography (CT) imaging and clinical parameters for adrenal tumor classification. This was a retrospective cohort study involving 162 patients who underwent hormone testing for adrenal incidentalomas at our institution. Nominal logistic regression analysis was used to identify the predictive factors for hormone-producing adrenal tumors, and three random forest classification models were developed using clinical and imaging parameters. The study included 55 patients with non-functioning adrenal tumors (NFAT), 44 with primary aldosteronism (PA), 22 with mild autonomous cortisol secretion (MACS), 18 with Cushing's syndrome (CS), and 23 with pheochromocytoma (Pheo). A random forest classification model combining the adrenal tumor diameter on CT, early morning hormone measurements, and several clinical parameters was constructed, and showed high diagnostic accuracy for PA, Pheo, and CS (area under the curve: 0.88, 0.85, and 0.80, respectively). However, sufficient diagnostic accuracy has not yet been achieved for MACS. This model provides a noninvasive and efficient tool for adrenal tumor classification, potentially reducing the need for additional hormonal stimulation tests. However, further validation studies are required to confirm the clinical utility of this method.

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-intensive and prone to variability, necessitating reliable automated solutions. This study explores lightweight deep learning models for multi-class brain tumor classification across four categories: glioma, meningioma, pituitary tumors, and no tumor. We investigate the performance of MobileNetV3 and SqueezeNet individually, and a feature-fusion hybrid model that combines their embedding layers. We utilized a publicly available MRI dataset containing 7023 images with a consistent internal split (65% training, 17% validation, 18% test) to ensure reliable evaluation. MobileNetV3 offers deep semantic understanding through its expressive features, while SqueezeNet provides minimal computational overhead. Their feature-level integration creates a balanced approach between diagnostic accuracy and deployment efficiency. Experiments conducted with consistent hyperparameters and preprocessing showed MobileNetV3 achieved the highest test accuracy (99.31%) while maintaining a low parameter count (3.47M), making it suitable for real-world deployment. Grad-CAM visualizations were employed for model explainability, highlighting tumor-relevant regions and helping visualize the specific areas contributing to predictions. Our proposed models outperform several baseline architectures like VGG16 and InceptionV3, achieving high accuracy with significantly fewer parameters. These results demonstrate that well-optimized lightweight networks can deliver accurate and interpretable brain tumor classification.

Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatomical priors, overlooking individual-specific structural differences during AD progression. To address these limitations, this work proposes a deep neural network that incorporates Transformer attention to jointly predict multiple cognitive scores, including ADAS, CDRSB, and MMSE. The architecture first employs a 3D convolutional neural network backbone to encode sMRI, capturing preliminary local structural information. Then an improved Transformer attention block integrated with 3D positional encoding and 3D convolutional layer to adaptively capture discriminative imaging features across the brain, thereby focusing on key cognitive-related regions effectively. Finally, an attention-aware regression network enables the joint prediction of multiple clinical scores. Experimental results demonstrate that our method outperforms some existing traditional and deep learning methods based on the ADNI dataset. Further qualitative analysis reveals that the dementia-related brain regions identified by the model hold important biological significance, effectively enhancing the performance of cognitive score prediction. Our code is publicly available at: https://github.com/lshsx/CTA_MRI.

