Xiaowei Han

Background: Early identification of high-risk small bowel obstruction remains clinically challenging, especially when prompt intervention is needed to prevent bowel ischemia and related adverse outcomes. Methods: In this multicenter retrospective study, 274 patients with small bowel obstruction from three hospitals were included. Patients from Hospital A were randomly divided into training and test cohorts, and patients from the other two hospitals formed an external validation cohort. Radiomics features were extracted from venous-phase contrast-enhanced CT images, and logistic regression, naive Bayes, Extra Trees, Light Gradient Boosting Machine (LightGBM), and multilayer perceptron models were developed. Independent clinical predictors were identified and integrated with radiomics features to build a combined model. Results: LightGBM showed the most stable internal performance, with AUC values of 0.934 and 0.870 in the training and test cohorts, respectively. Lower diastolic blood pressure and bowel wall thickening were independent predictors of high-risk status. The combined model achieved AUC values of 0.942, 0.867, and 0.803 in the training, test, and external validation cohorts, respectively, with an external validation accuracy of 0.833 and sensitivity of 0.946. Conclusion: CT-based radiomics can identify patients with small bowel obstruction at high risk, and a combined model integrating radiomics and clinical predictors may support earlier risk stratification and intervention.

Objective: To develop and validate single−energy CT (135 kVp)−based radiomics and deep learning models for the non−invasive detection of periarticular monosodium urate (MSU) crystal deposition. Methods: This retrospective study included 605 patients with suspected periarticular MSU deposition, randomly split into a training cohort (n=425) and a validation cohort (n=180). Clinical variables and CT values were collected. Hand−crafted radiomics features were extracted from lesion ROIs and selected using t−test, Pearson correlation, LASSO, and mRMR. Deep features were derived from the maximum cross−sectional ROI using a ResNet50 transfer learning framework, and fused features underwent the same selection. A multilayer perceptron was used to construct the radiomics, deep learning radiomics (DLR), and combined (clinical + DLR) models, as well as a clinical−only model. Results: Serum uric acid (OR 1.003), age (OR 1.017), bone erosion (OR 3.476), and CT value (OR 0.993) were independently associated with MSU deposition (all P < 0.05). AUCs for the validation cohort were 0.820 (clinical), 0.912 (radiomics), 0.940 (DLR), and 0.942 (combined). The combined model achieved accuracy 0.889, sensitivity 0.905, and specificity 0.837. The DLR and combined models significantly outperformed the clinical model (DeLong P < 0.05), with no significant difference between them. Conclusions: The single−energy CT−based radiomics and deep learning radiomics models showed comparable performance for identifying periarticular MSU deposition (no statistically significant difference). The combined clinical−imaging model achieved numerically higher performance but did not significantly outperform the deep learning radiomics model.

The rapid advancements in nanotechnology and medical imaging have positioned magnetic resonance imaging (MRI)-guided magnetic micro/nanorobots (MNRs) as a promising platform for integrated diagnosis and treatment of neurological diseases. This review highlights recent progress in MNR development, focusing on fabrication techniques, driving mechanisms, multimodal imaging integration, AI-enabled navigation, blood-brain barrier (BBB) penetration, and disease-specific applications. We analyze biomimetic designs and functional materials for MNRs, including pH- and ROS-responsive degradable materials, liquid metals, and multifunctional coatings.We further examine MRI-based navigation, emphasizing gradient and rotational field control, closed-loop kinematics, and MRI system compatibility. The review also explores AI-assisted multimodal imaging, including MRI, PET, photoacoustic, and fluorescence techniques, alongside the role of deep learning and digital twin models in real-time tracking and path optimization. In addressing the critical challenge of BBB penetration, we review both conventional and emerging strategies. Finally, we assess preclinical findings on the application of MNRs in brain tumors, cerebrovascular diseases, and neurodegenerative disorders. We further highlight how MNR systems improve therapeutic outcomes by enhancing local drug concentrations, enabling spatiotemporally controlled release, and providing real-time imaging feedback, thereby addressing key failure modes of conventional systemic therapies. The review concludes with a critical assessment of translational challenges and a roadmap toward intelligent, personalized neurotherapeutics.

Objective: To develop an interpretable machine learning model utilizing ultrasound radiomics for distinguishing between granulomatous lobular mastitis and breast cancer. Methods: This retrospective study encompassed 237 patients who underwent preoperative breast ultrasound examinations and received pathological diagnoses of either granulomatous lobular mastitis or breast cancer at Quzhou People’s Hospital between April 2013 and April 2023. Radiomic features were extracted from the ultrasound images, and feature selection was conducted using intra-class correlation coefficients, Pearson correlation coefficients, and the least absolute shrinkage and selection operator regression. Machine learning models based on radiomics were constructed using Extremely Randomized Trees, Light Gradient Boosting Machine, and Random Forest. Additionally, a combined model was developed by integrating independent clinical predictors with the radiomics signature.The model’s performance was assessed using the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. To evaluate clinical utility, decision curve analysis was employed, while Shapley additive explanation was utilized to interpret model explainability. Results: A total of 1,161 radiomic features were extracted from each ultrasound image. Following Pearson correlation filtering, 135 features were retained, and 15 features were selected using the least absolute shrinkage and selection operator regression for model construction. The combined model, which integrated clinical factors with the radiomics signature, exhibited superior performance, achieving an AUC of 0.935 (95% CI : 0.902–0.969) in the training cohort and 0.833 (95% CI : 0.710–0.950) in the validation cohort. DCA indicated favorable clinical applicability. The Shapley additive explanation analysis shows that the imaging biomarker features lbp_3D_k_glszm_SmallAreaLowGrayLevelEmphasis, gradient_glcm_Imc2, gradient_glszm_ZoneEntropy, original_shape_Elongation, squareroot_glrlm_RunEntropy, and wavelet_HLH_glszm_LowGrayLevelZoneEmphasis have a strong correlation with the prediction of granulomatous lobular mastitis. Conclusion: The combined model, which incorporates ultrasound radiomics and clinical factors, exhibited significant efficacy in preoperatively differentiating granulomatous lobular mastitis from breast cancer. This non-invasive and interpretable methodology shows potential for enhancing diagnostic precision and guiding clinical decision-making.

