Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study

AIMS

The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics and clinical features to distinguish intraspinal schwannomas from meningiomas.

MATERIALS AND METHODS

This research analyzed the preoperative magnetic resonance (MR) images and clinical characteristics of 209 patients with intraspinal tumors who received tumor resection at three institutions. 159 individuals from institutions 1 and 2 were randomly assigned into a training group (n=111) and a test group (n=48) in a 7-3 ratio. A nomogram was constructed using the training cohort and was internally and externally verified in the test cohort and an independent validation cohort (n=50). Model performance was assessed utilizing the area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration curves.

RESULTS

The nomogram exhibited superior predictive efficacy in distinguishing between spinal schwannomas and meningiomas when compared to both the radiomics model and the clinical model. The nomogram yielded AUCs of 0.994, 0.962, and 0.949 in the training, test, and external validation cohorts, respectively, indicating its exceptional differentiating ability. The DCAs demonstrated that the nomogram yielded the best net benefit. The calibration curves indicated that the nomogram got good agreement between the predicted and the actual observation.

CONCLUSION

This research suggests that the nomogram incorporating clinical and radiomic features may be an effective auxiliary tool for distinguishing between intraspinal schwannomas and meningiomas, and has important clinical significance for clinical decision-making and prognosis prediction.

Risk Factors for Lymphovascular Invasion in Invasive Ductal Carcinoma Based on Clinical and Preoperative Breast MRI Features: a Retrospective Study

RATIONALE AND OBJECTIVES

Lymphovascular invasion (LVI) plays an important role in the prediction of metastasis and prognosis in breast cancer (BC) patients. The present study assessed correlations between preoperative breast MRI, clinical features, and LVI in patients with invasive ductal carcinoma (IDC) and identified risk factors based on these correlation factors.

MATERIALS AND METHODS

Patients confirmed with IDC between 01/2012 and 12/2021 were retrospectively reviewed at our hospital. A total of 5 clinical and 14 MRI features to characterize tumours were extracted. LVI evaluated in hematoxylin and eosin sections. T-test and chi-square tests were used to compare the differences in clinical and MRI features between the LVI positive and negative groups. The associations between individual features and LVI were analysed by univariable logistic regression analysis, and risk factors for LVI were identified by multivariable logistic regression analysis based on these correlation factors.

RESULTS

This study included 353 patients with IDC, including 130 with positive LVI. Age, CEA, CA-153, amount of fibroglandular tissue (FGT), background parenchymal enhancement, tumour size, shape, skin thickening, nipple retraction, adjacent vessel sign, and axillary lymph node (ALN) size in the LVI positive group were significantly different from the LVI negative group (all p<0.05). Multivariate logistic regression analysis revealed that age (odds ratio OR = 1.030), CA-153 (OR = 1.018), heterogeneous FGT (OR = 2.484), shape (OR = 2.157), and ALN size (OR = 1.051) were risk factors for LVI (all p<0.05).

CONCLUSION

Preoperative breast MRI and clinical features correlated with LVI, age, CA-153, heterogeneous FGT, shape, and ALN size are risk factors for LVI in patients with IDC.

Targeting Peptide, Fluorescent Reagent Modified Magnetic Liposomes Coated with Rapamycin Target Early Atherosclerotic Plaque and Therapy

Atherosclerosis is the leading cause of global morbidity and mortality. Its therapy requires research in several areas, such as diagnosis of early arteriosclerosis, improvement of the pharmacokinetics and bioavailability of rapamycin as its therapeutic agents. Here, we used the targeting peptide VHPKQHR (VHP) (or fluorescent reagent) to modify the phospholipid molecules to target vascular cell adhesion molecule-1 (VCAM-1) and loaded ultrasmall paramagnetic iron oxide (USPIO/Fe₃O₄) plus rapamycin (Rap) to Rap/Fe₃O₄@VHP-Lipo (VHPKQHR-modified magnetic liposomes coated with Rap). This nanoparticle can be used for both the diagnosis and therapy of early atherosclerosis. We designed both an ex vivo system with mouse aortic endothelial cells (MAECs) and an in vivo system with ApoE knockout mice to test the labeling and delivering potential of Rap/Fe₃O₄@VHP-Lipo with fluorescent microscopy, flow cytometry and MRI. Our results of MRI imaging and fluorescence imaging showed that the T2 relaxation time of the Rap/Fe₃O₄@VHP-Lipo group was reduced by 2.7 times and 1.5 times, and the fluorescence intensity increased by 3.4 times and 2.5 times, respectively, compared with the normal saline group and the control liposome treatment group. It showed that Rap/Fe₃O₄@VHP-Lipo realized the diagnosis of early AS. Additionally, our results showed that, compared with the normal saline and control liposomes treatment group, the aortic fluorescence intensity of the Rap/Fe₃O₄@VHP-Lipo treatment group was significantly weaker, and the T2 relaxation time was prolonged by 8.9 times and 2.0 times, indicating that the targeted diagnostic agent detected the least plaques in the Rap/Fe₃O₄@VHP-Lipo treatment group. Based on our results, the synthesized theragnostic Rap/Fe₃O₄@VHP-Lipo serves as a great label for both MRI and fluorescence bimodal imaging of atherosclerosis. It also has therapeutic effects for the early treatment of atherosclerosis, and it has great potential for early diagnosis and can achieve the same level of therapy with a lower dose of Rap.

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume

Background

Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This study aims to develop an MRI segmentation model of intercondylar fossa based on deep learning to automatically measure notch volume, and explore its correlation with ACL injury.

Methods

The MRI data of 363 subjects (311 males and 52 females) with ACL injuries incurred during non-contact sports and 232 subjects (147 males and 85 females) with intact ACL were retrospectively analyzed. Each layer of intercondylar fossa was manually traced by radiologists on axial MR images. Notch volume was then calculated. We constructed an automatic segmentation system based on the architecture of Res-UNet for intercondylar fossa and used dice similarity coefficient (DSC) to compare the performance of segmentation systems by different networks. Unpaired t-test was performed to determine differences in notch volume between ACL-injured and intact groups, and between males and females.

Results

The DSCs of intercondylar fossa based on different networks were all more than 0.90, and Res-UNet showed the best performance. The notch volume was significantly lower in the ACL-injured group than in the control group (6.12 ± 1.34 cm³ vs. 6.95 ± 1.75 cm³, P < 0.001). Females had lower notch volume than males (5.41 ± 1.30 cm³ vs. 6.76 ± 1.51 cm³, P < 0.001). Males and females who had ACL injuries had smaller notch than those with intact ACL (p < 0.001 and p < 0.005). Men had larger notches than women, regardless of the ACL injuries (p < 0.001).

Conclusion

Using a deep neural network to segment intercondylar fossa automatically provides a technical support for the clinical prediction and prevention of ACL injury and re-injury after surgery.

Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features

Objectives

To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features.

Methods

Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models.

Results

A total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits.

Conclusion

The combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.

VHPKQHR Peptide Modified Ultrasmall Paramagnetic Iron Oxide Nanoparticles Targeting Rheumatoid Arthritis for T1-Weighted Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) could be the ideal diagnostic modality for early rheumatoid arthritis (RA). Vascular cell adhesion molecule-1 (VCAM-1) is highly expressed in synovial locations in patients with RA, which could be a potential target protein for RA diagnosis. The peptide VHPKQHR (VHP) has a high affinity to VCAM-1. To make the contrast agent to target RA at an early stage, we used VHP and ultrasmall paramagnetic iron oxide (USPIO) to synthesize UVHP (U stands for USPIO) through a chemical reaction with 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide. The size of UVHP was 6.7 nm; the potential was −27.7 mV, and the r₂/r₁ value was 1.73. Cytotoxicity assay exhibited that the cell survival rate was higher than 80% at even high concentrations of UVHP (Fe concentration 200 µg/mL), which showed the UVHP has low toxicity. Compared with no TNF-α stimulation, VCAM-1 expression was increased nearly 3-fold when mouse aortic endothelial cells (MAECs) were stimulated with 50 ng/mL TNF-α; cellular Fe uptake was increased very significantly with increasing UVHP concentration under TNF-α treatment; cellular Fe content was 17 times higher under UVHP with Fe concentration 200 µg/mL treating MAECs. These results indicate that UVHP can target overexpression of VCAM-1 at the cellular level. RA mice models were constructed with adjuvant-induced arthritis. In vivo MRI and biodistribution results show that the signal intensity of knee joints was increased significantly and Fe accumulation in RA model mice compared with normal wild-type mice after injecting UVHP 24 h. These results suggest that we have synthesized a simple, low-cost, and less toxic contrast agent UVHP, which targeted VCAM-1 for early-stage RA diagnosis and generates high contrast in T₁-weighted MRI.

