Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma.

RATIONALE AND OBJECTIVES: To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats. MATERIALS AND METHODS: The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC). RESULTS: Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor. CONCLUSION: Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.

[Genotype Analysis of Common and Rare Thalassemia in People of Reproductive Age in Huadu District, Guangzhou].

OBJECTIVE: To analyze the genotypes distribution of common and rare thalassemia in people of reproductive age in Huadu district of Guangzhou, enhance the database of thalassemia. METHODS: Peripheral blood samples were collected for genotype analysis in Maternity and Child Health Hospital of Huadu District from January 2016 to October 2022. Gap-PCR and Reverse dot blot hybridization were used to detect common thalassemia genotypes. DNA sequencing was performed in samples suspected of rare genotypes. RESULTS: A total of 16 171 subjects were identified as thalassemia carriers, and the positive rate was 44.41% (16 171/36 412). The genotypes of 114 cases (0.31%) were rare. A total of 10 845 cases were identified as α-thalassemia carriers (29.78%), and --SEA/αα was the most common genotype in those people, followed by -α3.7/αα and -α4.2/αα. A total of 4 531 subjects were identified as common ß-thalassemia carriers (12.44%). The most common ß-thalassemia mutation in the population was ß41-42./ßN., followed by ß654/ßN. and ß-28/ßN.. A total of 681 subjects were identified as αß thalassemia carriers (1.87%), among them --SEA/αα compounded with ßCD41-42./ßN. was the most common genotype. A total of 48 cases were identified as rare α-thalassemia carriers, 14 types of mutations, in which Fusion gene/αα was the most common. A total of 52 cases were identified as rare ß-thalassemia carriers, 11 types of mutation, in which ßSEA-HPFH/ßN. was the most common. CONCLUSION: The thalassemia genotypes in Huadu district are complex and diverse. We should attach great importance to the detection of rare thalassemia genotypes.

Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia.

PURPOSE: To compare the performance of MRI-based Gaussian mixture model (GMM), K-means clustering, and Otsu unsupervised algorithms in predicting sarcopenia and to develop a combined model by integrating clinical indicators. METHODS: Retrospective analysis was conducted on clinical and lumbar MRI data from 118 patients diagnosed with sarcopenia and 222 patients without the sarcopenia. All patients were randomly divided into training and validation groups in a 7:3 ratio. Regions of interest (ROI), specifically the paravertebral muscles at the L3/4 intervertebral disc level, were delineated on axial T2-weighted images (T2WI). The Gaussian mixture model (GMM), K-means clustering, and Otsu's thresholding algorithms were employed to automatically segment muscle and adipose tissues at both the cohort and case levels. Subsequently, the mean signal intensity, volumes, and percentages of these tissues were calculated and compared. Logistic regression analyses were conducted to construct models and identify independent predictors of sarcopenia. An combined model was developed by combining the optimal magnetic resonance imaging (MRI) model and clinical predictors. The performance of the constructed model was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS: Age, BMI, and serum albumin were identified as independent clinical predictors of sarcopenia. The cohort-level GMM demonstrated the best predictive performance both in the training group (AUC=0.840) and validation group (AUC=0.800), while the predictive performance of the other models was lower than that of the clinical model both in the training and validation groups. After combining the cohort-level GMM with the independent clinical predictors, the AUC of the training and validation groups increased to 0.871 and 0.867, respectively. CONCLUSION: The cohort-level GMM shows potential in predicting sarcopenia, and the incorporation of independent clinical predictors further increased the performance.

Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.

OBJECTIVE: To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. METHODS: Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. RESULTS: Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. CONCLUSION: Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.

The value of multiparametric MRI-based habitat imaging for differentiating uterine sarcomas from atypical leiomyomas: a multicentre study.

PURPOSE: To explore the feasibility of multiparametric MRI-based habitat imaging for distinguishing uterine sarcoma (US) from atypical leiomyoma (ALM). METHODS: This retrospective study included the clinical and preoperative MRI data of 69 patients with US and 225 patients with ALM from three hospitals. At both the individual and cohort levels, the K-means and Gaussian mixture model (GMM) algorithms were utilized to perform habitat imaging on MR images, respectively. Specifically, T2-weighted images (T2WI) and contrast-enhanced T1-weighted images (CE-T1WI) were clustered to generate structural habitats, while apparent diffusion coefficient (ADC) maps and CE-T1WI were clustered to create functional habitats. Parameters of each habitat subregion were extracted to construct distinct habitat models. The integrated models were constructed by combining habitat and clinical independent predictors. Model performance was assessed using the area under the curve (AUC). RESULTS: Abnormal vaginal bleeding, lactate dehydrogenase (LDH), and white blood cell (WBC) counts can serve as clinical independent predictors of US. The GMM-based functional habitat model at the cohort level had the highest mean AUC (0.766) in both the training and validation cohorts, followed by the GMM-based structural habitat model at the cohort level (AUC = 0.760). Within the integrated models, the K-means functional habitat model based on the cohort level achieved the highest mean AUC (0.905) in both the training and validation cohorts. CONCLUSION: Habitat imaging based on multiparametric MRI has the potential to distinguish US from ALM. The combination of clinical independent predictors with the habitat models can effectively improve the performance.

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study.

Purpose: To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA). Methods: Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC). Results: There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798). Conclusion: Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.

MAMILNet: advancing precision oncology with multi-scale attentional multi-instance learning for whole slide image analysis.

Background: Whole Slide Image (WSI) analysis, driven by deep learning algorithms, has the potential to revolutionize tumor detection, classification, and treatment response prediction. However, challenges persist, such as limited model generalizability across various cancer types, the labor-intensive nature of patch-level annotation, and the necessity of integrating multi-magnification information to attain a comprehensive understanding of pathological patterns. Methods: In response to these challenges, we introduce MAMILNet, an innovative multi-scale attentional multi-instance learning framework for WSI analysis. The incorporation of attention mechanisms into MAMILNet contributes to its exceptional generalizability across diverse cancer types and prediction tasks. This model considers whole slides as "bags" and individual patches as "instances." By adopting this approach, MAMILNet effectively eliminates the requirement for intricate patch-level labeling, significantly reducing the manual workload for pathologists. To enhance prediction accuracy, the model employs a multi-scale "consultation" strategy, facilitating the aggregation of test outcomes from various magnifications. Results: Our assessment of MAMILNet encompasses 1171 cases encompassing a wide range of cancer types, showcasing its effectiveness in predicting complex tasks. Remarkably, MAMILNet achieved impressive results in distinct domains: for breast cancer tumor detection, the Area Under the Curve (AUC) was 0.8872, with an Accuracy of 0.8760. In the realm of lung cancer typing diagnosis, it achieved an AUC of 0.9551 and an Accuracy of 0.9095. Furthermore, in predicting drug therapy responses for ovarian cancer, MAMILNet achieved an AUC of 0.7358 and an Accuracy of 0.7341. Conclusion: The outcomes of this study underscore the potential of MAMILNet in driving the advancement of precision medicine and individualized treatment planning within the field of oncology. By effectively addressing challenges related to model generalization, annotation workload, and multi-magnification integration, MAMILNet shows promise in enhancing healthcare outcomes for cancer patients. The framework's success in accurately detecting breast tumors, diagnosing lung cancer types, and predicting ovarian cancer therapy responses highlights its significant contribution to the field and paves the way for improved patient care.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

Irreversible repolarization of tumour-associated macrophages by low-Pi stress inhibits the progression of hepatocellular carcinoma.

Numerous studies have shown the positive correlation between high levels of Pi and tumour progression. A critical goal of macrophage-based cancer therapeutics is to reduce anti-inflammatory macrophages (M2) and increase proinflammatory antitumour macrophages (M1). This study aimed to investigate the relationship between macrophage polarization and low-Pi stress. First, the spatial populations of M2 and M1 macrophages in 22 HCC patient specimens were quantified and correlated with the local Pi concentration. The levels of M2 and M1 macrophage markers expressed in the peritumour area were higher than the intratumour levels, and the expression of M2 markers was positively correlated with Pi concentration. Next, monocytes differentiated from THP-1 cells were polarized against different Pi concentrations to investigate the activation or silencing of the expression of p65, IκB-α and STAT3 as well as their phosphorylation. Results showed that low-Pi stress irreversibly repolarizes tumour-associated macrophages (TAMs) towards the M1 phenotype by silencing stat6 and activating p65. Moreover, HepG-2 and SMCC-7721 cells were cultured in conditioned medium to investigate the innate anticancer immune effects on tumour progression. Both cancer cell lines showed reduced proliferation, migration and invasion, as epithelial-mesenchymal transition (EMT) was inactivated. In vivo therapeutic effect on the innate and adaptive immune processes was validated in a subcutaneous liver cancer model by the intratumoural injection of sevelamer. Tumour growth was significantly inhibited by the partial deprivation of intratumoural Pi as the tumour microenvironment under low-Pi stress is more immunostimulatory. The anticancer immune response, activated by low-Pi stress, suggests a new macrophage-based immunotherapeutic modality.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

OBJECTIVES: To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS: A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS: The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS: The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT: An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS: • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Low Pi stress enhances the sensitivity of hepatocellular carcinoma to sorafenib.

Sorafenib is a tyrosine kinase inhibitor for the treatment of advanced-stage HCC; however, clinical trials of sorafenib failed to demonstrate long-term survival benefits due to drug resistance. Low Pi stress has been shown to inhibit tumor growth and the expression of multidrug resistance-associated proteins. In this study, we investigated the sensitivity of HCC to sorafenib under conditions of low Pi stress. As a result, we found that low Pi stress facilitated sorafenib-mediated suppression of migration and invasion of HepG-2 and Hepa1-6 cells by decreasing the phosphorylation or expression of AKT, Erk and MMP-9. Angiogenesis was inhibited due to decreased expression of PDGFR under low Pi stress. Low Pi stress also decreased the viability of sorafenib-resistant cells by directly regulating the expression of AKT, HIF-1a and P62. In vivo drug sensitivity analysis in the four animal models showed a similar tendency that low Pi stress enhances sorafenib sensitivity in both the normal and drug-resistant models. Altogether, low Pi stress enhances the sensitivity of hepatocellular carcinoma to sorafenib and expands the indications for sevelamer.

[Anatomic Abnormalities of Hippocampal Subfields in First-Episode Drug-Naïve Schizophrenia and Major Depressive Disorder: A Structural MRI Comparative Study].

Objective: To compare the structural changes along the longitudinal axis of hippocampus subfields between schizophrenia (SCZ) patients and major depressive disorder (MDD) patients in the early stage of their SCZ and MDD. Methods: Seventy-nine first-episode drug-naïve patients with SCZ, 48 first-episode drug-naïve patients with MDD, and 79 healthy controls (HC) were recruited and underwent assessment of clinical symptoms and magnetic resonance imaging (MRI) of the head. Following the calculation of hippocampal and subfield volumes with FreeSurfer, the volume of longitudinal subfields were summed up. Inter-group comparison of these indicators was made with the data of different groups and the correlation between clinical symptoms and the volumes of longitudinal subfields was analyzed. Results: Compared with HC, SCZ patients had smaller bilateral posterior hippocampus (left: t=－2.69, P=0.01; right: t=－2.90, P=0.004), while MDD patients exhibited no changes along the longitudinal axis of hippocampal subfields. In SCZ patients, the volume of bilateral posterior hippocampus was negatively correlated with the negative symptom scores of Positive and Negative Syndrome Scale (left: r=－0.29, P=0.01; right: r=－0.23, P=0.04). Conclusion: The smaller posterior hippocampus may be an imaging feature for distinguishing SCZ from MDD and may have contributed to the neuropathophysiological mechanism of SCZ in the early stage of the onset of the disease.

Efficacy and safety of acupuncture for postpartum hypogalactia: protocol for a systematic review and meta-analysis.

INTRODUCTION: Breast milk is recognised as the best natural food for neonates, but many women experience postpartum hypogalactia (PH). Randomised trials have found that acupuncture exert therapeutic effect on women with PH. However, systematic reviews on the efficacy and safety of acupuncture are still lacking; therefore, this systematic review aims to evaluate the efficacy and safety of acupuncture for PH. METHODS AND ANALYSIS: Six English databases (PubMed, Cochrane Library, EMBASE, EBSCO, Scopus, and Web of Science) and four Chinese databases (China National Knowledge Infrastructure, Wan-Fang, Chinese Biomedical Literature and Chinese Scientific Journal) will be systematically searched from their establishment to 1 September 2022. Randomised controlled trials of the efficacy of acupuncture for PH will be reviewed. The study selection, data extraction and research quality evaluation will be conducted independently by two reviewers. The primary outcome is the change in serum prolactin level from baseline to the end of treatment. Secondary results include milk secretion volume, total effectiveness rate, degree of mammary fullness, rate of exclusive breast feeding, and adverse events. A meta-analysis will be performed using RevMan V.5.4 statistical software. Otherwise, a descriptive analysis will be conducted. The risk of bias will be assessed using the revised Cochrane risk-of-bias tool. ETHICS AND DISSEMINATION: This systematic review protocol does not require ethical approval because it does not include private information/data of the participants. This article will be published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42022351849.

Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma.

RATIONALE AND OBJECTIVES: To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC). MATERIALS AND METHODS: A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI). RESULTS: Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength. CONCLUSION: The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.

Evaluation of the safety and efficacy of transarterial sevelamer embolization in a rabbit liver cancer model: A challenge on the size rule for vascular occlusion.

As the most efficient method to treat hepatocellular carcinoma in the immediate or advanced stage, transarterial chemoembolization (TACE) is coming into the era of microsphere (MP). Drug-eluting beads have shown their huge potential as an embolic agent and drug carrier for chemoembolization, but their sizes are strictly limited to be above 40 µm, which was considered to occlude vessels in a safe mode. microsphere smaller than 40 µm is easy to be washed out and transported to the normal liver lobe or other organs, causing severe adverse events and failed embolization. To determine whether sevelamer ultrafine particle (0.2-0.5 µm) is qualified as a safe and efficient embolic agent, we investigated the safety and therapeutic efficiency of transarterial sevelamer embolization (TASE) in the VX2 rabbit liver cancer model, aiming to challenge the "40 µm" rule on the selection criteria of the MP. In a four-arm study, blank bead (Callisphere, 100-300 µm), luminescent polystyrene microsphere (10, 100 µm), and sevelamer particle were transarterially administered to evaluate the threshold size of the MP size for intrahepatic or extrahepatic permeability. Another four-arm study was designed to clarify the safety and efficiency of preclinical transarterial sevelamer embolizationTASE tests over other techniques. Sham (saline), TASE, C-TACE, and D-TACE (n = 6) were compared in terms of serum chemistry, histopathology, and tumor necrosis ratio. In the first trials, the "40 µm" rule was detectable on the VX2 cancer model, but the regulation has no application to the new embolic agent as sevelamer ultrafine particles have not been found to leak out from the VX2 lesions, only found in the embolized vessels. Pathology proves that less viable tumor residue was found 2 weeks after the procedure, evidencing a better therapeutic outcome. No adverse events were found except for a short stress response. These results indicate that sevelamer is a safe and efficient embolic as an alternative to the current MP-based embolization therapy techniques.

Preliminary Application of Magnetization Transfer Imaging in the Study of Normal Uterus and Uterine Lesions.

Purpose: The aim of this study is to evaluate the utility of magnetization transfer (MT) imaging in the study of normal uterus and common uterine lesions. Methods: This prospective study enrolled 160 consecutive patients with suspected uterine lesions. MT ratio (MTR) map was obtained by pelvic MT imaging on a 3.0T MRI scanner. Patients confirmed by pathology were divided into microscopic lesion group and lesion group, according to whether the maximum diameter of the lesion was less than 5 mm. After evaluating and eliminating patients with poor image quality by a three-point Likert scale, MTR values of lesions and normal endometrium, myometrium, and cervix were independently measured on the MTR map by two radiologists. Inter-reader agreement was evaluated. MTR values were compared among different uterine lesions and normal uterine structures using the Mann-Whitney U test with Bonferroni correction. Receiver operating characteristic curve was performed. The correlations between age and MTR values were explored by Pearson correlation analyses. Results: A total of 96 patients with 121 uterine lesions in the lesion group and 41 patients in the microscopic lesion group were measured. The MTR values among normal endometrium, myometrium, and cervix were statistical significant differences (P < 0.05). There were significant differences between endometrial cancer and normal endometrium and between cervical cancer and normal cervix (both P ≤ 0.001). Area under the curve (AUC) for diagnosing endometrial and cervical cancer were 0.73 and 0.86. Myometrial lesions had significantly higher MTR values than endometrial lesions and cervical cancer (both P < 0.001), and the AUC for differentiating myometrial lesions from them were 0.89 and 0.94. MTR values of endometrial cancer were significantly higher than those of cervical cancer (P = 0.02). There was a critical correlation between age and MTR values in endometrial cancer (r = 0.81, P = 0.04). Conclusions: MTR values showed significant differences among normal uterine structures. It was valuable for diagnosing and differentiating uterine cancer. MTR values could differentiate myometrial lesions from endometrial or cervical lesions.

Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study.

Purpose: The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. Methods: The data of patients with endometrial lesions from two centers were collected. The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, nine machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models. Results: A total of 371 patients were incorporated. The LR model was the optimal radiomics model with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups (AUC = 0.910 and 0.798, respectively), and outperformed the clinical model (AUC = 0.739 and 0.592, respectively) or the radiologist (AUC = 0.768 and 0.628, respectively). The nomogram (AUC = 0.917 and 0.802, respectively) achieved better discrimination performance than the optimal radiomics model in two validation groups. The stacking model (AUC = 0.915) and ensemble model (AUC = 0.918) had a similar performance compared with the nomogram in the internal validation group, whereas the AUCs of the stacking model (AUC = 0.792) and ensemble model (AUC = 0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI, and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits. Conclusions: Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram presents excellent and stable diagnostic efficiency.

Exosomes Derived from Glioma Cells under Hypoxia Promote Angiogenesis through Up-regulated Exosomal Connexin 43.

Glioblastoma multiform (GBM) is a highly aggressive primary brain tumor. Exosomes derived from glioma cells under a hypoxic microenvironment play an important role in tumor biology including metastasis, angiogenesis and chemoresistance. However, the underlying mechanisms remain to be elucidated. In this study, we aimed to explore the role of connexin 43 on exosomal uptake and angiogenesis in glioma under hypoxia. U251 cells were exposed to 3% oxygen to achieve hypoxia, and the expression levels of HIF-1α and Cx43, involved in the colony formation and proliferation of cells were assessed. Exosomes were isolated by differential velocity centrifugation from U251 cells under normoxia and hypoxia (Nor-Exos and Hypo-Exos), respectively. Immunofluorescence staining, along with assays for CCK-8, tube formation and wound healing along with a transwell assay were conducted to profile exosomal uptake, proliferation, tube formation, migration and invasion of HUVECs, respectively. Our results revealed that Hypoxia significantly up-regulated the expression of HIF-1α in U251 cells as well as promoting proliferation and colony number. Hypoxia also increased the level of Cx43 in U251 cells and in the exosomes secreted. The uptake of Dio-stained Hypo-Exos by HUVECs was greater than that of Nor-Exos, and inhibition of Cx43 by 37,43gap27 or lenti-Cx43-shRNA efficiently prevented the uptake of Hypo-Exos by recipient endothelial cells. In addition, the proliferation and total loops of HUVECs were remarkably increased at 24 h, 48 h, and 10 h after Hypo-Exos, respectively. Notably, 37,43gap27, a specific Cx-mimetic peptide blocker of Cx37 and Cx43, efficiently alleviated Hypo-Exos-induced proliferation and tube formation by HUVECs. Finally, 37,43gap27 also significantly attenuated Hypo-Exos-induced migration and invasion of HUVECs. These findings demonstrate that exosomal Cx43 contributes to glioma angiogenesis mediated by Hypo-Exos, and suggests that exosomal Cx43 might serve as a potential therapeutic target for glioblastoma.

