Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.

BACKGROUND: Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE: To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT: Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS: The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS: Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION: MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer.

BACKGROUND: Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. PURPOSE: To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). MATERIAL AND METHODS: In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed. RESULTS: The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. CONCLUSION: CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors.

OBJECTIVE: To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs). METHODS: A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use. RESULTS: Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort. CONCLUSION: Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.

Detection of rapidly accumulating stress-induced SUMO in prostate cancer cells by a fluorescent SUMO biosensor.

SUMO conjugates and SUMO chains form when SUMO, a small ubiquitin-like modifier protein, is covalently linked to other cellular proteins or itself. During unperturbed growth, cells maintain balanced levels of SUMO conjugates. In contrast, eukaryotic cells that are exposed to proteotoxic and genotoxic insults mount a cytoprotective SUMO stress response (SSR). One hallmark of the SSR is a rapid and massive increase of SUMO conjugates in response to oxidative, thermal, and osmotic stress. Here, we use a recombinant fluorescent SUMO biosensor, KmUTAG-fl, to investigate differences in the SSR in a normal human prostate epithelial cell line immortalized with SV40 (PNT2) and two human prostate cancer cell lines that differ in aggressiveness and response to androgen (LNCaP and PC3). In cells that grow unperturbed, SUMO is enriched in the nuclei of all three cell lines. However, upon 30 min of exposure to ultraviolet radiation or oxidative stress, we detected significant cytosolic enrichment of SUMO as measured by KmUTAG-fl staining. This rapid enrichment in cytosolic SUMO levels was on average fivefold higher in the LNCaP and PC3 prostate cancer cell lines compared to normal immortalized PNT2 cells. Additionally, this enhanced enrichment of cytosolic SUMO was reversible as cells recovered from stress exposure. Our study validates the use of the fluorescent KmUTAG-fl SUMO biosensor to detect differences of SUMO levels and localization between normal and cancer cells and provides new evidence that cancer cells may exhibit an enhanced SSR.

Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients.

OBJECTIVE: To investigate the feasibility of T2WI-based radiomics nomogram analysis to non-invasively predict normal-sized pelvic lymph node (LN) metastasis (LNM) in cervical cancer patients. METHODS: Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T2WI (model 1), model 1 combined with LN radiomics features (model 2), and model 2 combined with tumor score (model 3). Diagnostic performance was assessed and compared. RESULTS: Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15). CONCLUSIONS: T2WI-based radiomics nomogram incorporating the LN radiomics signature with the clinical morphological LN features is promising for predicting the normal-sized pelvic LNM in cervical cancer patients. The original tumor radiomics analysis did not significantly improve the differential diagnosis of LNM. KEY POINTS: • The combination of LN radiomics signature with LN clinical morphological features on T2WI could discriminate LNM relatively well. • The tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

Application of apparent diffusion coefficient values derived from diffusion-weighted imaging for assessing different sized metastatic lymph nodes in cervical cancers.

BACKGROUND: Lymph nodes metastasis is an important factor affecting survival rate and recurrence in cervical cancer patients. Currently, diagnosis of metastatic lymph nodes is mainly based on morphological changes on imaging. However, it is difficult to differentiate normal-sized metastatic lymph nodes with short axis of 5-10mm. PURPOSE: To assess the diagnostic value of apparent diffusion coefficient (ADC) for discriminating different-sized metastatic lymph nodes in patients with cervical cancers. MATERIAL AND METHODS: Pathologically confirmed cervical cancer patients were documented from January 2013 to July 2018 in our hospital. A total of 133 patients who underwent conventional MRI and diffusion-weighted imaging with complete pathology were finally enrolled. A total of 157 lymph nodes were harvested and analyzed. All lymph nodes were divided into three groups according to pathology and their short axis (S) measured on axial T2-weighted imaging: normal-sized (5 mm

Combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging to predict neoadjuvant chemotherapy effect in FIGO stage IB2-IIA2 cervical cancers.

PURPOSE: To explore the value of histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters and apparent diffusion coefficient (ADC) values in predicting the neoadjuvant chemotherapy (NACT) response for cervical cancers. METHODS: Sixty-three patients with pathologically proved stage IB2-IIA2 cervical cancer from March 2013 to January 2017 were retrospectively analyzed. They were divided into two groups on the basis of therapeutic response: the significant response (SR) group, which contains complete response patients and partial response patients, and nonsignificant response (non-SR) group, which contains progressive diseases and stable diseases. Clinical characteristics, DCE-MRI parameters (Ktrans, Kep, Ve), and ADC values before NACT were analyzed and compared between the two groups. RESULTS: SR group and non-SR group were documented in 35 and 28 patients. The mean Ktrans value, 90th percentile Ktrans value, maximal Ktrans value, and 90th percentile ADC value of tumors in SR were significantly higher than those in non-SR group (P = 0.012, P = 0.022, P = 0.005, P = 0.033, respectively), and the mean Ve value and 10th percentile Ve value of tumors were significantly lower in SR group (P = 0.041, P = 0.033, respectively). Kep values did not significantly differ between SR and non-SR. The 90th percentile Ktrans value combined with the 90th percentile ADC value had the highest area under the curve at 0.740 (P = 0.003) to predict NACT effectiveness. CONCLUSION: Histogram analysis of DCE-MRI multi-parameters combined with ADC values may serve as sensitive indicators for predicting NACT effectiveness in cervical cancers.

Combining tumor size and diffusion-weighted imaging to diagnose normal-sized metastatic pelvic lymph nodes in cervical cancers.

BACKGROUND: Detecting normal-sized metastatic pelvic lymph nodes (LNs) in cervical cancers, although difficult, is of vital importance. PURPOSE: To investigate the value of diffusion-weighted-imaging (DWI), tumor size, and LN shape in predicting metastases in normal-sized pelvic LNs in cervical cancers. MATERIAL AND METHODS: Pathology confirmed cervical cancer patients with complete magnetic resonance imaging (MRI) were documented from 2011 to 2016. A total of 121 cervical cancer patients showed small pelvic LNs (<5 mm) and 92 showed normal-sized (5-10 mm) pelvic LNs (39 patients with 55 nodes that were histologically metastatic, 53 patients with 71 nodes that were histologically benign). Preoperative clinical and MRI variables were analyzed and compared between the metastatic and benign groups. RESULTS: LN apparent diffusion coefficient (ADC) values and short-to-long axis ratios were not significantly different between metastatic and benign normal-sized LNs (0.98 ± 0.15 × 10-3 vs. 1.00 ± 0.18 × 10-3 mm2/s, P = 0.45; 0.65 ± 0.16 vs. 0.64 ± 0.16, P = 0.60, respectively). Tumor ADC value of the metastatic LNs was significantly lower than the benign LNs (0.98 ± 0.12 × 10-3 vs. 1.07 ± 0.21 × 10-3 mm2/s, P = 0.01). Tumor size (height) was significantly higher in the metastatic LN group (27.59 ± 9.18 mm vs. 21.36 ± 10.40 mm, P < 0.00). Spiculated border rate was higher in the metastatic LN group (9 [16.4%] vs. 3 [4.2%], P = 0.03). Tumor (height) combined with tumor ADC value showed the highest area under the curve of 0.702 ( P < 0.00) in detecting metastatic pelvic nodes, with a sensitivity of 59.1% and specificity of 78.8%. CONCLUSIONS: Tumor DWI combined with tumor height were superior to LN DWI and shape in predicting the metastatic state of normal-sized pelvic LNs in cervical cancer patients.

Multicystic mucinous adenocarcinoma of the uterine cervix compared with benign multicystic lesions: Multiparametric MR features.