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-Ⅲ-related medically refractory epilepsy. A total of 266 patients with FCD typed-Ⅲ-related medically refractory epilepsy were retrospectively studied. Presurgical clinical data, type of surgery, and postsurgical seizure outcome were analyzed. The minimum post-surgical follow-up was 1 year. A total of 266 patients of FCD type Ⅲ were included in this study and the median follow-up time was 30 months (range, 12~48 months). Age at onset ranged from 1.0 years to 58.0 years, with a median age of 12.5 years. The number of patients under 12 years old was 133 (50%) in patients with FCD type Ⅲ. A history of febrile seizures was present in 42 (15.8%) cases. In the entire postoperative period, 179 (67.3%) patients were seizure-free. Factors with p < 0.15 in univariate analysis, such as age of onset of epilepsy (p = 0.145), duration of epilepsy (p = 0.004), febrile seizures (p = 0.150), being MRI-negative (p = 0.056), seizure type (p = 0.145) and incomplete resection, were included in multivariate analysis. Multivariate analyses revealed that MRI-negative findings of FCD (OR 0.34, 95% CI 0.45-0.81, p = 0.015) and incomplete resection (OR 0.12, 95% CI 0.05-0.29, p < 0.001) are independent predictors of unfavorable seizure outcomes. MRI-negative finding of FCD lesions and incomplete resection were the most important predictive factors for poor seizure outcome in patients with FCD type Ⅲ.

Overuse wrist injuries in adolescent platform and springboard divers

To investigate overuse wrist injuries in adolescent divers using magnetic resonance imaging (MRI). The bilateral wrists (76 wrists) of 38 professional divers were examined by MRI; 42 wrists with pain were placed in the symptomatic group, and 34 wrists without pain were placed in the asymptomatic group. Two experienced radiologists assessed the wrist injuries. Chi‑square test was used to compare the difference in injuries between the two groups. Subchondral osteosclerosis was observed in 47.62% of symptomatic wrists and 52.94% of asymptomatic wrists. Distal radial epiphyseal plate injury was detected in 42.86% of symptomatic wrists and 11.76% of asymptomatic wrists. Triangular fibrocartilage complex injury was observed in 19.04% of symptomatic wrists and 8.82% of asymptomatic wrists. Bursae around the wrist were observed in 21.42% of symptomatic wrists and 17.64% of asymptomatic wrists. The incidence of distal radial epiphyseal plate injury was higher in the symptomatic group than in the asymptomatic group (P = 0.007), however, other injuries were not statistically different between the two groups (P > 0.05). This study demonstrated that adolescent divers had various wrist injuries, and distal radial epiphyseal plate injury was more common in the symptomatic wrist; these injuries show no specific clinical symptoms and are easily overlooked.

Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia

Objectives: To develop and validate a radiological nomogram combining radiological and clinical characteristics for differentiating mycoplasma pneumonia and bacterial pneumonia with similar CT findings. Methods: A total of 100 cases of pneumonia patients receiving chest CT scan were retrospectively analyzed, including 60 patients with mycoplasma pneumonia and 40 patients with bacterial pneumonia. The patients were divided into the train set (n = 70) and the test set (n = 30). The features were extracted from chest CT images of each patient by AK analysis software, then univarite analysis, spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) were utilized for dimension reduction in training set. A radiomics model was built by multivariable logistic regression based on the selected features, and a radiomics-clinical multivariable logistic regression model was built by combining imaging radiomics and clinical risk factors (age and temperature). ROC, AUC, sensitivity, specificity, and accuracy were calculated to validate the two models. The nomogram of the radiomics-clinical was built and evaluated by calibration curve. The clinical benefit of the two models was measured by using decision curve. Results: A total of 396 texture features were extracted from each chest CT image, and 10 valuable features were screened out. In the radiomics model, the AUC, sensitivity, specificity, and accuracy for the train set is 0.877, 0.762, 0.821, 78.6%, and for the test set it is 0.810, 0.667, 0.750 and 70.0%, respectively. In the radiomics-clinical model, the AUC, sensitivity, specificity, and accuracy for the train set is 0.905, 0.976, 0.714, 87.1%, and for the test set is is 0.847, 0.889, 0.667 and 80.0%, respectively. Decision curve analysis shows that both the two models increase the clinical benefits of the patients, and the radiomics-clinical model gains higher clinical benefits, compared to the radiomics model. Conclusion: The radiomics-clinical nomogram had good performance in identifying mycoplasma pneumonia and bacterial pneumonias, which would be helpful in clinical decision-making.

