To explore the association between pretherapeutic contrast-enhanced cone beam breast CT (CE-CBBCT) features and pathological complete response (pCR), and to develop a predictive model that integrates clinicopathological and imaging features. In this prospective study, a cohort of 200 female patients who underwent CE-CBBCT prior to neoadjuvant therapy and surgery was divided into train (n=150) and test (n=50) sets in a 3:1 ratio. Optimal predictive features were identified using univariate logistic regression and recursive feature elimination with cross-validation (RFECV). Models were constructed using XGBoost and evaluated through the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. The performance of combined model was further evaluated across molecular subtypes. Feature significance within the combined model was determined using the SHapley Additive exPlanation (SHAP) algorithm. The model incorporating three clinicopathological and six CE-CBBCT imaging features demonstrated robust predictive performance for pCR, with area under curves (AUCs) of 0.924 in the train set and 0.870 in the test set. Molecular subtype, spiculation, and adjacent vascular sign (AVS) grade emerged as the most influential SHAP features. The highest AUCs were observed for HER2-positive subgroup (train: 0.935; test: 0.844), followed by luminal (train: 0.841; test: 0.717) and triple-negative breast cancer (TNBC; train: 0.760; test: 0.583). SHAP analysis indicated that spiculation was crucial for luminal breast cancer prediction, while AVS grade was critical for HER2-positive and TNBC cases. Integrating clinicopathological and CE-CBBCT imaging features enhanced pCR prediction accuracy, particularly in HER2-positive cases, underscoring its potential clinical applicability.

This study aimed to develop a predictive model for peritoneal metastasis (PM) in ovarian cancer using a combination radiomics and clinical biomarkers to improve diagnostic accuracy. This retrospective cohort study of 619 ovarian cancer patients involved demographic data, radiomics, O-RADS standardized description, clinical biomarkers, and histological findings. Radiomics features were extracted using 3D Slicer and Pyradiomics, with selective feature extraction using Least Absolute Shrinkage and Selection Operator regression. Model development and validation were carried out using logistic regression and machine learning methods RESULTS: Interobserver agreement was high for radiomics features, with 1049 features initially extracted and 7 features selected through regression analysis. Multi-modal information such as Ascites, Fallopian tube invasion, Greatest diameter, HE4 and D-dimer levels were significant predictors of PM. The developed radiomics nomogram demonstrated strong discriminatory power, with AUC values of 0.912, 0.883, and 0.831 in the training, internal test, and external test sets respectively. The nomogram displayed superior diagnostic performance compared to single-modality models. The integration of multimodal information in a predictive model for PM in ovarian cancer shows promise for enhancing diagnostic accuracy and guiding personalized treatment. This multi-modal approach offers a potential strategy for improving patient outcomes in ovarian cancer management with PM.

Fibroblasts play essential roles in cancer progression, exhibiting activation states that can either promote or inhibit tumor growth. Understanding these differential activation states is critical for targeting the tumor microenvironment (TME) in cancer therapy. However, traditional molecular markers used to identify cancer-associated fibroblasts are limited by their co-expression across multiple fibroblast subtypes, making it difficult to distinguish specific activation states. Morphological and motility characteristics of fibroblasts reflect their underlying gene expression patterns and activation states, making these features valuable descriptors of fibroblast behavior. This study proposes an artificial intelligence-based classification framework to identify and characterize differentially activated fibroblasts by analyzing their morphodynamic and motile features. We extract these features from label-free live-cell imaging data of fibroblasts co-cultured with breast cancer cell lines using deep learning and machine learning algorithms. Our findings show that morphodynamic and motile features offer robust insights into fibroblast activation states, complementing molecular markers and overcoming their limitations. This biophysical state-based cellular classification framework provides a novel, comprehensive approach for characterizing fibroblast activation, with significant potential for advancing our understanding of the TME and informing targeted cancer therapies.

Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response. This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs. Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities, ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool. Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking. ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nevertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and multimodal studies to enhance clinical applicability. Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk of bias.