Machine learning is now extensively implemented in medical imaging for preoperative risk stratification and post-therapeutic outcome assessment, enhancing clinical decision-making. Numerous studies have focused on predicting whether thyroid nodules are benign or malignant using a nodule-based approach, which is time-consuming, inefficient, and overlooks the impact of the peritumoral region. To evaluate the effectiveness of using the whole-thyroid as the region of interest in differentiating between benign and malignant thyroid nodules, exploring the potential application value of the entire thyroid. This study enrolled 1121 patients with thyroid nodules between February 2017 and May 2023. All participants underwent contrast-enhanced CT scans prior to surgical intervention. Radiomics features were extracted from arterial phase images, and feature dimensionality reduction was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Four machine learning models were trained on the selected features within the training cohort and subsequently evaluated on the independent validation cohort. The diagnostic performance of whole-thyroid versus nodule-based radiomics models was compared through receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) metrics. The nodule-based logistic regression model achieved an AUC of 0.81 in the validation set, with sensitivity, specificity, and accuracy of 78.6%, 69.4%, and 75.6%, respectively. The whole-thyroid-based random forest model attained an AUC of 0.80, with sensitivity, specificity, and accuracy of 90.0%, 51.9.%, and 80.1%, respectively. The AUC advantage ratios on the LR, DT, RF, and SVM models are approximately - 2.47%, 0.00%, - 4.76%, and - 4.94%, respectively. The Delong test showed no significant differences among the four machine learning models regarding the region of interest defined by either the thyroid primary lesion or the whole thyroid. There was no significant difference in distinguishing between benign and malignant thyroid nodules using either a nodule-based or whole-thyroid-based strategy for ROI outlining. We hypothesize that the whole-thyroid approach provides enhanced diagnostic capability for detecting papillary thyroid carcinomas (PTCs) with ill-defined margins.

Mechanical thrombectomy has become the standard of care in patients with stroke due to large vessel occlusion (LVO). However, only 50% of successfully treated patients show a favorable outcome. We developed and evaluated interpretable deep learning models to predict functional outcomes in terms of the modified Rankin Scale score alongside individualized treatment effects (ITEs) using data of 449 LVO stroke patients from a randomized clinical trial. Besides clinical variables, we considered non-contrast CT (NCCT) and angiography (CTA) scans which were integrated using novel foundation models to make use of advanced imaging information. Clinical variables had a good predictive power for binary functional outcome prediction (AUC of 0.719 [0.666, 0.774]) which could slightly be improved when adding CTA imaging (AUC of 0.737 [0.687, 0.795]). Adding NCCT scans or a combination of NCCT and CTA scans to clinical features yielded no improvement. The most important clinical predictor for functional outcome was pre-stroke disability. While estimated ITEs were well calibrated to the average treatment effect, discriminatory ability was limited indicated by a C-for-Benefit statistic of around 0.55 in all models. In summary, the models allowed us to jointly integrate CT imaging and clinical features while achieving state-of-the-art prediction performance and ITE estimates. Yet, further research is needed to particularly improve ITE estimation.

This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC). Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clinical-radiological model and deep learning models aimed at MVI prediction. The models were developed on a dataset of 263 cases consisting of data from three centers, internally validated on a set of 66 patients, and externally tested on two independent sets. An adversarial network-based deep learning (AD-DL) model was developed to learn domain-invariant features from multiple centers within the training set. The area under the receiver operating characteristic curve (AUC) was calculated using pathological MVI status. With the best-performed model, early recurrence-free survival (ERFS) stratification was validated on the external test set by the log-rank test, and the differentially expressed genes (DEGs) associated with MVI status were tested on the RNA sequencing analysis of the Cancer Imaging Archive. The AD-DL model demonstrated the highest diagnostic performance and generalizability with an AUC of 0.793 in the internal test set, 0.801 in external test set 1, and 0.773 in external test set 2. The model's prediction of MVI status also demonstrated a significant correlation with ERFS (p = 0.048). DEGs associated with MVI status were primarily enriched in the metabolic processes and the Wnt signaling pathway, and the epithelial-mesenchymal transition process. The AD-DL model allows preoperative MVI prediction and ERFS stratification in HCC patients, which has a good generalizability and biological interpretability. The adversarial network-based deep learning model predicts MVI status well in HCC patients and demonstrates good generalizability. By integrating bioinformatics analysis of the model's predictions, it achieves biological interpretability, facilitating its clinical translation. Current MVI assessment models for HCC lack interpretability and generalizability. The adversarial network-based model's performance surpassed clinical radiology and squeeze-and-excitation network-based models. Biological function analysis was employed to enhance the interpretability and clinical translatability of the adversarial network-based model.

