Chenwang Jin

Background: Acute exacerbation (AE) is the most lethal event of idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD). This exploratory study compared clinical, radiomics, and combined predictor strategies across five survival-learning algorithms for survival risk stratification. Methods: We retrospectively analyzed 78 patients with AE-IIM-ILD and divided them into a training cohort (n = 54; 20 deaths) and a held-out internal testing cohort (n = 24; 9 deaths), stratified by observed mortality event status. Three predictor strategies were evaluated: a clinical strategy comprising PaO ₂ /FiO ₂ ratio, neutrophil percentage (NEU%), and arthritis-related symptoms; a radiomics strategy comprising two selected radiomics features; and a combined strategy comprising PaO ₂ /FiO ₂ ratio, arthritis-related symptoms, and a composite radiomics score (Rad-score). Each strategy was evaluated using five survival-learning algorithms. Harrell’s concordance index (C-index) was the primary discrimination metric, with mean time-dependent area under the curve (AUC) used as a secondary measure. Results: During the 1-year follow-up, 29 deaths were observed. Performance rankings varied across predictor strategies, algorithms, and evaluation metrics. The combined-Random Survival Forest configuration demonstrated the highest training-cohort C-index among the 15 configurations. Its mean time-dependent AUC was 0.923 (95% CI: 0.860–0.970) in the training cohort and 0.915 (95% CI: 0.736–0.986) in the testing cohort, with corresponding C-indices of 0.874 (95% CI: 0.803–0.940) and 0.842 (95% CI: 0.673–0.971), respectively. The model stratified patients into high- and low-risk groups with distinct observed survival trajectories. Conclusion: In this exploratory cohort, integrating selected clinical variables with a quantitative CT Rad-score using RSF showed promising numerical discrimination and risk-stratification performance. These findings suggest that clinical and quantitative imaging information may provide complementary prognostic information after AE-IIM-ILD.

Cognitive impairment is a common but poorly understood comorbidity in chronic obstructive pulmonary disease (COPD). Although gray matter abnormalities have

Background/Objectives: Radiological expertise draws on semantic knowledge and perceptual–cognitive mechanisms that support diagnostic reasoning. Early radiological training is a formative period when key cognitive processes begin to integrate. Nevertheless, how the brain pattern of early radiological expertise reorganizes during the first weeks of clinical exposure remains unknown, as prior work has relied mainly on cross-sectional designs comparing mature experts to beginners. Methods: We therefore conducted a longitudinal resting-state fMRI study in radiology interns (n = 43; 41 valid) scanned before and after short-term training. Behavioral performance improved significantly after training (p < 0.01). Regional homogeneity (ReHo) was computed for 246 Brainnetome ROIs for each subject. Results: Using a Support Vector Machine (SVM)-based recursive feature elimination (RFE) pipeline, 14 of these 246 features were identified as most discriminative, spanning regions involved in visual, semantic, memory, attentional, and decision-making processes. An SVM trained on these features effectively differentiated pre- and post-training brain states (training set: 86.67% accuracy, AUC = 0.97; validation set: 81.82% accuracy, AUC = 0.72). Conclusions: The observed neuroplastic changes provide direct evidence that multidimensional cognitive functions reorganize early in radiological expertise development and offer neural targets to inform evidence-based curriculum design, personalized training, and brain-targeted interventions (e.g., neuromodulation or neurofeedback) in radiology education.

Diagnosing and classifying the imaging patterns of idiopathic inflammatory myopathies-associated interstitial lung disease (IIM-ILD) is a crucial but challenging task requiring specialized physicians’ expertise. This study aims to develop and validate a deep-learning model to assist in classifying the IIM-ILD imaging patterns. The study retrospectively collected 629 patients with IIM-ILD and split them into a training set (361 subjects), an internal testing set (156 subjects) from January 2015 to December 2019, and a temporal external validation set (112 subjects) from January 2020 to August 2022. A deep-learning model was developed and validated to categorize IIM-ILD imaging patterns using HRCT images. Class activation mapping and label smoothing strategy were utilized to enhance the interpretability and performance. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1-score. The proposed deep-learning model achieved an average AUC of 0.885, accuracy of 0.724, and F1-score of 0.706 in the internal testing set, and an AUC of 0.835, accuracy of 0.795, and F1-score of 0.727 in the temporal external validation set. In summary, the deep-learning model can effectively classify the multiple imaging patterns of IIM-ILD, showing potential as a valuable radiological diagnostic support system in clinical practice.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease with various phenotypes. Registered inspiratory and expiratory CT images can generate the parametric response map (PRM) that characterizes phenotypes’ spatial distribution and proportions. However, increased radiation dosage, scan time, quality control, and patient cooperation requirements limit the utility of PRM. This study aims to synthesize a PRM using only inspiratory CT scans. First, a CycleGAN with perceptual loss and a multiscale discriminator (MPCycleGAN) is proposed and trained to synthesize registered expiratory CT images from inspiratory images. Next, a strategy named InspirationOnly is introduced, where synthesized images replace actual expiratory CT images. The image synthesizer outperformed state-of-the-art models, achieving a mean absolute error of 105.66 ± 36.64 HU, a peak signal-to-noise ratio of 21.43 ± 1.87 dB, and a structural similarity of 0.84 ± 0.02. The intraclass correlation coefficients of emphysema, fSAD, and normal proportions between the InspirationOnly and ground truth were 0.995, 0.829, and 0.914, respectively. The proposed MPCycleGAN enables the InspirationOnly strategy to yield PRM using only inspiratory CT. The estimated COPD phenotypes are consistent with those from dual-phase CT and correlated with the spirometry parameters. This offers a potential tool for characterizing phenotypes of COPD, particularly when expiratory CT images are unavailable. Graphical Abstract

Background: Several functional magnetic resonance imaging (fMRI) investigations of patients with vestibular migraine (VM) have revealed abnormal functionality in different networks, indicating that VM is related to alterations in brain function. We sought to investigate the resting-state functional connectivity (FC) patterns during the interictal period in VM by combining data-driven voxel-wise degree centrality (DC) calculations and seed-based FC analyses, and thereby determine the associations between cerebral function and clinical symptoms.Methods: Thirty-eight patients with VM and 33 matched normal controls were recruited. DC was calculated and compared between the groups, and the FC of locations showing DC alterations was further tested using a seed-based technique. The participants’ clinical indicators were correlated with the DC and FC values of the brain areas.Results: In contrast to the control group, the VM group showed considerably lower DC values in the bilateral medial prefrontal cortex (mPFC) and significantly higher DC values in the right occipital lobe. In the seed-based FC analyses, patients with VM demonstrated fewer connections of the bilateral mPFC with the bilateral posterior cingulate cortex, right parahippocampus, right cerebellar posterior lobe, bilateral cuneus, and left precuneus. In addition, clinical data from patients, such as pain intensity, episode frequency, and the Dizziness Handicap Inventory score, were negatively related to these FC and DC impairments.Conclusion: Our findings showed changes in the default mode network and visual cortex in patients with VM, providing further insights into the complexity of the mechanisms underlying VM.

Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis