Reading disability is characterized by reduced print-speech convergence.

Reading disability (RD) may be characterized by reduced print-speech convergence, which is the extent to which neurocognitive processes for reading and hearing words overlap. We examined how print-speech convergence changes from children (mean age: 11.07+0.48) to adults (mean age: 21.33+1.80) in 86 readers with or without RD. The participants were recruited in elementary schools and associate degree colleges in China (from 2020 to 2021). Three patterns of abnormalities were revealed: (1) persistent reduction of print-speech convergence in the left inferior parietal cortex in both children and adults with RD, suggesting a neural signature of RD; (2) reduction of print-speech convergence in the left inferior frontal gyrus only evident in children but not adults with RD, suggesting a developmental delay; and (3) increased print-speech convergence in adults with RD than typical adults in the bilateral cerebella/fusiform, suggesting compensations. It provides insights into developmental differences in brain functional abnormalities in RD.

Shared and task-specific brain functional differences across multiple tasks in children with developmental dyslexia.

Different tasks have been used in examining the neural functional differences associated with developmental dyslexia (DD), and consequently, different findings have been reported. However, very few studies have systematically compared multiple tasks in understanding what specific task differences each brain region is associated with. In this study, we employed an auditory rhyming task, a visual rhyming task, and a visual spelling task, in order to investigate shared and task-specific neural differences in Chinese children with DD. First, we found that children with DD had reduced activation in the opercular part of the left inferior frontal gyrus (IFG) only in the two rhyming tasks, suggesting impaired phonological analysis. Children with DD showed functional differences in the right lingual gyrus/inferior occipital gyrus only in the two visual tasks, suggesting deficiency in their visuo-orthographic processing. Moreover, children with DD showed reduced activation in the left dorsal inferior frontal gyrus and increased activation in the right precentral gyrus across all of the three tasks, suggesting neural signatures of DD in Chinese. In summary, our study successfully separated brain regions associated with differences in orthographic processing, phonological processing, and general lexical processing in DD. It advances our understanding about the neural mechanisms of DD.

Comparative analysis of holmium: YAG laser internal urethrotomy versus Cold-Knife optical internal urethrotomy in the management of urethral stricture - a systematic review and meta-analysis.

OBJECTIVE: Urethral stricture management has posed enduring challenges in urology, demanding innovative and effective treatment modalities. This study addresses the persistent issues associated with urethral strictures, focusing on the comparative evaluation of two contemporary treatment modalities: Ho: YAG Laser Internal Urethrotomy (HIU) and Cold Knife Optical Internal Urethrotomy (CIU). METHODS: A comprehensive search of PubMed, Embase, Web of Science, Cochrane Library, and ResearchGate was conducted to identify relevant studies up to November 2023. Inclusion criteria encompassed comparative studies evaluating HIU and CIU in patients with urethral stricture. Data extraction, quality assessment, and subgroup analyses were performed using standardized methods. Outcome measures included preoperative and postoperative mean the maximum urine flow rate (Qmax), mean surgical time, recurrence rates, and perioperative complications. RESULTS: Nine articles met the inclusion criteria, and their data were analyzed using RevMan 5.4.1. Forest plots were generated for preoperative and postoperative mean Qmax, mean surgical time, recurrence rates, and perioperative complications. While postoperative mean Qmax (MD -0.06; 95% CI: -0.28-0.16; P =0.60, I2 =75%) and mean surgical time (MD 2.16; 95% CI: -1.66-5.99; P =0.27, I2 =98%) showed no significant differences between HIU and CIU, a trend towards lower recurrence rates (RR 0.71; 95% CI: 0.48-1.06; P =0.09, I2 =46%) was observed with HIU but without statistical significance. Perioperative complications, particularly bleeding (RR 0.21; 95% CI: 0.08-0.53; P =0.001, I2 =0%), favored HIU over CIU. The subanalysis indicates that for the treatment of complex urethral strictures, the two surgical methods differ in terms of mean Qmax at 6 months postoperatively (MD -2.51; 95% CI: -4.10--0.91; P =0.002, I2 =59%) and 12 months postoperatively (MD 2.62; 95% CI: 0.93-4.30; P =0.002, I2 =0%). The HIU group shows a significant decrease in recurrence rate at 12 months postoperatively (RR 0.44; 95% CI: 0.21-0.92; P =0.03, I2 =0%). For short-segment urethral strictures with a length ≤1.5 cm, CIU had a shorter operative time compared to the HIU (MD 4.49; 95% CI: 3.87-5.10; P <0.00001, I2 =44%). CONCLUSION: Overall, both interventions demonstrated similar efficacy in improving postoperative mean Qmax, mean surgical time, and recurrence rates. However, subanalysis indicates that in the short-term, CIU is more effective than HIU in improving Qmax after complex urethral stricture surgery. In long-term Qmax follow-up after surgery, HIU is significantly higher than CIU, and HIU has a low recurrence rate. In addition, for short-segment urethral stricture (≤1.5 cm), CIU requires less time. In terms of complications, HIU has a lower risk of bleeding.

Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling.

Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.

Exploring the Role of Modified Vascular Anatomical Molding (MVAM) in Prenatal Diagnosis Teaching and Prognosis Prediction of Fetal Complex Congenital Heart Disease (CCHD): A Preliminary Study.

Objective: The present study aimed to explore the role of modified vascular anatomical molding (MVAM) in prenatal diagnosis teaching and prognosis prediction of fetal complex congenital heart disease (CCHD). Methods: Step 1, MVAM method was used to cast the micro-blood vessels and trachea of 52 CCHD specimens. Subsequently, 52 MVAMs were analyzed and compared with the prenatal ultrasound to summarize their characteristics, misdiagnosis and MVAM's teaching role. Step 2, the surgical and follow-up data of 206 CCHD cases were retrospectively analyzed. Cases that evolved into critical illnesses or died within 1-3 years after surgery (poor prognosis) were classified into the study group (n = 77) and those with good prognosis into the control group (n = 129), which were split into the training set and the test set in the ratio 7:3 based on the time cut-off. In the training set, the prognosis of CCHD was predicted using the MVAM anatomical soft markers (distortion and narrowing of aorta/pulmonary artery, right ventricular infundibulum, etc.) and the decision curve analysis (DCA) performed. The model was validated using the test set, and a nomogram was finally established. Results: It was observed that all 52 CCHD cases were confirmed using MVAM. A total of 91 cardiac malformations were recorded, among which 41 malformations were misdiagnosed, and 29 malformations were missed by the prenatal echocardiography. The MVAM method has a good teaching/feedback effect on prenatal diagnosis. The combined model exhibited a higher predictive performance in the training- and test-set. Its high clinical net benefit was proved by DCA. Additionally, the nomogram established using the combined model received a favorable response in clinical practice. Conclusion: The research results indicated that MVAM improved the prenatal diagnosis teaching and training performance. The combined model established based on MVAM anatomical soft markers can offer a high clinical significance for prognosis prediction of CCHD.

Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data.

Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years' follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time < 6 months), and further the training set and test set were set at a ratio of 7/3. Then logistic regression was conducted to find independent risk factors, establish predictive models, and further the models were validated. Results: The Kaplan-Meier analysis showed that age, diabetes, tumor, node, and metastasis stage, CT enhancement mode, peripancreatic lymph node swelling, nerve invasion, surgery in a top hospital, tumor size, carbohydrate antigen 19-9, carcinoembryonic antigen, Radscore 1/2/3 were the influencing factors of PBTC recurrence. The overall average survival was 7.4 months in this study. The multivariate logistic analysis confirmed that nerve invasion, surgery in top hospital, dilation of the main pancreatic duct, and Radscore 2 were independent factors affecting the mortality of PBTC (P < .05). In the test set, the combined model achieved the best predictive performance [AUC 0.944, 95% CI (0.826-0.991)], significantly superior to the clinicopathological model [AUC 0.770, 95% CI (0.615-0.886), P = .0145], and the CT radiomics model [AUC 0.883, 95% CI (0.746-0.961), P = .1311], with a good clinical net benefit confirmed by decision curve. The same results were subsequently validated on the test set. Conclusion: The diagnosis and treatment of PBTC are challenging, and survival is poor. Nevertheless, the combined model benefits the clinical management and prognosis of PBTC.

PD-L1 Blockade Peptide-Modified Polymeric Nanoparticles for Oxygen-Independent-Based Hypoxic Tumor Photo/Thermodynamic Immunotherapy.

Distant metastasis of malignant tumors is considered to be the main culprit for the failure of current antitumor treatments. Conventional single treatments often exhibit limited efficacy in inhibiting tumor metastasis. Therefore, there is a growing interest in developing collaborative antitumor strategies based on photothermal therapy (PTT) and free-radical-generated photodynamic therapy (PDT), especially utilizing oxygen-independent nanoplatforms, to address this challenge. Such antitumor strategies can enhance the therapeutic outcomes by ensuring the cytotoxicity of free radicals even in the hypoxic tumor microenvironment, thereby improving the effective suppression of primary tumors. Additionally, these approaches can stimulate the production of tumor-associated antigens and amplify the immunogenic cell death (ICD) effects, potentially feasible for enhancing the therapeutic outcomes of immunotherapy. Herein, we fabricated a functional nanosystem that co-loads IR780 and 2,2'-azobis[2-(2-imidazolin-2-yl)propane]-dihydrochloride (AIPH) to realize PTT-triggered thermodynamic combination therapy via the oxygen-independent pathway for the elimination of primary tumors. Furthermore, the nanocomposites were surface-decorated with a predesigned complex peptide (PLGVRGC-anti-PD-L1 peptide, MMP-sensitive), which facilitated the immunotherapy targeting distant tumors. Through the specific recognition of matrix metalloproteinase (MMP), the sensitive segment on the obtained aNC@IR780A was cleaved. As a result, the freed anti-PD-L1 peptide effectively blocked immune checkpoints, leading to the infiltration and activation of T cells (CTLs). This nanosystem was proven to be effective at inhibiting both primary tumors and distant tumors, providing a promising combination strategy for tumor PTT/TDT/immunotherapy.

Clinical Features and Computed Tomography Radiomics-Based Model for Predicting Pancreatic Ductal Adenocarcinoma and Focal Mass-Forming Pancreatitis.

Objective: To establish a predictive model distinguishing focal mass-forming pancreatitis (FMFP) from pancreatic ductal adenocarcinoma (PDAC) based on computed tomography (CT) radiomics and clinical data. Methods: A total of 78 FMFP patients (FMFP group) and 120 PDAC patients (PDAC group) who were admitted to Xiangyang No.1 People's Hospital and Xiangyang Central Hospital from February 2012 to May 2021 and were pathologically diagnosed were included in this study, and were input to set up the training set and test set at a ratio of 7:3. The 3Dslicer software was used to extract the radiomic features and radiomic scores (Radscores) of the 2 groups, and the clinical data (age, gender, etc), CT imaging features (lesion location, size, enhancement degree, vascular wrapping, etc) and CT radiomic features of the 2 groups were compared. Logistic regression was used to screen the independent risk factors of the 2 groups, and multiple prediction models (clinical imaging model, radiomics model, and combined model) were established. Then the receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were conducted to compare the prediction performance and net benefit of the models. Results: The multivariate logistic regression results indicated that dilation of the main pancreatic duct, vascular wrapping, Radscore1 and Radscore2 were independent influencing factors for distinguishing FMFP from PDAC. In the training set, the combined model showed the best predictive performance (area under the ROC curve [AUC] 0.857, 95% CI [0.787-0.910]), significantly higher than the clinical imaging model (AUC 0.650, 95% CI [0.565-0.729]) and the radiomics model (AUC 0.812, 95% CI [0.759-0.890]). DCA confirmed that the combined model had the highest net benefit. These results were further validated by the test set. Conclusion: The combined model based on clinical-CT radiomics data can effectively identify FMFP and PDAC, providing a reference for clinical decision-making.

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study.

OBJECTIVE: To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. METHOD: We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. RESULT: For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. CONCLUSION: Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics.