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classification. We demonstrate that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve polyp classification performance by deep learning models. We invert pairs of images with the same label to a semantically rich and disentangled latent space and manipulate latent representations to produce new synthetic images. These synthetic images maintain the same label as the input pairs. We perform image modality translation (style transfer) between white light and narrow-band imaging (NBI). We also generate realistic synthetic lesion images by interpolating between original training images to increase the variety of lesion shapes in the training dataset. Our experiments show that GAN inversion can produce multiple colonoscopy data augmentations that improve the downstream polyp classification performance by 2.7% in F1-score and 4.9% in sensitivity over other methods, including state-of-the-art data augmentation. Testing on unseen out-of-domain data also showcased an improvement of 2.9% in F1-score and 2.7% in sensitivity. This approach outperforms other colonoscopy data augmentation techniques and does not require re-training multiple generative models. It also effectively uses information from diverse public datasets, even those not specifically designed for the targeted downstream task, resulting in strong domain generalizability. Project code and model: https://github.com/DurrLab/GAN-Inversion.

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

To demonstrate that a deep learning (DL) model can be employed as a teaching tool to improve radiologists' ability to perform a subsequent imaging task without additional artificial intelligence (AI) assistance at time of image interpretation. Three human readers were tasked to categorize 50 frontal knee radiographs by male and female sex before and after reviewing data derived from our DL model. The model's high accuracy in performing this task was revealed to the human subjects, who were also supplied the DL model's resultant occlusion interpretation maps ("heat maps") to serve as a teaching tool for study before final testing. Two weeks later, the three human readers performed the same task with a new set of 50 radiographs. The average accuracy of the three human readers was initially 0.59 (95%CI: 0.59-0.65), not statistically different than guessing given our sample skew. The DL model categorized sex with 0.96 accuracy. After study of AI-derived "heat maps" and associated radiographs, the average accuracy of the human readers, without the direct help of AI, on the new set of radiographs increased to 0.80 (95%CI: 0.73-0.86), a significant improvement (p=0.0270). AI-derived data can be used as a teaching tool to improve radiologists' own ability to perform an imaging task. This is an idea that we have not before seen advanced in the radiology literature. AI can be used as a teaching tool to improve the intrinsic accuracy of radiologists, even without the concurrent use of AI.

This study aims to develop a deep learning model for a robust diagnosis of Carpal Tunnel Syndrome (CTS) based on comparative classification leveraging the ultrasound images of the thenar and hypothenar muscles. We recruited 152 participants, both patients with varying severities of CTS and healthy individuals. The enrolled patients underwent ultrasonography, which provided ultrasound image data of the thenar and hypothenar muscles from the median and ulnar nerves. These images were used to train a deep learning model. We compared the performance of our model with previous comparative methods using echo intensity ratio or machine learning, and non-comparative methods based on deep learning. During the training process, comparative guidance based on cosine similarity was used so that the model learns to automatically identify the abnormal differences in echotexture between the ultrasound images of the thenar and hypothenar muscles. The proposed deep learning model with comparative guidance showed the highest performance. The comparison of Receiver operating characteristic (ROC) curves between models demonstrated that the Comparative guidance was effective in autonomously identifying complex features within the CTS dataset. The proposed deep learning model with comparative guidance was shown to be effective in automatically identifying important features for CTS diagnosis from the ultrasound images. The proposed comparative approach was found to be robust to the traditional problems in ultrasound image analysis such as different cut-off values and anatomical variation of patients. Proposed deep learning methodology facilitates accurate and efficient diagnosis of CTS from ultrasound images.

This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict metachronous liver metastasis (MLM) in locally advanced rectal cancer (LARC) patients. A nomogram was developed by integrating multiple factors to enhance predictive accuracy. Retrospective data from 385 LARC patients across two centers were gathered. The data from Center 1 were split into a training set of 203 patients and an internal validation set of 87 patients, while Center 2 provided an external test set of 95 patients. K - means clustering was used on T2 - weighted images, and the region of interest was extended at different thicknesses. After feature extraction and selection, four machine - learning algorithms were utilized to build radiomics models. A nomogram was created by combining habitat radiomics, conventional radiomics, and clinical independent predictors. Model performance was evaluated by the AUC, and clinical utility was assessed through calibration curve and DCA. Habitat radiomics outperformed other single models in predicting MLM, with AUCs of 0.926, 0.864, and 0.851 in respective sets. The integrated nomogram achieved even higher AUCs of 0.959, 0.925, and 0.889. DCA and calibration curve analysis showed its high net benefit and good calibration. MRI - based habitat radiomics can effectively predict MLM in LARC patients. The integrated nomogram has optimal predictive performance and improves model accuracy significantly.

Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD. Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features. The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks. Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.