Remarkable gastrointestinal and liver manifestations of COVID-19: A clinical and radiologic overview

The coronavirus disease 2019 (COVID-19) raging around the world still has not been effectively controlled in most countries and regions. As a severe acute respiratory syndrome coronavirus, in addition to the most common infectious pneumonia, it can also cause digestive system disease such as diarrhea, nausea, vomiting, liver function damage, etc. In medical imaging, it manifests as thickening of the intestinal wall, intestinal perforation, pneumoperitoneum, ascites and decreased liver density. Angiotensin-converting enzyme 2 has great significance in COVID-19-related digestive tract diseases. In this review, we summarized the data on the clinical and imaging manifestations of gastrointestinal and liver injury caused by COVID-19 so far and explored its possible pathogenesis.

Prediction of Treatment Response According to ASAS-EULAR Management Recommendations in 1 Year for Hip Involvement in Axial Spondyloarthritis Based on MRI and Clinical Indicators

BACKGROUND

To predict the treatment response for axial spondyloarthritis (axSpA) with hip involvement in 1 year based on MRI and clinical indicators.

METHODS

A total of 77 axSpA patients with hip involvement (60 males; median age, 25 years; interquartile, 22-31 years old) were treated with a drug recommended by the Assessment of SpondyloArthritis international Society and the European League Against Rheumatism (ASAS-EULAR) management. They were prospectively enrolled according to Assessment in SpondyloArthritis international Society (ASAS) criteria. Clinical indicators, including age, gender, disease duration, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), were collected at baseline and in 3 months to 1-year follow-up. Treatment response was evaluated according to ASAS response criteria. MRI indicators consisting of bone marrow edema (BME) in acetabulum and femoral head, hip effusion, fat deposition, thickened synovium, bone erosion, bone proliferation, muscle involvement, enthesitis and bony ankylosis were assessed at baseline. Spearman's correlation analysis was utilized for indicator selection. The selected clinical and MRI indicators were integrated with previous clinical knowledge to develop multivariable logistic regression models. Receiver operator characteristic curve and area under the curve (AUC) were used to assess the performance of the constructed models.

RESULTS

The model combining MR indicators comprising hip effusion, BME in acetabulum and femoral head and clinical indicators consisting of disease duration, ESR and CRP yielded AUC values of 0.811 and 0.753 for the training and validation cohorts, respectively.

CONCLUSION

The model combining MRI and clinical indicators could predict treatment response for axSpA with hip involvement in 1 year.

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard

BACKGROUND

MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection system based on a deep-learning algorithm may improve interpretation time and reliability.

PURPOSE

To determine the feasibility of using a deep learning approach to detect ACL injuries within the knee joint on MRI.

STUDY TYPE

Retrospective.

POPULATION

In all, 163 subjects with an ACL tear and 245 subjects with an intact ACL. There were 285, 81, and 42 volumes for training, validation, and test sets, respectively.

FIELD STRENGTH/SEQUENCE

2D sagittal proton density-weighted spectral attenuated inversion recovery sequences at 1.5T and 3.0T.

ASSESSMENT

Based on the architecture of 3D DenseNet, we constructed a classification convolutional neural network. We tested this deep learning approach with different inputs and two other algorithms, including VGG16 and ResNet. Then we had both inexperienced radiologists and senior radiologists read the MR images.

STATISTICAL TESTS

Using arthroscopic results as the reference standard, the performance of three different inputs and three different algorithms, the residents and senior radiologists assessed the classification accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC).

RESULTS

The accuracy, sensitivity, specificity, PPV, and NPV of our customized 3D deep learning architecture was 0.957, 0.976, 0.944, 0.940, and 0.976, respectively. The average AUCs were 0.946, 0.859, 0.960 for ResNet, VGG16, and our proposed network, respectively. The diagnostic accuracy of our model, residents, and senior radiologists was 0.957, 0.814, and 0.899, respectively.

DATA CONCLUSION

Our study demonstrated the feasibility of using an automated deep-learning-based detection system to evaluate ACL injury.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 1 J. MAGN. RESON. IMAGING 2020;52:1745-1752.

