Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of medicinal chemistry
SMDNet: A Self-Training-Aware and Multi-Modal-Adaptive Deep Learning Network for Low-Data Aβ42 Probe Design and Optimization
Amyloid-β (Aβ) plaque accumulation is a crucial hallmark of Alzheimer's disease, and fluorescence imaging can support disease diagnosis and monitoring. However, Aβ42 probe development is often hindered by trial-and-error experiments due to subtle structure-property effects. Here, we developed SMDNet, a self-training-aware and multimodal-adaptive deep learning (DL) framework for low-data Aβ42 probe design and optimization. SMDNet combines iterative self-training with confidence-aware and distribution-aware sampling to improve data quality, while integrating molecular graphs, fingerprints and protein descriptors through cross-attention and protein-conditional adaptive layer normalization. Ablation studies, external validation and generalization analyses confirmed the strong predictive ability of SMDNet. Interpretability analyses further highlighted chemically meaningful substructures associated with model predictions. As a proof of concept, SMDNet guided the rational design of five ThT-derived probe candidates, with TA3 showing favorable binding affinity and high-contrast imaging performance. Additional validation on coumarin- and naphthalimide-based candidates further supported useful predictive discrimination across distinct fluorescent scaffold classes.
24 July 2026
Read appraisal →Physics in medicine and biology
Four-dimensional on-beam computed tomography reconstruction using projection-difference images
Purpose.Accurate verification of delivered dose requires anatomical information during the treatment beam delivery. However, conventional cone-beam computed tomography (CBCT) is typically acquired prior to beam delivery and fails to account for intrafractional tumor shift/drift. This study aims to develop a 4D on-beam computed tomography (4D OBCT) framework to capture anatomical states during irradiation.Materials and methods.A thoracic phantom with rigid tumor motion was used to develop a conformal arc treatment plan. During delivery, the target was displaced by 1 cm superior to its planned position to simulate a baseline shift, and megavoltage transmission data were acquired using an electronic portal imaging device. Transmission data corresponding to the planned tumor position were obtained from the planning CT (pCT) via Monte Carlo simulation. Volumetric difference images were reconstructed from the differences between the measured and simulated transmission data for each phase. Subsequently, the images were superimposed onto the corresponding phases of the 4D pCT to obtain 4D OBCT. For evaluation, we compared 4D OBCT with 4D pCT (ground-truth) and 4D CBCT which were acquired after the target displacement. A dosimetric performance was evaluated by recalculating 4D dose distributions on the three images and comparing in dose-volume histograms and gamma indices.Results.4D OBCT achieved lower root mean square error than 4D CBCT in both the lung (58.46 HU vs 74.37 HU) and chest wall regions (23.78 HU vs 66.84 HU). This quantitative accuracy was also reflected in the dosimetric comparison. Under 2%/2 mm and 1%/1 mm criteria, 4D OBCT achieved gamma pass rates of 99.76% and 99.08%, respectively, whereas 4D CBCT yielded 72.50% and 57.77%, respectively.Conclusions.The proposed 4D OBCT framework successfully reconstructed accurate phase-resolved images of the target during irradiation. By providing high-resolution volumetric images that reflect intrafractional motion, 4D OBCT could provide a robust solution for precise post-delivery dosimetric verification and adaptation.
