It is not rare to experience a biochemical recurrence (BCR) following radical prostatectomy (RP) for prostate cancer (PCa). It has been reported that early detection and management of BCR following surgery could improve survival in PCa. This study aimed to develop a nomogram integrating deep learning-based radiomic features and clinical parameters to predict 1-year BCR after RP and to examine the associations between radiomic scores and the tumor microenvironment (TME). In this retrospective multicenter study, two independent cohorts of patients (n = 349) who underwent RP after multiparametric magnetic resonance imaging (mpMRI) between January 2015 and January 2022 were included in the analysis. Single-cell RNA sequencing data from four prospectively enrolled participants were used to investigate the radiomic score-related TME. The 3D U-Net was trained and optimized for prostate cancer segmentation using diffusion-weighted imaging, and radiomic features of the target lesion were extracted. Predictive nomograms were developed via multivariate Cox proportional hazard regression analysis. The nomograms were assessed for discrimination, calibration, and clinical usefulness. In the development cohort, the clinical-radiomic nomogram had an AUC of 0.892 (95% confidence interval: 0.783--0.939), which was considerably greater than those of the radiomic signature and clinical model. The Hosmer-Lemeshow test demonstrated that the clinical-radiomic model performed well in both the development (P = 0.461) and validation (P = 0.722) cohorts. Decision curve analysis revealed that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone in both cohorts. Radiomic scores were associated with a significant difference in the TME pattern. Our study demonstrated the feasibility of a DWI-based clinical-radiomic nomogram combined with deep learning for the prediction of 1-year BCR. The findings revealed that the radiomic score was associated with a distinctive tumor microenvironment.

To evaluate the performance of machine learning (ML) models for the automated scoring of spinal MRI bone marrow oedema (BMO) in patients with axial spondyloarthritis (axSpA) and compare them with expert scoring. ML algorithms using SpineNet software were trained and validated on 3483 spinal MRIs from 686 axSpA patients across two clinical trial datasets. The scoring pipeline involved (i) detection and labelling of vertebral bodies and (ii) classification of vertebral units for the presence or absence of BMO. Two models were tested: Model 1, without manual segmentation, and Model 2, incorporating an intermediate manual segmentation step. Model outputs were compared with those of human experts using kappa statistics, balanced accuracy, sensitivity, specificity, and AUC. Both models performed comparably to expert readers, regarding presence vs absence of BMO. Model 1 outperformed Model 2, with an AUC of 0.94 (vs 0.88), accuracy of 75.8% (vs 70.5%), and kappa of 0.50 (vs 0.31), using absolute reader consensus scoring as the external reference; this performance was similar to the expert inter-reader accuracy of 76.8% and kappa of 0.47, in a radiographic axSpA dataset. In a non-radiographic axSpA dataset, Model 1 achieved an AUC of 0.97 (vs 0.91 for Model 2), accuracy of 74.6% (vs 70%), and kappa of 0.52 (vs 0.27), comparable to the expert inter-reader accuracy of 74.2% and kappa of 0.46. ML software shows potential for automated MRI BMO assessment in axSpA, offering benefits such as improved consistency, reduced labour costs, and minimised inter- and intra-reader variability. Clinicaltrials.gov, MEASURE 1 study (NCT01358175); PREVENT study (NCT02696031).

Novel antibody-drug conjugates highlight the benefits for breast cancer patients with low human epidermal growth factor receptor 2 (HER2) expression. This study aims to develop and validate a Vision Transformer (ViT) model based on dynamic contrast-enhanced MRI (DCE-MRI) to classify HER2-zero, -low, and -positive breast cancer patients and to explore its interpretability. The model is trained and validated on early enhancement MRI images from 708 patients in the FUSCC cohort and tested on 80 and 101 patients in the GFPH cohort and FHCMU cohort, respectively. The ViT model achieves AUCs of 0.80, 0.73, and 0.71 in distinguishing HER2-zero from HER2-low/positive tumors across the validation set of the FUSCC cohort and the two external cohorts. Furthermore, the model effectively classifies HER2-low and HER2-positive cases, with AUCs of 0.86, 0.80, and 0.79. Transcriptomics analysis identifies significant biological differences between HER2-low and HER2-positive patients, particularly in immune-related pathways, suggesting potential therapeutic targets. Additionally, Cox regression analysis demonstrates that the prediction score is an independent prognostic factor for overall survival (HR, 2.52; p = 0.007). These findings provide a non-invasive approach for accurately predicting HER2 expression, enabling more precise patient stratification to guide personalized treatment strategies. Further prospective studies are warranted to validate its clinical utility.

Brain tumor classification is critical for therapeutic applications that benefit from computer-aided diagnostics. Misdiagnosing a brain tumor can significantly reduce a patient's chances of survival, as it may lead to ineffective treatments. This study proposes a novel approach for classifying brain tumors in MRI images using Transfer Learning (TL) with state-of-the-art deep learning models: AlexNet, MobileNetV2, and GoogleNet. Unlike previous studies that often focus on a single model, our work comprehensively compares these architectures, fine-tuned specifically for brain tumor classification. We utilize a publicly available dataset of 4,517 MRI scans, consisting of three prevalent types of brain tumors-glioma (1,129 images), meningioma (1,134 images), and pituitary tumors (1,138 images)-as well as 1,116 images of normal brains (no tumor). Our approach addresses key research gaps, including class imbalance, through data augmentation and model efficiency, leveraging lightweight architectures like MobileNetV2. The GoogleNet model achieves the highest classification accuracy of 99.2%, outperforming previous studies using the same dataset. This demonstrates the potential of our approach to assist physicians in making rapid and precise decisions, thereby improving patient outcomes. The results highlight the effectiveness of TL in medical diagnostics and its potential for real-world clinical deployment. This study advances the field of brain tumor classification and provides a robust framework for future research in medical image analysis.

This proof-of-concept study introduces a novel, deep learning-based, parameter-free, automatic slice-tracking technique for continuous catheter tracking and visualization during MR-guided cardiac catheterization. The proposed sequence includes Calibration and Runtime modes. Initially, Calibration mode identifies the catheter tip's three-dimensional coordinates using a fixed stack of contiguous slices. A U-Net architecture with a ResNet-34 encoder is used to identify the catheter tip location. Once identified, the sequence then switches to Runtime mode, dynamically acquiring three contiguous slices automatically centered on the catheter tip. The catheter location is estimated from each Runtime stack using the same network and fed back to the sequence, enabling prospective slice tracking to keep the catheter in the central slice. If the catheter remains unidentified over several dynamics, the sequence reverts to Calibration mode. This artificial intelligence (AI)-based approach was evaluated prospectively in a three-dimensional-printed heart phantom and 3 patients undergoing MR-guided cardiac catheterization. This technique was also compared retrospectively in 2 patients with a previous non-AI automatic tracking method relying on operator-defined parameters. In the phantom study, the tracking framework achieved 100% accuracy/sensitivity/specificity in both modes. Across all patients, the average accuracy/sensitivity/specificity were 100 ± 0/100 ± 0/100 ± 0% (Calibration) and 98.4 ± 0.8/94.1 ± 2.9/100.0 ± 0.0% (Runtime). The parametric, non-AI technique and the proposed parameter-free AI-based framework yielded identical accuracy (100%) in Calibration mode and similar accuracy range in Runtime mode (Patients 1 and 2: 100%-97%, and 100%-98%, respectively). An AI-based prospective slice-tracking framework was developed for real-time, parameter-free, operator-independent, automatic tracking of gadolinium-filled balloon catheters. Its feasibility was successfully demonstrated in patients undergoing MRI-guided cardiac catheterization.

To create a Python-based toolbox to simulate commonly occurring artifacts for single voxel gamma-aminobutyric acid (GABA)-edited MRS data. The toolbox was designed to maximize user flexibility and contains artifact, applied, input/output (I/O), and support functions. The artifact functions can produce spurious echoes, eddy currents, nuisance peaks, line broadening, baseline contamination, linear frequency drifts, and frequency and phase shift artifacts. Applied functions combine or apply specific parameter values to produce recognizable effects such as lipid peak and motion contamination. I/O and support functions provide additional functionality to accommodate different kinds of input data (MATLAB FID-A.mat files, NIfTI-MRS files), which vary by domain (time vs. frequency), MRS data type (e.g., edited vs. non-edited) and scale. A frequency and phase correction machine learning model experiment trained on corrupted simulated data and validated on in vivo data is shown to highlight the utility of our toolbox. Data simulated from the toolbox are complementary for research applications, as demonstrated by training a frequency and phase correction deep learning model that is applied to in vivo data containing artifacts. Visual assessment also confirms the resemblance of simulated artifacts compared to artifacts found in in vivo data. Our easy to install Python artifact simulated toolbox SMART_MRS is useful to enhance the diversity and quality of existing simulated edited-MRS data and is complementary to existing MRS simulation software.

