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Rationale and Objectives: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs)., Methods: A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA)., Results: Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models., Conclusion: Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Biomarker-targeted molecular imaging holds promise for early detection of pancreatic cancer. The aim of this study was to design and evaluate a plectin-1 targeted multi-functional nanoparticle probe for pancreatic cancer imaging., Methods: 1,2-Distearoyl-sn-glycero-3-phosphoethanolamine-N-amino(polyethylene glycol) (DSPE-PEG-NH2)-modified superparamagnetic iron oxide (Fe 3 O 4 ) nanoparticles (SPION) were conjugated with plectin-1 antibody and/or Cy7 to create the multi-functional targeted nanoparticle targeted probe (Plectin-SPION-Cy7) or non-targeted probe (SPION-Cy7). Pancreatic carcinoma cell lines expressing plectin-1 were cultured with the targeted or control probes and then were imaged using confocal laser scanning microscopy and magnetic resonance imaging (MRI). Accumulations of the nanoparticles in pancreatic tumor xenografted mice were determined by MRI and fluorescence imaging., Results: In vitro optical imaging and MRI showed that the targeted nanoparticles were highly accumulated in MIAPaCa2 and XPA-1 carcinoma cells but not in non-carcinoma MIN6 cells, which was further confirmed by Prussian blue staining. In vivo MRI showed a significant T2 signal reduction. Prussian blue staining further confirmed that the plectin-1 targeted nanoparticles were highly accumulated in the tumor mass but not in normal pancreatic tissues, or in the liver and kidney, and few nanoparticles were observed in the tumors of mice injected with SPION-Cy7., Conclusions: Our data demonstrate that plectin-1 targeted fluorescence and MR dual-functional nanoparticle can visualize pancreatic cancer, and it has great potential to be used with various imaging devices for pancreatic cancer detection., (Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.)

Background: Low-grade fibromyxoid sarcoma (LGFMS) is a distinctive slow growing soft tissue neoplasm, mostly affecting young individuals with no gender difference. It usually arises in deep soft tissue of the lower limbs and trunk, but few cases of LGFMS located in pelvis have been reported., Case Presentation: We describe the magnetic resonance imaging(MRI) features of LGFMS located in the anterior pelvic wall of a 21-year-old female and correlate them with clinicopathological features. The tumor was completely resected and there is no recidivism during the follow-up one year., Conclusions: We report on the radiological findings of LGFMS with histological correlation. Awareness of the imaging features may be useful for the diagnosis of LGFMS and helpful to distinguish among mimics.

Background and Purpose: Imaging studies of traumatic brain injury demonstrate that the cerebellum is often affected. We aim to examine fractional anisotropy alteration in acute-phase mild traumatic brain injury patients in cerebellum-related white matter tracts. Materials and Methods: This prospective study included 47 mild traumatic brain injury patients in the acute stage and 37 controls. MR imaging and neurocognitive tests were performed in patients within 7 days of injury. White matter integrity was examined by using diffusion tensor imaging. We used three approaches, tract-based spatial statistics, graphical-model-based multivariate analysis, and region-of-interest analysis, to detect altered cerebellar white matter integrity in mild traumatic brain injury patients. Results: Results from three analysis methods were in accordance with each other, and suggested fractional anisotropy in the middle cerebellar peduncle and the pontine crossing tract was changed in the acute-phase mild traumatic brain injury patients, relative to controls (adjusted p-value < 0.05). Higher fractional anisotropy in the middle cerebellar peduncle was associated with worse performance in the fluid cognition composite (r = -0.289, p-value = 0.037). Conclusion: Altered cerebellar fractional anisotropy in acute-phase mild traumatic brain injury patients is localized in specific regions and statistically associated with cognitive deficits detectable on neurocognitive testing. [ABSTRACT FROM AUTHOR]

Polyethylene glycol (PEG)-coated superparamagnetic iron oxide nanoparticles (PEG·SPIONs) were prepared by a facile one-pot approach. The synthesized PEG·SPIONs were found to be uniform in size with an average hydrodynamic diameter of 11.7nm. PEG·SPIONs exhibited excellent dispersibility in water, colloidal stability, and biocompatibility. The magnetic resonance imaging (MRI) properties of PEG·SPIONs were characterized both in vitro and in vivo. The dual contrast both in T1 and T2-weighted imaging was well enhanced with longitudinal and transverse relaxivity (r1, r2) of 35.92s−1 per mM of Fe3+ and 206.91s−1 per mM of Fe3+ respectively. In vivo T2-weighted MRI shows pronounced enhancement in the liver and spleen but not in T1-weighted MRI. Accumulations of nanoparticles were found primarily in the liver, spleen, and intestine, while much lower uptake in the kidney, heart, and lungs. A gradual excretion of PEG·SPIONs was observed via hepatobiliary (HB) processing over a period of 14days. The toxicity of PEG·SPIONs was also evaluated in vitro and in vivo. PEG·SPIONs were found to be biocompatible by investigating organ tissues after hematoxylin–eosin staining. The conclusion of the study indicates a high potential of PEG·SPIONs in medical MRI. [ABSTRACT FROM AUTHOR]