OBJECTIVE: To build a combined model that integrates clinical data, contrast-enhanced ultrasound, and magnetic resonance perfusion-weighted imaging-based radiomics for predicting the possibility of biochemical recurrence of prostate carcinoma and develop a nomogram tool. METHOD: We retrospectively analyzed the clinical, ultrasound, and magnetic resonance imaging data of 206 patients pathologically confirmed with prostate carcinoma and receiving radical prostatectomy at Xiangyang No. 1 People's Hospital from February 2015 to August 2021. Based on one to 7 years of follow-up (prostate specific antigen [PSA] level≥0.2 ng/mL, indicative of prostate carcinoma-biochemical recurrence), the patients were divided into biochemical recurrence group (n = 77) and normal group (n = 129). The training and testing sets were formed by dividing the patients at a 7:3 ratio. In training set, The magnetic resonance perfusion-weighted imaging-based radiomics radscore was generated using lasso regression. Several predictive models were built based on the patients' clinical imaging data. The predictive efficacy (area under the curve) of these models was compared using the MedCalc software. The decision curve analysis was conducted using the R to compare the net benefit. Finally, an external validation was carried out on the testing set, and the nomogram tool was developed for predicting prostate carcinoma-biochemical recurrence. RESULT: The univariate analysis confirmed that Tumor diameter, tumor node metastasis classification stage of tumor, lymph node metastasis or distance metastasis, Gleason grade, preoperative PSA, ultrasound (peak intensity, arrival time, and elastography grade), and magnetic resonance imaging-radscore1/2 were predictors of prostate carcinoma-biochemical recurrence. On the training set, the combined model based on the above factors had the highest predictive efficacy for prostate carcinoma-biochemical recurrence (area under the curve: 0.91; odds ratio 0.02, 95% confidence interval: 0.85-0.95). The predictive performance of the combined model was significantly higher than that of the model based on general clinical data (area under the curve: 0.74; odds ratio 0.04, 95% confidence interval: 0.67-0.81, P < .05), contrast-enhanced ultrasound (area under the curve: 0.61; odds ratio 0.05 95% confidence interval: 0.53-0.69, P < .05), and the magnetic resonance imaging-based radiomics model (area under the curve: 0.85; odds ratio 0.03, 95% confidence interval: 0.78-0.91, P = .01). The decision curve analysis also indicated the maximum net benefit derived from the combined model, which agreed with the validation results on the testing set. The nomogram tool developed based on the combined model achieved a good performance in clinical applications. CONCLUSION: The magnetic resonance imaging texture parameters extracted by magnetic resonance perfusion-weighted imaging Lasso regression could help increase the accuracy of the predictive model. The combined model and the nomogram tool provide support for the clinical screening of the populations at a risk for biochemical recurrence.

Distinct neural correlates of poor decoding and poor comprehension in children with reading disability.

Reading disability (RD) can manifest itself as a word decoding problem or a reading comprehension problem. In the current study, we identified 3 subtypes of RD: poor decoders (PD), poor comprehenders (PC), and poor-in-both (PB). We found that PD had greater deficits in meta-linguistic skills such as phonological awareness, orthographic skills, and morphological skills than PC, whereas PC had greater deficits in listening comprehension than PD. In the brain, we also found different patterns of deficits during an auditory rhyming judgment task using functional magnetic resonance imaging. PD showed less activation than PC and age controls in the left dorsal inferior frontal gyrus (IFG) and pre-supplementary motor area (SMA), brain activation of which was correlated with phonological awareness and working memory. In contrast, PC showed less activation in the left fusiform gyrus than PD and age controls, which was correlated with reading comprehension fluency and morphological skill. Last, PB showed both PD's and PC's deficits, as well as additional deficits in the bilateral lingual gyri. Our findings contribute to revealing different neural signatures of poor decoding and poor comprehension, which are distinct disorders but co-occur very often. These findings implicate possibility and necessity of precise diagnosis and individualized intervention.

Mindfulness and negative emotions among Chinese college students: chain mediation effect of rumination and resilience.

Objective: This study examines the mediation effect of rumination and resilience between the relationship of mindfulness and negative emotions in Chinese college students. Method: A total of 3,038 college students (19.94 ± 1.10) were investigated by Mindfulness Attention Awareness Scale (MASS), Rumination Response Style Scale (RRS), Resilience Scale (RES) and Depression-anxiety-pressure scale (DASS-21), and the mediation analyses were conducted by adopting PROCESS macro in the SPSS software. Results: ① Mindfulness was negatively associated with rumination and negative emotions (r = -0.69, -0.72; P < 0.01), and positively associated with resilience (r = 0.63, P < 0.01). Rumination was negatively associated with resilience (r = -0.59, P < 0.01), and positively associated with negative emotions (r = 0.83, P < 0.01). Resilience was negatively associated with negative emotions (r = -0.71, P < 0.01). ② Mindfulness can not only directly predict negative emotions (95%CI, -0.12~-0.09) but also affects negative emotions through three indirect paths: Rumination was a mediator (95%CI, -0.24~-0.20), resilience was a mediator (95%CI, -0.07~-0.06), and resilience and rumination were a chain mediator (95%CI, -0.04 ~ -0.03). Conclusion: Mindfulness not only influences negative emotions directly, but also through the mediating effect of rumination and resilience indirectly.