Optical Biopsy of Melanoma and Basal Cell Carcinoma Progression by Noncontact Photoacoustic and Optical Coherence Tomography: In Vivo Multi-Parametric Characterizing Tumor Microenvironment

Measuring the structural and functional status of tumor microenvironment for malignant melanoma (MM) and basal cell carcinoma (BCC) is of profound significance in understanding dermatological condition for biopsy. However, conventional optical imaging techniques are limited to visualize superficial skin features and parameter information is deficient to depict pathophysiology correlations of skin diseases. Here, we demonstrate a preclinical device, all-optically integrated photoacoustic and optical coherence tomography (AOPA/OCT), that, for the first time, can simultaneously provide label-free biomarkers of vascular patterns, temporal and spatial heterogeneity of blood flow, and tissue micro-structure changes during tumor growth with pathophysiological correlations in mice models. We found that tumor microenvironment of MM and BCC led to the alternation in spatial-temporal heterogeneity that affected morphological and functional parameters, performing the AOPA/OCT quantitative metrics. A robust correlation between imaging biomarkers derived from this in vivo technique and histopathology validation ex vivo in distinguishing benign from malignant is also presented. In receiver operating characteristics (ROC) analysis, multi-parametric AOPA/OCT yields improved diagnostic accuracy of 98.4% and 95.8% for MM and BCC respectively, which indicate that AOPA/OCT represents a high-performance and clinically translatable technique for accurate diagnosis and therapy monitoring in dermatology.

Construction of dual nanomedicines for the imaging and alleviation of atherosclerosis

Magnetic resonance imaging (MRI) is an essential tool for the diagnosis of atherosclerosis, a chronic cardiovascular disease. MRI primarily uses superparamagnetic iron oxide (SPIO) as a contrast agent. However, SPIO integrated with therapeutic drugs has rarely been studied. In this study, we explored biocompatible paramagnetic iron-oxide nanoparticles (NPs) in a complex with low pH-sensitive cyclodextrin for the diagnostic imaging and treatment of atherosclerosis. The NPs were conjugated with profilin-1 antibody (PFN1) to specifically target vascular smooth muscle cells (VSMCs) in the atherosclerotic plaque and integrated with the anti-inflammatory drug, rapamycin. The PFN1-CD-MNPs were easily binded to the VSMCs, indicating their good biocompatibility and low renal toxicity over the long term. Ex vivo near-infrared fluorescence (NIRF) imaging and in vivo MRI indicated the accumulation of PFN1-CD-MNPs in the atherosclerotic plaque. The RAP@PFN1-CD-MNPs alleviated the progression of arteriosclerosis. Thus, PFN1-CD-MNPs served not only as multifunctional imaging probes but also as nanovehicles for the treatment of atherosclerosis.

Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: A retrospective multicenter study

PURPOSE

To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection.

METHODS

This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index：0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful.

CONCLUSION

The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.

Convergent syndromic atrophy of pain and emotional systems in patients with irritable bowel syndrome and depressive symptoms

Irritable bowel syndrome (IBS) is a brain-gut disorder that is often accompanied by psychiatric comorbidities, particularly depression. However, the neuroanatomical substrates of IBS with depressive symptoms (DEP-IBS) and how depressive symptoms and brain morphology modulate IBS symptoms remain unknown. In this study, structural MRI data were processed using a voxel-based morphometry technique and one-way analysis of covariance (ANCOVA) and post-hoc t-tests were performed to compare gray matter volume (GMV) among 28 patients with DEP-IBS, 21 patients with IBS who lacked depressive symptoms (nDEP-IBS), and 36 healthy controls (HC). Correlation and mediation analyses were performed to evaluate the relationship between differing GMV in DEP-IBS and clinical variables. We found that GMV in the bilateral prefrontal, insular, and dorsal striatal areas, as well as the left temporal pole, were significantly lower in the DEP-IBS group than in the HC group. Moreover, compared with the nDEP-IBS group, the DEP-IBS group exhibited decreased GMV in the bilateral medial, dorsolateral prefrontal, and orbitofrontal cortices, bilateral dorsal striatum, and left insular cortices. Correlation analysis revealed that GMV in these atrophic brain areas of the DEP-IBS group was negatively correlated with depression, gastrointestinal symptoms, and disease duration. Our results further revealed that depressive symptoms served as a mediator between gastrointestinal symptoms and GMV in the left insula, right medial prefrontal cortex, and right middle frontal gyrus, while gastrointestinal symptoms served as a mediator between depression and GMV in these regions. Our results suggest convergent syndromic atrophy in the pain and emotional systems of patients with DEP-IBS.