BACKGROUND: Multicystic mucinous adenocarcinoma is rarely recognized and has a high misdiagnosis rate. PURPOSE: To distinguish malignant multicystic mucinous adenocarcinomas from benign multicystic lesions in the uterine cervix using multiparametric MR. STUDY TYPE: Retrospective. POPULATION: Forty patients with seven cystic mucinous adenocarcinomas and 33 benign multicystic lesions. FIELD STRENGTH/SEQUENCE: 3.0T. Diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) images. ASSESSMENT: Lesion size, intracystic hemorrhage, solid component, and heterogeneous enhancement were subjectively assessed, and apparent diffusion coefficient (ADC) values, Ktrans , Kep , and Ve parameters were compared. STATISTICAL ANALYSIS: Student's t-test was used to compare age, tumor size, ADC values, and DCE parameters. Pearson's chi-square test was used to compare intracystic hemorrhage, solid component, and heterogeneous enhancement. Receiver-operating-characteristic (ROC) analysis of ADC values, tumor size, and Ktrans were performed. RESULTS: The size of mucinous adenocarcinomas was larger than benign multicystic lesions (4.09 ± 2.09 vs. 2.23 ± 0.58 cm, P < 0.001); the area under the curve (AUC) for tumor size was 0.859 with a sensitivity of 71.4% and specificity of 90.9%. Stromal ADC value was lower for mucinous adenocarcinomas (1.19 ± 0.22 vs. 1.68 ± 0.22 × 10-3 mm2 /s, P < 0.001); AUC for stromal ADC value was 0.970, with a sensitivity of 86.4% and specificity of 100.0%. Among quantitative DCE parameters, only ktrans offered a discriminative value (1.72 ± 1.42 vs. 0.69 ± 0.30 min-1 , P = 0.031); the AUC for ktrans was 0.831 with a sensitivity of 71.4% and specificity of 97.0%. Intracystic hemorrhage (3/7), solid component (5/7), and heterogeneous enhancement (4/7) were only found in mucinous adenocarcinomas. Five patients (71.4%) had lymphovascular space invasion and three (42.9%) had lymph node metastasis. The 1-year tumor recurrence or metastasis rate was 28.5% (2/7). DATA CONCLUSION: Awareness of multiparametric MR features can assist in the differentiation of mucinous adenocarcinomas from benign multicystic lesions. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1336-1343.

In vivo MRI detection of atherosclerosis in ApoE-deficient mice by using tenascin-C-targeted USPIO.

BACKGROUND: Atherosclerosis is the main cause of cardiovascular and cerebrovascular diseases. Non-invasive molecular imaging to detect and characterize the plaques is essential for reducing life-threatening cardiovascular events. PURPOSE: To investigate the possibility of the anti-tenascin-C-USPIO specific probe as a molecular marker of atherosclerotic plaques detected by 7.0-T magnetic resonance imaging (MRI). MATERIAL AND METHODS: Twenty ApoE-/- mice fed with a high fat diet were used for detecting the aorta arch atherosclerotic plaques by 7.0-T MRI at 16 and 24 weeks. Ten mice in the targeted group were injected with anti-tenascin-C-USPIO and another ten in the control group were injected with pure USPIO (n = 5 each time point in each group). Histopathologic examination was used to evaluate the plaques and immunohistochemistry analysis was used to compare tenascin-C expression. RESULTS: The relative signal intensity (rSI) changes of the targeted group decreased more than those of the control group (16 weeks: -15.65 ± 0.78% vs. -3.43 ± 2.57%; 24 weeks: -26.38 ± 1.54% vs. -11.12 ± 1.60%, respectively; P < 0.05). Histopathological analyses demonstrated visible atherosclerotic plaques formation and development over time from 16 weeks to 24 weeks. Tenascin-C expression of the plaques at 24 weeks was higher than that at 16 weeks (0.22 ± 0.04 vs. 0.13 ± 0.02, P < 0.05). The MR images correlated well with the progression of atherosclerotic plaques. CONCLUSION: Tenascin-C expression increased with the progression of atherosclerosis. Anti-tenascin-C-USPIO could provide a useful molecular imaging tool for detecting and monitoring atherosclerotic plaques by MRI.

MR targeted imaging for the expression of tenascin-C in myocardial infarction in vivo.

PURPOSE: To investigate the presence of viable myocardium in mice with acute myocardial infarction (MI) using a molecular targeted probe. MATERIALS AND METHODS: Super paramagnetic iron oxide (SPIO) nanoparticles and tenascin-C antibody were conjugated as an MRI probe. Fifteen mice with infarction were injected with SPIO-anti-tenascin-C (3 days [d], 5d, 7d after infarction; n = 5 for each group). Another five mice with infarction (5d, n = 5) were injected with SPIO for comparison. In vivo MR (7 Tesla, fast low-angle shot multi-slice T2* sequence) was performed for tracing. Histological analysis was used to compare surviving cardiomyocytes with signal changes on MR. RESULTS: The mRNA expression of tenascin-C increased directly after MI and peaked at the fifth day (5d 24.29 ± 1.41 versus 3d 10.63 ± 0.72, 7d 6.56 ± 0.12; P < 0.01). T2 relaxation rate of synthesized SPIO-anti-tenascin-C was r2 = 338 mM-1 s-1 . After MR, the signal changes (contrast-to-noise ratio) of the research group were 3d 6.51 ± 1.13 versus 5d 14.06 ± 3.19 versus 7d 5.02 ± 2.65, P < 0.05. The MR signal showed a small decrease in the contrast group on 5d (research group 14.06 ± 3.19 versus contrast group 1.75 ± 0.59, P < 0.05). CONCLUSION: Tenascin-C was expressed by surviving cardiomyocytes within the infarcted region. MR imaging with SPIO-anti-tenascin-C might be used to evaluate myocardial viability of MI patients before therapy. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2017;45:1668-1674.

Prognostic aspects of dynamic contrast-enhanced magnetic resonance imaging in synchronous distant metastatic rectal cancer.

OBJECTIVES: To explore the correlations between DCE-MRI quantitative parameters and synchronous distant metastasis and the clinicopathological factors in rectal cancers. METHODS: Sixty-three patients with rectal cancer (synchronous distant metastasis, n = 31; non-metastasis, n = 32) were enrolled in this study. Student's t test and ANOVA were used to compare DCE-MRI parameters (K trans , K ep and V e ). The receiver operating characteristic (ROC) analysis was used to find the reasonable threshold of DCE-MRI parameters to differentiate lesions with synchronous distant metastasis from those without metastasis. RESULTS: The K trans , K ep , and V e value were significantly higher in the lesions with distant metastasis than in the lesions without distant metastasis (0.536 ± 0.242 vs. 0.299 ± 0.118 min-1, p < 0.001; 1.598 ± 0.477 vs. 1.341 ± 0.390 min-1, p = 0.022; and 0.324 ± 0.173 vs. 0.249 ± 0.091, p = 0.034; respectively). The K trans showed the highest AUCs of 0.788 (p < 0.001), with sensitivity of 61.29 % and specificity of 87.5 %, respectively. CONCLUSIONS: DCE-MRI parameters may represent a prognostic indicator for synchronous distant metastases in patients with rectal cancer. KEY POINTS: • The K trans , K ep and V e values correlated with synchronous distant metastasis. • Higher K trans , K ep and V e values were noted among patients with metastasis. • DCE-MRI parameters might represent a prognostic indicator for synchronous distant metastases.

The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To evaluate the feasibility and value of diffusion kurtosis (DK) imaging in assessing treatment response to neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC). METHODS: Forty-one patients were included. All patients underwent pre- and post-CRT DCE-MRI on a 3.0-Tesla MRI scanner. Imaging indices (D app , K app and ADC values) were measured. Change value (∆X) and change ratio (r∆X) were calculated. Pathological tumour regression grade scores (Mandard) were the standard reference (good responders: pTRG 1-2; poor responders: pTRG 3-5). Diagnostic performance was compared using ROC analysis. RESULTS: For the pre-CRT measurements, pre-D app-10th was significantly lower in the good responder group than that of the poor responder group (p = 0.036). For assessing treatment response to neoadjuvant CRT, pre-D app-10th resulted in AUCs of 0.753 (p = 0.036) with a sensitivity of 66.67 % and a specificity of 77.78 %. The r∆D app had a relatively high AUC (0.859) and high sensitivity (100 %) compared with other image indices. CONCLUSIONS: DKI is feasible for selecting good responders for neoadjuvant CRT for LARC. KEY POINTS: • LARC responded well after neoadjuvant chemoradiotherapy with lower pre-D app-10th . • LARC responded well with greater increases in mean ADC and D app . • The change ratio of D app (r∆D app ) had a relatively better diagnostic performance.

Potential Utility of Visually AcceSAble Rembrandt Images Assessment in Brain Astrocytoma Grading.

OBJECTIVE: The aim of this study was to evaluate the predictive value of multivariate factors of Visually AcceSAble Rembrandt Images (VASARI) in brain astrocytoma grading. METHODS: Presurgical magnetic resonance images of 126 patients with brain astrocytomas (World Health Organization grade 2, n = 38; grade 3, n = 36; grade 4, n = 52) were rated by 2 neuroradiologists for tumor size, location, and tumor morphology by using a standardized imaging feature set VASARI. RESULTS: Significant differences were noted in 12 factors of VASARI including enhancement quality, enhancing proportion, noncontrast enhancing tumor proportion, necrosis proportion, edema proportion, hemorrhage, thickness of enhancing margin, definition of the enhancing margin, pial and ependymal invasion, enhanced tumor crossing midline, and satellites between brain astrocytoma grades (grades 1-IV, P < 0.05). On multivariate regression analysis, enhancement quality was an independent diagnostic factor for high-grade brain astrocytoma, whereas edema proportion was an independent diagnostic factor in differentiating grade 2 and grade 3. Noncontrast enhancing tumor proportion was a predictive factor in the diagnosis of grade 4 astrocytoma. Receiver operating characteristic analysis illustrates edema proportion score higher than 2 with sensitivity of 86.1% in differentiating grade 2 and grade 3 astrocytoma. Noncontrast enhancing tumor proportion scores 4 or lower has high sensitivity (92.3%) but moderate specificity (50.0%) in differentiating grade 3 and grade 4 astrocytoma. CONCLUSIONS: Our data illustrate that magnetic resonance features of VASARI especially enhancement quality, edema proportion, and noncontrast enhancing tumor proportion provided precise and detailed information of astrocytoma grading and suggested that prediction of astrocytoma grading is based on VASARI as an adjunct to biopsy.

Distal hyperintense vessels alleviate insula infarction in proximal middle cerebral artery occlusion.

PURPOSE: Insula involvement in acute cerebral ischemia more likely causes penumbral loss and poor clinical outcome than infarct-sparing insula. Our objective was to prove the hypothesis that abundant collateral circulation represented by distal hyperintense vessels (HV) on MRI alleviates insula infarction and facilitates prognosis. MATERIAL AND METHODS: One hundred and fourteen stroke cases with M1 totally occlusion on MR angiography were documented consecutively from 2012 to 2014. The degree of HV was graded as absent, subtle or prominent. Clinical data were recorded retrospectively by reviewing the medical records. The infarct volume on diffusion-weighted image, along with National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS), was used to evaluate the clinical severity and prognosis. RESULTS: The degree of HV was more abundant in insula-uninvolved stroke compared with stroke involving insula infarction (p = 0.026). Insula-involved stroke patients were older (p = 0.039) with a higher percentage of atrial fibrillation history (p = 0.042). Univariate analysis revealed that insula infarction, age, infarct volume and NIHSS predicted unfavorable prognosis of stroke, whereas HV had a favorable effect. The protective effect of HV was confirmed by multivariate analysis. CONCLUSION: HV is a protective barrier between insula infarction and severity of clinical symptoms among stroke patients.

Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.

This study aims to propose a novel approach for enhancing clinical prediction models by combining structured and unstructured data with multimodal data fusion. We presented a comprehensive framework that integrated multimodal data sources, including textual clinical notes, structured electronic health records (EHRs), and relevant clinical data from National Electronic Injury Surveillance System (NEISS) datasets. We proposed a novel hybrid fusion method, which incorporated state-of-the-art pre-trained language model, to integrate unstructured clinical text with structured EHR data and other multimodal sources, thereby capturing a more comprehensive representation of patient information. The experimental results demonstrated that the hybrid fusion approach significantly improved the performance of clinical prediction models compared to traditional fusion frameworks and unimodal models that rely solely on structured data or text information alone. The proposed hybrid fusion system with RoBERTa language encoder achieved the best prediction of the Top 1 injury with an accuracy of 75.00% and Top 3 injuries with an accuracy of 93.54%. Our study highlights the potential of integrating natural language processing (NLP) techniques with multimodal data fusion for enhancing clinical prediction models' performances. By leveraging the rich information present in clinical text and combining it with structured EHR data, the proposed approach can improve the accuracy and robustness of predictive models. The approach has the potential to advance clinical decision support systems, enable personalized medicine, and facilitate evidence-based health care practices. Future research can further explore the application of this hybrid fusion approach in real-world clinical settings and investigate its impact on improving patient outcomes.

The Factors for the Occurrence of Pulmonary Infection after Gastrointestinal Surgery and the Construction of a Predictive Model Using sTREM-1 and TIM-4: A Retrospective Study.

AIM: Identifying and intervening with high-risk postoperative pulmonary infections patients pose challenges in clinical practice. This study aims to conduct a comprehensively analysis of the risk factors and predictive factors associated with post-gastrointestinal surgery pulmonary infections and to develop a predictive model that can predict occurrence of pulmonary infection. METHODS: A retrospective analysis was conducted on 96 patients who underwent gastrointestinal surgery at our hospital from May 2021 to October 2023. The occurrence rate of postoperative pulmonary infections was calculated, and patients were categorized into two groups: those with pulmonary infections (the occurrence group) and those without pulmonary infections (the non-occurrence group). Logistic regression analysis was utilized to identify the risk factors for post-gastrointestinal surgery pulmonary infections and to evaluate the predictive value of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and T cell immunoglobulin and mucin domain-4 (TIM-4) using nomograms, calibration curves, and Receiver Operating Characteristic (ROC) curves. RESULTS: Out of 96 patients, 20 (20.83%) developed postoperative pulmonary infections. Significant differences were noted between occurrence and non-occurrence groups in terms of smoking (65.00% vs. 34.21%, p = 0.013), surgical duration (70.00% vs. 31.58%, p = 0.002), Preoperative hemoglobin level (35.00% vs. 65.79%, p = 0.013), sTREM-1 levels (23.57 ± 3.16 pg/mL vs. 15.62 ± 2.48 pg/mL, p < 0.001), and TIM-4 levels (61.48 ± 6.35 pg/mL vs. 44.73 ± 5.22 pg/mL, p < 0.001). Logistic regression analysis leads to the development of a risk prediction model for post-gastrointestinal surgery pulmonary infections. The high predictive values of sTREM-1 (Area Under Curve (AUC) = 0.962, 95% confidence interval (CI) 0.917~0.999) and TIM-4 (AUC = 0.970, 95% CI 0.925~1.000) were highlighted by the AUC values, underscoring their clinical importance. CONCLUSIONS: A predictive model utilizing sTREM-1 and TIM-4 for pulmonary infection following gastrointestinal surgery was developed. Additionally, other risk factors such as smoking, surgical duration, and preoperative hemoglobin level were evaluated. This finding can be applied in clinical practice to identify potentially susceptible patients and facilitate early intervention measures.

Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor.

PURPOSE: To investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. METHODS: Totally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. RESULTS: In this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. CONCLUSION: Time-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

Mechanoelectronic stimulation of autologous extracellular vesicle biosynthesis implant for gut microbiota modulation.

Pathogenic gut microbiota is responsible for a few debilitating gastrointestinal diseases. While the host immune cells do produce extracellular vesicles to counteract some deleterious effects of the microbiota, the extracellular vesicles are of insufficient doses and at unreliable exposure times. Here we use mechanical stimulation of hydrogel-embedded macrophage in a bioelectronic controller that on demand boost production of up to 20 times of therapeutic extracellular vesicles to ameliorate the microbes' deleterious effects in vivo. Our miniaturized wireless bioelectronic system termed inducible mechanical activation for in-situ and sustainable generating extracellular vesicles (iMASSAGE), leverages on wireless electronics and responsive hydrogel to impose mechanical forces on macrophages to produce extracellular vesicles that rectify gut microbiome dysbiosis and ameliorate colitis. This in vivo controllable extracellular vesicles-produced system holds promise as platform to treat various other diseases.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.

Objective: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination. Methods: We collected Tweet posts by the residents in the United States after the dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially. Results: A total of 3 198 686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2 358 783 Tweets were identified to contain clear opinions, among which 824 755 (35.0%) expressed negative opinions towards vaccination while 1 534 028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity. Conclusion: We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.