Reduced pattern similarity in brain activation during orthographic processing in children with developmental dyslexia.

Intra-individual variability of neural response has been found to be negatively associated with cognitive proficiency and automaticity. However, whether developmental dyslexia (DD) is marked by greater intra-individual neural variability remains unclear. Using a multivariate approach and dual-control group design, the current study aims to examine whether the pattern similarity of brain activation during a visual spelling task is abnormal in children with DD compared to age control and reading control children. We found that there was reduced intra-subject pattern similarity at the left occipito-temporal regions in children with DD than both control groups, suggesting a neural signature of DD. Furthermore, we found that pattern similarity was positively associated with stability of reaction time and reading fluency in both children with DD and typical control children, suggesting that neural stability supports behavioral stability and automaticity during reading.

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs).

Purpose: Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). Methods: In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People's Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group (n = 108) and the recurrent group (n = 117), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data (P < 0.05) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. Results: Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. Conclusion: The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.

Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study.

Objective: This study was aimed at developing a model for predicting postoperative biochemical recurrence of prostate cancer (PCa) using clinical data-CEUS-MRI radiomics and at verifying its clinical effectiveness. Methods: The clinical imaging data of 159 patients pathologically confirmed with PCa and who underwent radical prostatectomy in Xiangyang No. 1 People's Hospital and Jiangsu Hospital of Chinese Medicine from March 2016 to December 2020 were retrospectively analyzed. According to the 2-5-year follow-up results, the patients were divided into the biochemical recurrence (BCR) group (n = 59) and the control group (n = 100). The training set and test set were established in the proportion of 7/3; 4 prediction models were established based on the clinical imaging data. In training set, the area under the curve (AUC) and decision curve analysis (DCA) by R was conducted to compare the efficiency of 4 prediction models, and then, external validation was performed using the test set. Finally, a nomogram tool for predicting BCR was developed. Results: Univariate regression analysis confirmed that the SmallAreaHighGrayLevelEmphasis, RunVariance, Contrast, tumor diameter, clinical T stage, lymph node metastasis, distant metastasis, Gleason score, preoperative PSA, treatment method, CEUS-peak intensity (PI), time to peak (TTP), arrival time (AT), and elastography grade were the influencing factors for predicting BCR. In the training set, the AUC of combinatorial model demonstrated the highest efficiency in predicting BCR [AUC: 0.914 (OR 0.0305, 95% CI: 0.854-0.974)] vs. the general clinical data model, the CEUS model, and the MRI radiomics model. The DCA confirmed the largest net benefits of the combinatorial model. The test set validation gave consistent results. The nomogram tool has been well applied clinically. Conclusion: The previous clinical and imaging data alone did not perform well for predicting BCR. Our combinatorial model firstly using clinical data-CEUS-MRI radiomics provided an opportunity for clinical screening of BCR and help improve its prognosis.

Risk score based on three mRNA expression predicts the survival of bladder cancer.

Bladder cancer (BLCA) is one of the most malignant cancers worldwide, and its prognosis varies. 1214 BLCA samples in five different datasets and 2 platforms were enrolled in this study. By utilizing the gene expression in The Cancer Genome Atlas (TCGA) dataset, and another two datasets, in GSE13507 and GSE31684, we constructed a risk score staging system with Cox multivariate regression to evaluate predict the outcome of BLCA patients. Three genes consist of RCOR1, ST3GAL5, and COL10A1 were used to predict the survival of BLCA patients. The patients with low risk score have a better survival rate than those with high risk score, significantly. The survival profiles of another two datasets (GSE13507 and GSE31684), which were used for candidate gene selection, were similar as the training dataset (TCGA). Furthermore, survival prediction effect of risk score staging system in another 2 independent datasets, GSE40875 and E-TABM-4321, were also validated. Compared with other clinical observations, and the risk score performs better in evaluating the survival of BLCA patients. Moreover, the correlation between radiation were also evaluated, and we found that patients have a poor survival in high risk group, regardless of radiation. Gene Set Enrichment Analysis was also implemented to find the difference between high-risk and low-risk groups on biological pathways, and focal adhesion and JAK signaling pathway were significantly enriched. In summary, we developed a risk staging model for BLCA patients with three gene expression. The model is independent from and performs better than other clinical information.