Multiple sclerosis (MS) diagnosis and monitoring rely heavily on accurate assessment of brain MRI biomarkers, particularly white matter hyperintensities (WMHs) and ventricular changes. Current segmentation approaches suffer from several limitations: they typically segment these structures independently despite their pathophysiological relationship, struggle to differentiate between normal and pathological hyperintensities, and are poorly optimized for anisotropic clinical MRI data. We propose a novel 2D pix2pix-based deep learning framework for simultaneous segmentation of ventricles and WMHs with the unique capability to distinguish between normal periventricular hyperintensities and pathological MS lesions. Our method was developed and validated on FLAIR MRI scans from 300 MS patients. Compared to established methods (SynthSeg, Atlas Matching, BIANCA, LST-LPA, LST-LGA, and WMH-SynthSeg), our approach achieved superior performance for both ventricle segmentation (Dice: 0.801+/-0.025, HD95: 18.46+/-7.1mm) and WMH segmentation (Dice: 0.624+/-0.061, precision: 0.755+/-0.161). Furthermore, our method successfully differentiated between normal and abnormal hyperintensities with a Dice coefficient of 0.647. Notably, our approach demonstrated exceptional computational efficiency, completing end-to-end processing in approximately 4 seconds per case, up to 36 times faster than baseline methods, while maintaining minimal resource requirements. This combination of improved accuracy, clinically relevant differentiation capability, and computational efficiency addresses critical limitations in current neuroimaging analysis, potentially enabling integration into routine clinical workflows and enhancing MS diagnosis and monitoring.

Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to tackle these problems by creating an automated brain tumor classification system utilizing MRI data obtained from many hospitals in Bangladesh. Advanced deep learning models, including VGG16, VGG19, and ResNet50, were utilized to classify glioma, meningioma, and various brain cancers. Explainable AI (XAI) methodologies, such as Grad-CAM and Grad-CAM++, were employed to improve model interpretability by emphasizing the critical areas in MRI scans that influenced the categorization. VGG16 achieved the most accuracy, attaining 99.17%. The integration of XAI enhanced the system's transparency and stability, rendering it more appropriate for clinical application in resource-limited environments such as Bangladesh. This study highlights the capability of deep learning models, in conjunction with explainable artificial intelligence (XAI), to enhance brain tumor detection and identification in areas with restricted access to advanced medical technologies.

MRI, as a mature diagnostic method in clinical application, is favored by doctors and patients, there are also insurmountable bottleneck problems. AI strategies such as multimodal imaging integration and machine learning are used to build an intelligent multimodal imaging system based on MRI data to solve the unmet clinical needs in various medical environments. This review systematically discusses the development of MRI-guided multimodal imaging systems and the application of intelligent multimodal imaging systems integrated with artificial intelligence in the early diagnosis of brain and cardiovascular diseases. The safe and effective deployment of AI in clinical diagnostic equipment can help enhance early accurate diagnosis and personalized patient care.

Mandibular reconstruction to restore mandibular continuity often relies on patient-specific implants and virtual surgical planning, but current implant designs rarely consider individual biomechanical demands, which are critical for preventing complications such as stress shielding, screw loosening, and implant failure. The inclusion of patient-specific masticatory muscle parameters such as cross-sectional area, vectors, and volume could improve implant success, but manual segmentation of these parameters is time-consuming, limiting large-scale analyses. In this study, a deep learning model was trained for automatic segmentation of eight masticatory muscles on MRI images. Forty T1-weighted MRI scans were segmented manually or via pseudo-labelling for training. Training employed 5-fold cross-validation over 1000 epochs per fold and testing was done on 10 manually segmented scans. The model achieved a mean Dice similarity coefficient (DSC) of 0.88, intersection over union (IoU) of 0.79, precision of 0.87, and recall of 0.89, demonstrating high segmentation accuracy. These results indicate the feasibility of large-scale, reproducible analyses of muscle volumes, directions, and estimated forces. By integrating these parameters into implant design and surgical planning, this method offers a step forward in developing personalized surgical strategies that could improve postoperative outcomes in mandibular reconstruction. This brings the field closer to truly individualized patient care.