Intravoxel Incoherent Motion Imaging Study of Madecassoside in Improving Lipopolysaccharide-Induced Cognitive Impairment in Rats

BACKGROUND

Central nervous system inflammation is associated with neurodegenerative diseases and is thought to play a part in the pathophysiological cascade leading to cognitive impairment. Madecassoside (MA) has shown potential for the treatment of neuroinflammation. Lipopolysaccharide (LPS) can be used to establish an animal model of cognitive dysfunction induced by neuroinflammation. Intravoxel incoherent motion (IVIM) may potentially provide diffusion and perfusion data.

PURPOSE

To investigate the effect of MA on neurocognitive impairment induced by LPS in rats, and to explore the changes of brain microstructure and microcirculatory perfusion by IVIM imaging.

STUDY TYPE

Prospective.

POPULATION

Thirty-six male Sprague-Dawley rats were randomly divided into six groups (control group, sham operation group, LPS group, low-dose MA group, middle-dose MA group, and high-dose MA group) in a model of neurocognitive impairment induced by LPS (150 μg / 5 μL, 5 μL).

FIELD STRENGTH/SEQUENCE

IVIM-DWI sequence at 3.0T MRI; the scan time was 2 minutes and 17 seconds.

ASSESSMENT

The escape latency times of a Morris water maze test was used to evaluate the cognitive impairment rat model and the changes of learning ability of rats treated with different doses of MA (30 mg/kg, 60 mg/kg, 120 mg/kg). A GE postprocessing workstation (adw 4.5) was used to analyze the changes of each parameter (f value, D value, and D* value) in the IVIM data of each group.

STATISTICAL TESTS

All the data were analyzed by one-way and two-way analysis of variance (ANOVA).

RESULTS

The escape latency of the LPS group was significantly longer than the sham group (P = 0.05, 0.001, 0.006, and 0.042, respectively), and the high-dose group was significantly shorter than the LPS group on the sixth day (P = 0.034). Compared with the control group, the D values and f values of cerebral cortex and hippocampus were decreased significantly in the LPS group (P = 0.043 and 0.003; P = 0.029 and 0.016, respectively). With the increasing dose of MA, the D and f values of hippocampus and cortex increased, and there was a significant difference between the high-dose MA group and LPS group (D values: P = 0.038, 0.036; f values: P = 0.048, 0.039, respectively) DATA CONCLUSION: MA can improve the cognitive impairment induced by LPS by reducing neuroinflammation, and the changes of microcirculation and microperfusion in the brain tissue of these rats can be detected by IVIM imaging.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1836-1843.

Interplay of Autophagy Inducer Rapamycin and Proteasome Inhibitor MG132 in Reduction of Foam Cell Formation and Inflammatory Cytokine Expression

MG132 is a pivotal inhibitor of the ubiquitin-proteasome system (UPS), and rapamycin (RAPA) is an important inducer of autophagy. MG132 and RAPA have been shown to be effective agents that can cure multiple autoimmune diseases by reducing inflammation. Although individual MG132 and RAPA showed protective effects for atherosclerosis (AS), the combined effect of these two drugs and its molecular mechanism are still unclear. In this article we investigate the regulation of oxidative modification of low-density lipoprotein (ox-LDL) stress and foam cell formation in the presence of both proteasome inhibitor MG132 and the autophagy inducer RAPA to uncover the molecular mechanism underlying this process. We established the foam cells model by ox-LDL and an animal model. Then, we tested six experimental groups of MG132, RAPA, and 3MA drugs. As a result, RAPA-induced autophagy reduces accumulation of polyubiquitinated proteins and apoptosis of foam cells. The combination of MG132 with RAPA not only suppressed expression of the inflammatory cytokines and formation of macrophage foam cells, but also significantly affected the NF-κB signaling pathway and the polarization of RAW 264.7 cells. These data suggest that the combination of proteasome inhibitor and autophagy inducer ameliorates the inflammatory response and reduces the formation of macrophage foam cells during development of AS. Our research provides a new way to suppress vascular inflammation and stabilize plaques of late atherosclerosis.