Sevelamer arsenite nanoparticle as a Pi-responsive drug carrier and embolic agent for chemoembolization.

Arsenic trioxide (As2O3, ATO) has limited therapeutic benefit to treat solid tumors, whether used alone or in combination. Nanoscale drug delivery vehicles have great potential to overcome the limitation of the utility of ATO by rapid renal clearance and dose-limiting toxicity. Polymeric materials ranging from gelatin foam to synthetic polymers such as poly(vinyl alcohol) were developed for vascular embolic or chemoembolic applications. Recently, we have introduced sevelamer, an oral phosphate binder, as a new polymeric embolic for vascular interventional therapy. In this paper, sevelamer arsenite nanoparticle with a polygonal shape and a size of 50-300 nm, synthesized by anionic exchange from sevelamer chloride, was developed as a Pi-responsive bifunctional drug carrier and embolic agent for chemoembolization therapy. At the same arsenic dosage, sevelamer arsenite-induced severer tumor necrosis than ATO on the VX2 cancer model. In vitro tests evidenced that Pi deprivation by sevelamer could enhance ATO's anticancer effect. The results showed that ATO in Pi starvation reduced cell viability, induced more apoptosis, and diminished the mitochondrial membrane potential (Δψm) of cells since Pi starvation helps ATO to further down-regulate Bcl-2 expression, up-regulate Bax expression, enhance the activation of caspase-3 and increase the release of cytochrome c, and the production of excessive reactive oxygen species (ROS). Sevelamer arsenite not only plays a Pi-activated nano-drug delivery system but also integrated anticancer drug with embolic for interventional therapy. Therefore, our results presented a new administration route of ATO as well as an alternative chemoembolization therapy.

Pi-inducedin-situaggregation of sevelamer nanoparticles for vascular embolization.

Decades have witnessed rapid progress of polymeric materials for vascular embolic or chemoembolic applications. Commercially available polymeric embolics range from gelatin foam to synthetic polymers such as poly(vinyl alcohol). Current systems under investigation include tunable, bioresorbable microspheres composed of chitosan or poly(ethylene glycol) derivatives,in situgelling liquid embolics with improved safety profiles, and radiopaque embolics that are trackablein vivo. In this paper, we proposed a concept of 'responsive embolization'. Sevelamer, clinically proved as an inorganic phosphate binder, was ground into nanoparticles. Sevelamer nanoparticle is highly mobile and capable of swelling and aggregating in the presence of endogenous inorganic phosphate, thereby effectively occluding blood flow in the vessel as it was administered as an embolic agent for interventional therapy. Moreover, citrated sevelamer nanoparticles delayed the aggregation, preferable to penetrate deeply into the capillary system. On the rabbit VX2 liver cancer model, both sevelamer particles aggregates occlude the tumor feeding artery, but backflow was found for the pristine one, thereby citrate passivation of sevelamer nanoparticles endows it have potential from 'bench to bedside' as a new type of vascular embolic.