Recent advances in the treatment of acute ischemic stroke contribute to improved patient outcomes, yet the mechanisms driving long-term disease trajectory are not well-understood. Current trends in the literature emphasize the distributed disruptive impact of stroke lesions on brain network organization. While most studies use population-derived data to investigate lesion interference on healthy tissue, the potential for individualized treatment strategies remains underexplored due to a lack of availability and effective utilization of the necessary clinical imaging data. To validate the potential for individualized patient evaluation, we explored and compared the differential information in network models based on normative and individual data. We further present our novel deep learning approach providing usable and accurate estimates of individual stroke impact utilizing minimal imaging data, thus bridging the data gap hindering individualized treatment planning. We created normative and individual disconnectomes for each of 78 patients (mean age 65.1 years, 32 females) from two independent cohort studies. MRI data and Barthel Index, as a measure of activities of daily living, were collected in the acute and early sub-acute phase after stroke (baseline) and at three months post stroke incident. Disconnectomes were subsequently described using 12 network metrics, including clustering coefficient and transitivity. Metrics were first compared between disconnectomes and further utilized as features in a classifier to predict a patients disease trajectory, as defined by three months Barthel Index. We then developed a deep learning architecture based on graph convolution and trained it to predict properties of the individual disconnectomes from the normative disconnectomes. Both disconnectomes showed statistically significant differences in topology and predictive power. Normative disconnectomes included a statistically significant larger number of connections (N=604 for normative versus N=210 for individual) and agreement between network properties ranged from r2=0.01 for clustering coefficient to r2=0.8 for assortativity, highlighting the impact of disconnectome choice on subsequent analysis. To predict patient deficit severity, individual data achieved an AUC score of 0.94 compared to an AUC score of 0.85 for normative based features. Our deep learning estimates showed high correlation with individual features (mean r2=0.94) and a comparable performance with an AUC score of 0.93. We were able to show how normative data-based analysis of stroke disconnections provides limited information regarding patient recovery. In contrast, individual data provided higher prognostic precision. We presented a novel approach to curb the need for individual data while retaining most of the differential information encoding individual patient disease trajectory.

Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI). A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally. The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], p < 0.001). The high BrainAGE cluster was associated with older age (p = 0.001) and higher proportion of female patients (p = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], p < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], p < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, p = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, p = 0.03). K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.

Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance diagnostic accuracy. However, its effectiveness in differentiating benign from malignant thoracic LNs remains uncertain. This meta-analysis aimed to evaluate the diagnostic performance of AI-assisted EBUS compared to the pathological reference standards. A systematic search was conducted across PubMed, Embase, and Web of Science for studies assessing AI-assisted EBUS in differentiating benign and malignant thoracic LNs. The reference standard included pathological confirmation via EBUS-guided transbronchial needle aspiration, surgical resection, or other histological/cytological validation methods. Sensitivity, specificity, diagnostic likelihood ratios, and diagnostic odds ratio (OR) were pooled using a random-effects model. The area under the receiver operating characteristic curve (AUROC) was summarized to evaluate diagnostic accuracy. Subgroup analyses were conducted by study design, lymph node location, and AI model type. Twelve studies with a total of 6,090 thoracic LNs were included. AI-assisted EBUS showed a pooled sensitivity of 0.75 (95% confidence interval [CI]: 0.60-0.86, I² = 97%) and specificity of 0.88 (95% CI: 0.83-0.92, I² = 96%). The positive and negative likelihood ratios were 6.34 (95% CI: 4.41-9.08) and 0.28 (95% CI: 0.17-0.47), respectively. The pooled diagnostic OR was 22.38 (95% CI: 11.03-45.38), and the AUROC was 0.90 (95% CI: 0.88-0.93). The subgroup analysis showed higher sensitivity but lower specificity in retrospective studies compared to prospective ones (sensitivity: 0.87 vs. 0.42; specificity: 0.80 vs. 0.93; both p < 0.001). No significant differences were found by lymph node location or AI model type. AI-assisted EBUS shows promise in differentiating benign from malignant thoracic LNs, particularly those with high specificity. However, substantial heterogeneity and moderate sensitivity highlight the need for cautious interpretation and further validation. PROSPERO CRD42025637964.