- A multimodal machine learning model for predicting reproductive outcomes in the rare congenital hypogonadotropic hypogonadism after treatment. [Journal Article]BMC Med Imaging. 2026 Aug 04; 26(1).BM
- CONCLUSIONS: The multimodal model, which integrated clinical factors with radiomic features from both ultrasound and magnetic resonance imaging, demonstrated favorable performance in predicting reproductive outcomes in patients with CHH after treatment.
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- Development and validation of a preoperative biparametric MRI-based habitat model for risk stratification of non-muscle-invasive bladder cancer. [Multicenter Study]
- CONCLUSIONS: Our biparametric MRI-based habitat models, particularly the SVM classifier, showed a trend toward improved preoperative risk stratification of NMIBC. These findings require large multicenter prospective validation before clinical individualized treatment decisions and follow-up planning.
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- Clinical value of a CT radiomics model for predicting myocardial injury after cancer therapy. [Journal Article]
- CONCLUSIONS: A pretreatment chest computed tomography-based myocardial radiomics model showed preliminary potential for identifying patients at increased risk of myocardial injury after cancer therapy. This approach may support low-burden baseline cardiotoxicity risk assessment using imaging data already acquired during routine cancer care. Larger external cohorts are required for validation.
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- Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study. [Journal Article]
- CONCLUSIONS: Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.
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- An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children. [Multicenter Study]
- CONCLUSIONS: A CT radiomics-based machine learning model integrating clinical features can accurately differentiate complicated from uncomplicated appendicitis in children, providing an interpretable framework to support preoperative surgical decision-making.
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- Whole-phase DCE-MRI radiomics for differentiating benign and malignant BI-RADS 4 breast lesions: a comparative machine learning study. [Journal Article]
- CONCLUSIONS: Whole-phase DCE-MRI radiomics showed favorable internal performance for differentiating benign from malignant BI-RADS 4 breast lesions. The SVM-based combined model performed favorably for the primary malignancy task, whereas the HER-2 analysis should be regarded only as a secondary exploratory observation requiring further validation.
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- Diagnostic accuracy of MRI for preoperative mapping of anal fistula: a prospective study of 204 patients with surgical correlation. [Journal Article]
- CONCLUSIONS: MRI is a highly accurate and valuable imaging modality for anal fistula. It allows comprehensive classification and detailed mapping of primary tracts and extensions. MRI provides comprehensive preoperative mapping and may facilitate individualized surgical planning, particularly for complex or recurrent anal fistulas.
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- Quantitative ultrasound evaluation of medial compartment knee osteoarthritis under standing weight-bearing condition. [Journal Article]
- CONCLUSIONS: Sonographic joint space width measured at 20° and 45° knee flexion under standing weight-bearing conditions yielded AUC values similar to those obtained from weight-bearing radiographs for diagnosing medial compartment KOA. Ultrasound may act as a potential supplementary imaging alternative to conventional standing radiograph for grading the severity of medial compartment KOA.
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- Gliomas grading prediction using multicenter 3T Magnetic Resonance Spectroscopy. [Multicenter Study]
- CONCLUSIONS: Multicenter data analysis confirmed the good classification accuracy of Cho/NAA and Cho/Cr in glioma grading, providing a reliable basis for clinical treatment and prognosis planning. This aligns with findings from other studies.
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- Construction and validation of a unified 3D convolutional neural network framework for automated identification of cervical spinal cord injury based on CT imaging. [Journal Article]
- CONCLUSIONS: Based on standardized data processing and network design, the 3D CNN effectively extracted latent spatial information associated with cervical spinal cord injury from CT images. The proposed framework demonstrated good stability and reproducibility across multicenter data and may serve as a preliminary research framework for CT-based CSCI risk evaluation, particularly in scenarios where MRI is unavailable or limited. Prospective validation is required before clinical deployment. It also provides a methodological reference for the standardized construction of deep learning models in medical imaging research.
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- Performance of GPT-4o in CT-based crista galli morphological classification: image-only versus morphometric dataset-driven approaches: a controlled comparative study. [Journal Article]
- CONCLUSIONS: Although ChatGPT has demonstrated promising performance in diagnostic radiology when provided with textual or numerical datasets, in this controlled CT-based crista galli morphologic classification task, GPT-4o demonstrated substantially lower performance when relying solely on image input compared with structured morphometric data. These findings suggest that the performance of multimodal LLMs may strongly depend on structured information. Further studies involving broader diagnostic settings are required before conclusions regarding clinical applicability can be drawn.
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- Myocardial injury patterns on cardiac magnetic resonance: diffuse myocardial damage with prominent septal involvement in adults with severe myocarditis requiring extracorporeal membrane oxygenation. [Journal Article]
- CONCLUSIONS: Survivors of severe myocarditis who required ECMO support presented diffuse myocardial injury as the dominant pattern on early post-weaning CMR examination, with the interventricular septum showing notably prominent lesions among all myocardial segments.
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- DSA radiomics and clinical features for hemorrhagic risk assessment in bAVM embolization. [Journal Article]
- CONCLUSIONS: A model integrating DSA radiomics and clinical features provides an exploratory approach to assessing the risk of EVT-related HCs in patients with bAVM. These findings suggest the potential value of DSA-derived radiomics for individualized peri-procedural risk assessment, although external validation is required before clinical application.
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- Diagnostic accuracy, time window estimation, and prognostic value of diffusion-weighted MRI in acute ischemic stroke: a systematic review and meta-analysis. [Systematic Review]
- CONCLUSIONS: DWI offers excellent diagnostic accuracy for AIS (sensitivity: 0.912, specificity: 0.959) but only modest performance for time estimation (sensitivity: 0.786, specificity: 0.727). DWI-derived biomarkers show good prognostic value (sensitivity: 0.702, specificity: 0.904), while AI and ultra-fast MRI protocols demonstrate promise for improving clinical utility.
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- CT-based radiomics combined with clinical features for non-invasive differentiation of hepatitis B and schistosomiasis cirrhosis: a multicenter study. [Multicenter Study]
- CONCLUSIONS: The proposed CT-based clinical-radiomics model provides a promising non-invasive approach for differentiating HBV-related and schistosomiasis-related cirrhosis. As a complementary imaging tool, it may assist etiological assessment in endemic regions when interpreted alongside routine clinical and laboratory evaluation. Further prospective multicenter studies are warranted to validate its clinical utility.
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