This study aimed to investigate alterations in brain functional networks in Coal Workers’ Pneumoconiosis (CWP) using independent component analysis (

This study aimed to evaluate the value of contrast-enhanced computed tomography (CT) imaging radiomics in distinguishing malignant lesions from benign ones in the orbit. A retrospective analysis was conducted on CT imaging data from 139 patients with orbital tumor lesions, all of whom underwent contrast-enhanced CT scans within 2 weeks before diagnosis. Of these, 45 cases were benign lesions and 94 were malignant lesions. Radiomic features were extracted from the contrast-enhanced CT images, and 12 features were selected through the minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods. The selected features were used to build models using logistic regression, Naive Bayes Classifier (NaiveBayes), support vector machine (SVM), Extra Trees Classifier (ExtraTrees), and multilayer perceptron, with the best-performing model identified. Multivariate logistic regression was employed to identify clinical risk factors for malignant orbital lesions, and a nomogram model was developed by combining radiomic features and clinical variables. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve. Among the 3 machine learning models, the SVM model demonstrated the best predictive performance and robustness across datasets. Therefore, the SVM model was used to construct the nomogram. The nomogram achieved area under the receiver operating characteristic curve values of 0.957 and 0.833 in the training and testing cohorts, respectively, both of which were higher than 0.80. The performance of the nomogram was significantly superior to that of the clinical model (De-long test, P < .05), but no statistically significant difference was observed when compared to the radiomics model (De-long test, P > .05). Contrast-enhanced CT radiomics can effectively differentiate between malignant and benign orbital lesions. Both the nomogram and radiomics models exhibited high predictive performance, offering valuable insights for clinical decision-making.

Rationale: We report a rare case of rectal misplaced glands (RMG) with a 2-year disease course. Digital rectal examination identified a semi-circumferential mass approximately 4 cm from the anal verge. Imaging studies revealed irregular thickening of the middle and lower rectal walls with multiple mildly enlarged surrounding lymph nodes. Colonoscopic biopsy excluded a diagnosis of rectal cancer. Upon further evaluation of the patient’s history, it was determined that chronic inflammation caused by prolonged soapstick enema use led to ectopic glandular proliferation within the rectal wall. Patient concerns: A 72-year-old female presented with a 2-year history of altered bowel habits and a 1-week history of anal fullness. Diagnoses: Imaging studies, including computed tomography, magnetic resonance imaging, and positron emission tomography–computed tomography, suggested rectal cancer. However, colonoscopic biopsy pathology revealed extensive inflammatory exudates, necrotic tissue, focal granulation tissue formation, and severe compression artifact. Considering the patient’s 20-year history of soapstick enema use, clinicians diagnosed the condition as RMG. Interventions: The patient was advised to discontinue the use of soapstick enemas and was managed with regular follow-up and observation. Outcomes: Misdiagnosis of RMG as rectal cancer could lead to unnecessary radical surgical interventions. Raising awareness of this rare condition and accurately interpreting pathological findings are critical to improving patient outcomes. Lessons: This case highlights the importance of considering rare conditions like RMG in the diagnosis of rectal masses. A detailed patient history, such as prolonged soapstick enema use, was crucial in identifying the cause. Imaging may suggest rectal cancer, but biopsy findings showing inflammation helped differentiate RMG.

Objectives: This study aimed to investigate resting-state functional magnetic resonance imaging (fMRI) changes in Coal Workers’ Pneumoconiosis (CWP) patients with alcoholism using Amplitude of Low-Frequency Fluctuation (ALFF) and Functional Connectivity (FC) analyses.Materials and methods: A total of 60 patients with stage I CWP and 30 healthy controls were prospectively enrolled. The CWP patients were further divided into drinking and non-drinking groups. Resting-state fMRI scans were performed for all three groups, and correlations between abnormal ALFF signals, FC changes, and clinical baseline data were analyzed.Results: Compared to the control group, both the CWP drinking and non-drinking groups exhibited increased ALFF signals in the left orbitofrontal cortex, left frontal pole, right intracalcarine cortex, and right precuneus cortex, as well as decreased ALFF signals in the bilateral temporal pole and left occipital fusiform gyrus. Functional connectivity analysis revealed increased connectivity from the right orbitofrontal cortex to the right pars triangularis of the inferior frontal gyrus and from the right precuneus cortex to the right lingual gyrus in both CWP groups. Conversely, decreased connectivity was observed in the left and right frontal poles. Additionally, a positive correlation was found between ALFF values in the left temporal pole and PaO2 levels in the CWP drinking group (r = 0.369, p = 0.038). In the CWP non-drinking group, functional connectivity between the right precuneus cortex and the right lingual gyrus showed a negative correlation with FVC (r = −0.442, p = 0.027).Conclusion: CWP patients with alcoholism exhibit abnormal brain regions and functional connectivity associated with spontaneous neural activity changes. Significant correlations between specific brain regions and clinical indicators were identified. These findings provide a foundation for understanding neuroimaging changes in CWP patients with alcoholism through fMRI.