This study investigates the use of CT-based radiomics for predicting extrahepatic metastasis in hepatocellular carcinoma (HCC) following hepatectomy. We analyzed data from 374 patients from two centers (277 in the training cohort and 97 in an external validation cohort). Radiomic features were extracted from contrast-enhanced CT scans. Key features were identified using the least absolute shrinkage and selection operator (LASSO) to compute radiomics scores (radscore) for model development. A clinical model based on risk factors was also created. We developed a combined model integrating both radscore and clinical variables, constructing nomograms for personalized risk assessment. Model performance was compared via the Delong test, with calibration curves assessing prediction consistency. Decision curve analysis (DCA) was employed to assess the clinical utility and net benefit of the predictive models across different threshold probabilities, thereby evaluating their potential value in guiding clinical decision-making for extrahepatic metastasis. Radscore based on CT was an independent predictor of extrahepatic disease (p < 0.05). The combined model showed high predictive performance with an AUC of 87.2% (95% CI: 81.8%-92.6%) in the training group and 86.0% (95% CI: 69.4%-100%) in the validation group. Predictive performance of the combined model significantly outperformed both the radiomics and clinical models (p < 0.05). The DCA shows that the combined model has a higher net benefit in predicting extrahepatic metastases of HCC than the clinical model and radiomics model. The combined prediction model, utilizing CT radscore alongside clinical risk factors, effectively forecasts extrahepatic metastasis in HCC patients.

This study aims to apply a semi-supervised machine learning approach for classifying major depressive disorder (MDD) patients into more homogeneous cognitive subtypes based on multidimensional cognitive profiles, and to perform multimodal neuroimaging to identify subtype-specific neural signatures. A total of 147 MDD patients and 222 healthy controls (HCs) completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) and magnetic resonance imaging (MRI) scans. Cognitive subtypes were derived based on neurocognitive profiles using heterogeneity through discriminative analysis (HYDRA). General linear models (GLMs) were employed to assess differences across groups in neurocognitive indexes and neuroimaging data followed by Tukey's post-hoc test for pairwise comparisons between the groups. Based on cognitive profiles, MDD patients were classified into cognitive deficit (CD, N = 75) and cognitive preservation (CP, N = 72) subtypes. Voxel-based morphometry (VBM) revealed reduced grey matter volume (GMV) in the left fusiform gyrus and left cerebellum in MDD patients when compared to HCs, with CD patients showing greater atrophy than patients in CP subtype. Meanwhile, the amplitude of low-frequency fluctuations (ALFF) in the temporal lobe of both MDD subtypes was decreased when compared to that of HCs, showing no inter-subtype differences. A subtype of MDD characterized by comprehensive cognitive deficits is associated with structural atrophy in the left fusiform gyrus and cerebellum, suggesting these regions as potential biomarkers for the cognitive deficit subtype of MDD. However, no significant differences in ALFF were observed between the two cognitive subgroups.

Alzheimers and Parkinsons diseases are age-related neurodegenerative diseases that often require invasive procedures for diagnosis. Traditional diagnostic methods may fail to capture the interplay between genetic, molecular, and neuroanatomical markers. This manuscript aims to develop interpretable machine learning models that can predict key biomarkers, such as pTau, tTau, A{beta} positivity, and motor symptom severity, using non-invasive data. Machine learning models (Random Forest, XGBoost) were trained using ADNI and PPMI baseline data. Using the APOE4 genotype, MRI volumes, cognitive scores, and demographics as inputs, SHAP was employed to enhance model interpretability. Models achieved AUCs of 0.859 (tTau) and 0.852 (pTau) with recall > 80%. The PD motor severity yielded an MAE of 5.72 and an R2 of 0.586. SHAP confirmed the contributions of APOE4 status, hippocampal atrophy, and dopaminergic asymmetries. The pipelines provide clinically meaningful predictions of biomarker status and motor symptoms, supporting interpretable, multi-axis neurodiagnostic tools within the neurodiagnoses framework.

Health care systems around the world face numerous challenges. Recent advances in artificial intelligence (AI) have offered promising solutions, particularly in diagnostic imaging. This systematic review focused on evaluating the economic feasibility of AI in real-world diagnostic imaging scenarios, specifically for dermatological, neurological, and pulmonary diseases. The central question was whether the use of AI in these diagnostic assessments improves economic outcomes and promotes equity in health care systems. This systematic review has 2 main components, economic evaluation and equity assessment. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) tool to ensure adherence to best practices in systematic reviews. The protocol was registered with PROSPERO (International Prospective Register of Systematic Reviews), and we followed the PRISMA-E (Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Equity Extension) guidelines for equity. Scientific articles reporting on economic evaluations or equity considerations related to the use of AI-based tools in diagnostic imaging in dermatology, neurology, or pulmonology were included in the study. The search was conducted in the PubMed, Embase, Scopus, and Web of Science databases. Methodological quality was assessed using the following checklists, CHEC (Consensus on Health Economic Criteria) for economic evaluations, EPHPP (Effective Public Health Practice Project) for equity evaluation studies, and Welte for transferability. The systematic review identified 9 publications within the scope of the research question, with sample sizes ranging from 122 to over 1.3 million participants. The majority of studies addressed economic evaluation (88.9%), with most studies addressing pulmonary diseases (n=6; 66.6%), followed by neurological diseases (n=2; 22.3%), and only 1 (11.1%) study addressing dermatological diseases. These studies had an average quality access of 87.5% on the CHEC checklist. Only 2 studies were found to be transferable to Brazil and other countries with a similar health context. The economic evaluation revealed that 87.5% of studies highlighted the benefits of using AI in dermatology, neurology, and pulmonology, highlighting significant cost-effectiveness outcomes, with the most advantageous being a negative cost-effectiveness ratio of -US $27,580 per QALY (quality-adjusted life year) for melanoma diagnosis, indicating substantial cost savings in this scenario. The only study assessing equity, based on 129,819 radiographic images, identified AI-assisted underdiagnosis, particularly in certain subgroups defined by gender, ethnicity, and socioeconomic status. This review underscores the importance of transparency in the description of AI tools and the representativeness of population subgroups to mitigate health disparities. As AI is rapidly being integrated into health care, detailed assessments are essential to ensure that benefits reach all patients, regardless of sociodemographic factors.

To develop a CT-based deep learning radiomics nomogram (DLRN) for the preoperative prediction of peritoneal metastasis (PM) in patients with ovarian cancer (OC). A total of 296 patients with OCs were randomly divided into training dataset (N = 207) and test dataset (N = 89). The radiomics features and DL features were extracted from CT images of each patient. Specifically, radiomics features were extracted from the 3D tumor regions, while DL features were extracted from the 2D slice with the largest tumor region of interest (ROI). The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomics and DL features, and the radiomics score (Radscore) and DL score (Deepscore) were calculated. Multivariate logistic regression was employed to construct clinical model. The important clinical factors, radiomics and DL features were integrated to build the DLRN. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and DeLong's test. Nine radiomics features and 10 DL features were selected. Carbohydrate antigen 125 (CA-125) was the independent clinical predictor. In the training dataset, the AUC values of the clinical, radiomics and DL models were 0.618, 0.842, and 0.860, respectively. In the test dataset, the AUC values of these models were 0.591, 0.819 and 0.917, respectively. The DLRN showed better performance than other models in both training and test datasets with AUCs of 0.943 and 0.951, respectively. Decision curve analysis and calibration curve showed that the DLRN provided relatively high clinical benefit in both the training and test datasets. The DLRN demonstrated superior performance in predicting preoperative PM in patients with OC. This model offers a highly accurate and noninvasive tool for preoperative prediction, with substantial clinical potential to provide critical information for individualized treatment planning, thereby enabling more precise and effective management of OC patients.

To establish an ultrasound-based radiomics model to differentiate fibro adipose vascular anomaly (FAVA) and intramuscular venous malformation (VM). The clinical data of 65 patients with VM and 31 patients with FAVA who were treated and pathologically confirmed were retrospectively analyzed. Dimensionality reduction was performed on these features using the least absolute shrinkage and selection operator (LASSO). An ultrasound-based radiomics model was established using support vector machine (SVM) and random forest (RF) models. The diagnostic efficiency of this model was evaluated using the receiver operating characteristic. A total of 851 features were obtained by feature extraction, and 311 features were screened out using the <i>t</i>-test and Mann-Whitney <i>U</i> test. The dimensionality reduction was performed on the remaining features using LASSO. Finally, seven features were included to establish the diagnostic prediction model. In the testing group, the AUC, accuracy and specificity of the SVM model were higher than those of the RF model (0.841 [0.815-0.867] vs. 0.791 [0.759-0.824], 96.6% vs. 93.1%, and 100.0% vs. 90.5%, respectively). However, the sensitivity of the SVM model was lower than that of the RF model (88.9% vs. 100.0%). In this study, a prediction model based on ultrasound radiomics was developed to distinguish FAVA from VM. The study achieved high classification accuracy, sensitivity, and specificity. SVM model is superior to RF model and provides a new perspective and tool for clinical diagnosis.

The differentiation between traumatic and degenerative rotator cuff tears (RCTs remains a diagnostic challenge with significant implications for treatment planning. While magnetic resonance imaging (MRI) is standard practice, traditional radiological interpretation has shown limited reliability in distinguishing these etiologies. This study evaluates the potential of artificial intelligence (AI) models, specifically a multimodal vision transformer (ViT), to differentiate between traumatic and degenerative RCT. In this retrospective, single-centre study, 99 shoulder MRIs were analysed from patients who underwent surgery at a specialised university shoulder unit between 2016 and 2019. The cohort was divided into training (n = 79) and validation (n = 20) sets. The traumatic group required a documented relevant trauma (excluding simple lifting injuries), previously asymptomatic shoulder and MRI within 3 months posttrauma. The degenerative group was of similar age and injured tendon, with patients presenting with at least 1 year of constant shoulder pain prior to imaging and no trauma history. The ViT was subsequently combined with demographic data to finalise in a multimodal ViT. Saliency maps are utilised as an explainability tool. The multimodal ViT model achieved an accuracy of 0.75 ± 0.08 with a recall of 0.8 ± 0.08, specificity of 0.71 ± 0.11 and a F1 score of 0.76 ± 0.1. The model maintained consistent performance across different patient subsets, demonstrating robust generalisation. Saliency maps do not show a consistent focus on the rotator cuff. AI shows potential in supporting the challenging differentiation between traumatic and degenerative RCT on MRI. The achieved accuracy of 75% is particularly significant given the similar groups which presented a challenging diagnostic scenario. Saliency maps were utilised to ensure explainability, the given lack of consistent focus on rotator cuff tendons hints towards underappreciated aspects in the differentiation. Not applicable.

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion imaging, diffusion kurtosis imaging, diffusion-tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water fraction and hybrid multidimensional MRI metrics. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative lesion size and shape features can be combined with the aforementioned techniques and can be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms and use cases.

Purpose: Prenatal ultrasound is a key tool in evaluating fetal structural development and detecting abnormalities, contributing to reduced perinatal complications and improved neonatal survival. Accurate identification of standard fetal torso planes is essential for reliable assessment and personalized prenatal care. However, limitations such as low contrast and unclear texture details in ultrasound imaging pose significant challenges for fine-grained anatomical recognition. Methods: We propose a novel Multi-Contrast Fusion Module (MCFM) to enhance the model's ability to extract detailed information from ultrasound images. MCFM operates exclusively on the lower layers of the neural network, directly processing raw ultrasound data. By assigning attention weights to image representations under different contrast conditions, the module enhances feature modeling while explicitly maintaining minimal parameter overhead. Results: The proposed MCFM was evaluated on a curated dataset of fetal torso plane ultrasound images. Experimental results demonstrate that MCFM substantially improves recognition performance, with a minimal increase in model complexity. The integration of multi-contrast attention enables the model to better capture subtle anatomical structures, contributing to higher classification accuracy and clinical reliability. Conclusions: Our method provides an effective solution for improving fetal torso plane recognition in ultrasound imaging. By enhancing feature representation through multi-contrast fusion, the proposed approach supports clinicians in achieving more accurate and consistent diagnoses, demonstrating strong potential for clinical adoption in prenatal screening. The codes are available at https://github.com/sysll/MCFM.

In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist's eye gaze has distinct patterns that capture both fine-grained and coarser level disease related information. While interpreting an image, a radiologist's attention varies throughout the duration; it is critical to incorporate this into a deep learning framework to improve automated image interpretation. Another important aspect of visual attention is that apart from looking at major/obvious disease patterns, experts also look at minor/incidental findings (few of these constituting long-tailed classes) during the course of image interpretation. GazeLT harnesses the temporal aspect of the visual search process, via an integration and disintegration mechanism, to improve long-tailed disease classification. We show the efficacy of GazeLT on two publicly available datasets for long-tailed disease classification, namely the NIH-CXR-LT (n=89237) and the MIMIC-CXR-LT (n=111898) datasets. GazeLT outperforms the best long-tailed loss by 4.1% and the visual attention-based baseline by 21.7% in average accuracy metrics for these datasets. Our code is available at https://github.com/lordmoinak1/gazelt.