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Background: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance. [ABSTRACT FROM AUTHOR]

Objective: Ischemic stroke seriously threatens human health, characterized by the high rates of incidence, disability, and death. Developing a reliable animal model that mimics most of the features of stroke is critical for pathological studies and clinical research. In this study, we aimed to establish and examine a model of middle cerebral artery occlusion (MCAO) guided by digital subtraction angiography (DSA) in cynomolgus monkeys. Materials and Methods: In this study, 15 adult male cynomolgus monkeys were enrolled. Under the guidance of DSA, a MCAO model was established by injecting an autologous venous clot into the middle cerebral artery (MCA) via femoral artery catheter. Thrombolytic therapy with alteplase (rt-PA) was given to eight of these monkeys at 3 h after the occlusion. Blood test and imaging examination, such as computed tomography angiography (CTA), CT perfusion (CTP), brain magnetic resonance imaging (MRI), and brain magnetic resonance angiography (MRA), were performed after the operation to identify the post-infarction changes. The behavioral performance of cynomolgus monkeys was continuously observed for 7 days after operation. The animals were eunthanized on the 8th day after operation, and then the brain tissues of monkeys were taken for triphenyltetrazolium chloride (TTC) staining. Results: Among the 15 cynomolgus monkeys, 12 of them were successfully modeled, as confirmed by the imaging findings and staining assessment. One monkey died of brain hernia resulted from intracranial hemorrhage confirmed by necropsy. DSA, CTA, and MRA indicated the presence of an arterial occlusion. CTP and MRI showed acute focal cerebral ischemia. TTC staining revealed infarct lesions formed in the brain tissues. Conclusion: Our study may provide an optimal non-human primate model for an in-depth study of the pathogenesis and treatment of focal cerebral ischemia. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to evaluate the computed diffusion-weighted images (DWI) in image quality and diagnostic performance of rectal cancer by comparing with the acquired DWI. Methods: A total of 103 consecutive patients with primary rectal cancer were enrolled in this study. All patients underwent two DWI sequences, namely, conventional acquisition with b = 0 and 1,000 s/mm2 (aDWIb1,000) and another with b = 0 and 700 s/mm2 on a 3.0T MR scanner (MAGNETOM Prisma; Siemens Healthcare, Germany). The images (b = 0 and 700 s/mm2) were used to compute the diffusion images with b value of 1,000 s/mm2 (cDWIb1,000). Qualitative and quantitative analysis of both computed and acquired DWI images was performed, namely, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal intensity ratio (SIR), and also diagnostic staging performance. Interclass correlation coefficients, weighted κ coefficient, Friedman test, Wilcoxon paired test, and McNemar or Fisher test were used for repeatability and comparison assessment. Results: Compared with the aDWIb1,000 images, the cDWIb1,000 ones exhibited significant higher scores of subjective image quality (all P <0.050). SNR, SIR, and CNR of the cDWIb1,000 images were superior to those of the aDWIb1,000 ones (P <0.001). The overall diagnostic accuracy of computed images was higher than that of the aDWIb1,000 images in T stage (P <0.001), with markedly better sensitivity and specificity in distinguishing T1–2 tumors from the T3–4 ones (P <0.050). Conclusion: cDWIb1,000 images from lower b values might be a useful alternative option and comparable to the acquired DWI, providing better image quality and diagnostic performance in preoperative rectal cancer staging. [ABSTRACT FROM AUTHOR]

Background: To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC). Methods: By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods. Results: Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80–0.87, I 2 = 76.44%) and 0.83 (95% CI: 0.78–0.87, I 2 = 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value. Conclusion: Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: To explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer. Methods: The clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n =123) and validation set (n = 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated. Results: There is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set. Conclusions: We revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients. [ABSTRACT FROM AUTHOR]

A novel negative-sense, single-stranded (ss) RNA virus was identified in a "Shennong Jinhuanghou" (SJ) grapevine showing severe chlorotic mottling symptoms by integrating high-throughput sequencing (HTS) and conventional Sanger sequencing of reverse transcription polymerase chain reaction (RT-PCR) products. The virus was provisionally named as "grapevine emaravirus A" (GEVA). GEVA had a genome comprising five genomic RNA segments, each containing a single open reading frame on the viral complementary strand and two untranslated regions with complementary 13- nt stretches at the 5′ and 3′ terminal ends. RNA1 (7,090 nt), RNA2 (2,097 nt), RNA3 (1,615 nt), and RNA4 (1,640 nt) encoded putative proteins P1–P4 that, based on their conserved motifs, were identified as the RNA-dependent RNA polymerase, glycoprotein, nucleocapsid protein, and movement protein, respectively. However, the functional role of protein P5 encoded by RNA5 (1,308 nt) could not be determined. Phylogenetic trees constructed based on amino acids of P1 to P4, allocated GEVA in clade I, together with other species-related emaraviruses. These data support the proposal that GEVA is a representative member of a novel species in the genus Emaravirus of the family Fimoviridae. Moreover, when GEVA was graft-transmitted to SJ and "Beta" grapevines, all grafted plants showed the same symptoms, similar to those observed in the source of the inoculum. This is the first report to our knowledge of an emaravirus infecting grapevine and its possible association with chlorotic mottling symptoms. [ABSTRACT FROM AUTHOR]