12 July 2026
Read appraisal →Journal of medical Internet research
Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis
BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns. OBJECTIVE: This study aimed to comprehensively evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based AI models in predicting biochemical recurrence (BCR) of PCa. METHODS: Systematic searches were conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases up to January 13, 2026. Studies were included that involved participants diagnosed with PCa, used MRI-based AI for predicting BCR, and had clearly defined reference standards. The quality of the included studies was assessed using the PROBAST+AI tool. A bivariate random effects model was used to pool sensitivity, specificity, and area under the curve (AUC) statistics. RESULTS: A total of 28 studies were included, with 2623 patients in internal validation and 1134 patients in external validation. Diagnostic contingency tables were reconstructed from published performance metrics for most studies, while others were extracted from receiver operating characteristic curves due to the lack of direct reporting. In the internal validation set, pooled sensitivity was 0.80 (95% CI 0.73-0.86; prediction interval [PI] 0.48-0.99), specificity was 0.83 (95% CI 0.77-0.89; PI 0.49-1.00), and AUC was 0.86 (95% CI 0.83-0.89; PI 0.74-0.99). In the external validation set, pooled sensitivity was 0.82 (95% CI 0.72-0.91; PI 0.54-0.99), specificity was 0.83 (95% CI 0.71-0.92; PI 0.49-1.00), and AUC was 0.84 (95% CI 0.79-0.90; PI 0.70-0.98). No statistically significant differences were observed between internal and external validation in sensitivity (P=.73), specificity (P>.99), AUC (P=.53), or diagnostic odds ratio (P=.98). Medical Net and Extreme Gradient Boosting achieved the highest sensitivity and AUC, whereas multiple kernel learning and support vector machine had the highest specificity. Subgroup and meta-regression analyses suggested that AI method, model type, timing of MRI acquisition, and treatment modality may contribute to heterogeneity. CONCLUSIONS: This meta-analysis innovatively realizes the quantitative direct comparison of MRI-based AI model performance between internal and external validation cohorts for PCa BCR prediction. It comprehensively evaluates AI performance across diverse PCa treatment modalities and integrates machine learning and deep learning approaches. For the field, it identifies key sources of performance heterogeneity (eg, MRI acquisition timing and treatment modality) and quantifies the sensitivity-specificity trade-off in integrated radiomic-clinical models, advancing the systematic understanding of MRI-based AI for BCR prediction. In real-world practice, it provides actionable guidance to prioritize pretreatment MRI for AI model development and clinical BCR assessment and underscores the urgent need for standardized imaging protocols and prospective multicenter studies, laying a foundation for the safe clinical translation of these AI tools as adjunctive decision support instruments.
8 July 2026
Read appraisal →European journal of radiology
Deep learning-based segmentation and quantification of pulmonary masses on apparent diffusion coefficient maps: a multicentre reproducibility study
OBJECTIVES: To develop and validate a deep learning model for automatic segmentation of pulmonary masses on apparent diffusion coefficient (ADC) maps and to assess repeatability of automated ADC quantification. METHODS: We proposed ADCSegNet, a deep learning model tailored to pulmonary ADC maps, trained and tested on ADC maps from centre 1 (303 MRI examinations) and externally evaluated on datasets from centre 2 (70 examinations) and centre 3 (12 examinations) for generalisability. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC). Agreement between model-derived and manual ADC measurements (senior and junior radiologists) was examined using Bland-Altman analysis, the intraclass correlation coefficient [ICC(2,1)], and the reproducibility coefficient (RDC) following QIBA profiles. Repeat-scan stability was assessed on an independent prospective test-retest dataset (22 × 2 examinations) using Lin's concordance correlation coefficient (CCC). RESULTS: ADCSegNet achieved DSCs of 0.843 internally and 0.701 externally, outperforming nnU-Net and other contemporary backbones. Agreement was highest between the model and the senior radiologist; for mean ADC, the mean difference was 0.00 (95% limits of agreement, -0.13 to 0.13) × 10⁻3 mm2/s, exceeding model-junior and inter-radiologist agreement. ICC(2,1) exceeded 0.75 for most ADC metrics, and RDC confirmed high reproducibility for key metrics (mean ADC RDC%: 12.98% for model-senior vs. 22.87% for senior-junior radiologist). Test-retest analyses showed higher repeatability of ADCSegNet than radiologists for core metrics such as mean ADC (CCC 0.931 vs. 0.908). CONCLUSIONS: ADCSegNet enables accurate segmentation of pulmonary masses on ADC maps and provides reproducible automated ADC quantification, outperforming state-of-the-art backbone models while showing strong agreement with expert readers and stable performance on repeat scans.
4 July 2026
Read appraisal →Biomedical physics & engineering express
Challenges and strategies in fusion of probes and imaging equipment for neural imaging
Recent years have seen significant progress in imaging technology, which has deepened our understanding of the brain. However, challenges remain in detecting nanoscale structures like the extracellular space and mapping complex neural networks. Various chemical probes have been developed to improve the imaging precision of microstructures and functional imaging. Yet, the integration of imaging technologies and probes stays underdeveloped. Differences in disciplinary paradigms, unclear quantitative and qualitative relationships, and a lack of optimized cross-functional orchestration are the main causes of this problem. In this paper, we propose potential strategies to achieve deep integration of probes and equipment for high-quality imaging. Specifically, we advocate for three main approaches: (1) designing imaging sequences driven by probe properties, (2) constructing quantitative dose-effect models, and (3) leveraging artificial intelligence for system optimization. Importantly, these strategies establish an adaptable framework that will facilitate the seamless integration of emerging probe and equipment technologies in future brain science research. Ultimately, this enhanced integration promises to advance our knowledge of brain function and structure.
29 June 2026
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