IntroductionThe primary objective was to investigate the value of the fluid attenuated inversion recovery (FLAIR) signal intensity ratio (SIR) in identifying stroke within 4.5 h. The secondary objective was to ascertain whether large vessel occlusion (LVO) mediated the relationship between the SIR and stroke within 4.5 h.MethodsWe analyzed 633 acute stroke patients within 24 h of clear symptom onset. The SIR and DWI-FLAIR mismatch were evaluated. First, we determined whether demographic variables, vascular risk factors and LVO were related to stroke within 4.5 h with multivariate logistic regression analyses and stratified regression analysis. Next, we used mediation analysis to determine whether LVO explained the association between SIR and stroke within 4.5 h. Finally, we used receiver operating characteristic (ROC) analysis to assess the value of SIR, independent variable, and multiparameter models in identifying stroke within 4.5 h and compared with DWI-FLAIR mismatch.ResultsHyperlipemia, LVO and SIR were associated with stroke within 4.5 h. Mediation analysis revealed that LVO partially mediated the relationship between SIR and stroke within 4.5 h (p < 0.001). The multiparameter model (hyperlipemia, LVO and SIR) showed significantly improved performance (AUC 0.869) in identifying stroke within 4.5 h over DWI-FLAIR mismatch (0.684), hyperlipemia (0.632), LVO (0.667) and SIR (0.773) models.ConclusionSIR is associated with stroke within 4.5 h, and LVO partially mediates this relationship. A multiparameter model combining hyperlipemia, LVO and SIR can more accurately identify stroke within 4.5 h than individual parameter models.

Objectives We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. Methods ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. Results A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). Conclusions The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. Clinical relevance statement Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. Key Points • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

Background: Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2- weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm. Methods: Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs. Results: Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs . 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) >= 0.819] and the most stable (relative standard deviations in AUC <= 0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05). Conclusions: Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

Abstract Objective To evaluate the ability of 68Ga‐PSMA PET/CT maximum standard uptake value (SUVmax) to distinguish prostate cancer (PCa) International Society of Urological Pathology ISUP grade group (GG) 2 and GG3. Methods The PET/CT images and data of 147 patients were analyzed retrospectively, and the SUVmax of the index lesions were measured. The receiver operating characteristic curve was used to analyze the diagnostic value of PET/CT for PCa. The correlation between SUVmax and ISUP GG was analyzed. A logistic regression model was established with SUVmax and was validated to predict its value of diagnosing intermediate‐ and high‐risk PCa (ihPCa). Results Of the 147 patients, 112 cases were PCa (76.2%), and 35 cases were benign lesions (23.8%). There was a significant difference between the benign and the malignant groups (p < 0.05). The median SUVmax of ihPCa was significantly higher than that of the benign and low‐risk groups (p < 0.05). The median SUVmax of GG3 was significantly higher than that of the GG2 group (p < 0.05). There were no statistically significant SUVmax differences among GG3, GG4, and GG5 groups (p > 0.05). The specificity and the positive predictive value of 68Ga‐PSMA PET/CT in the diagnosis of PCa were 97% and 99% with cut‐off SUVmax of 6.94, while the specificity and the positive predictive value of ihPCa were 95% and 96% with cut‐off SUVmax of 10.12. Conclusion 68Ga‐PSMA PET/CT can reliably distinguish GG2 from GG3 PCa.

Background: This systematic review and meta-analysis evaluated the diagnostic performance of biparametric magnetic resonance imaging (bpMRI) for the detection of intermediate- and high-risk prostate cancer (IHPC). Methods: Two medical databases (PubMed and Web of Science) were systematically reviewed by 2 independent researchers. Studies published before March 15, 2022, that used bpMRI (i.e., T2-weighted images combined with diffusion-weighted imaging) to detect prostate cancer (PCa) were included. The results of prostatectomy or prostate biopsy were the reference standards for the studies. The Quality Assessment of Diagnosis Accuracy Studies 2 tool was used to assess the quality of the included studies. Data on true- and false-positive and -negative results were extracted to complete 2x2 contingency tables, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each study. Summary receiver operating characteristic (SROC) plots were constructed using these results. Results: In all, 16 studies (6,174 patients) that used Prostate Imaging Reporting and Data System version 2 or other scoring systems, such as Likert, SPL and Questionnaire were included. Sensitivity, specificity, positive and negative likelihood ratios, and the diagnosis odds ratio of bpMRI in the detection of IHPC were 0.91 (95% CI: 0.87-0.93), 0.67 (95% CI: 0.58-0.76), 2.8 (95% CI: 2.2-3.6), 0.14 (95% CI: 0.11-0.18), and 20 (95% CI: 15-27), respectively, with an area under the SROC curve of 0.90 (95% CI: 0.87-0.92). There was considerable heterogeneity between the studies. Conclusions: bpMRI exhibited a high negative predictive value and accuracy in the diagnosis of IHPC, and may be valuable for detecting PCa with poor prognosis. However, the bpMRI protocol needs to be standardized further to improve its wider applicability.

Mitochondria are the key regulatory organelles of many cell behaviors, and the reduction of mitochondrial membrane potential is considered to be one of the earliest events of cell apoptosis. Therefore, mitochondrial imaging and the detection and analysis of mitochondrial membrane potential are of great scientific significance for the detection and treatment of diseases. In this work, AuNCs/PLEL/JC/KLA, a mitochondrial targeted fluorescent nanoprobe, was developed using Au nanocages (AuNCs) mediated photothermal damage combined with temperature-sensitive drug release. At the same time, the temperature-sensitive hydrogel poly(d,l-lactide)-poly(ethylene glycol)-poly(d,l-lactide) (PDLLA-PEG-PDLLA, PLEL) is used as the outer structure to control the release. A mitochondrial targeting peptide (KLAKLAKKLAKLAK, KLA) was introduced as the "pointer" of the nanoprobe to specifically target the mitochondria. The colocalization experiment showed that the nanoprobe was highly colocalized with mitochondria, indicating that the nanoprobe was selectively enriched in mitochondria. It is worth noting that the nanoprobe has excellent photothermal properties, and its photothermal conversion efficiency can be as high as 39.11%. Therefore, under the irradiation of near infrared light, the probe can absorb light energy into heat. Subsequently, the results of Cell Counting Kit 8 (CCK-8) confirmed that the nanoprobes could achieve local photothermal damage at mitochondrial sites, triggering high temperature mediated mitochondrial dysfunction and inducing apoptosis of cancer cells. Meanwhile, rheological analysis and fluorescence curve showed that high temperature promotes the gel-sol transformation of PLEL thermosensitive hydrogel and realizes the release of fluorescent dye (JC-10). The confocal images of the cells showed that the released JC-10 fluorescent dye can display red and green fluorescence signals based on mitochondrial activity. In conclusion, the fluorescence nanoprobe can not only achieve mitochondrial targeted fluorescence imaging and damage cells, but also monitor the changes of mitochondrial membrane potential.

Currently, there are no effective approaches for differentiating ovarian fibrothecoma (OF) from broad ligament myoma (BLM). This retrospective study aimed to construct a nomogram prediction model based on MRI to differentiate OF from BLM. The quantitative and qualitative MRI features of 41 OFs and 51 BLMs were compared. Three models were established based on the combination of these features. The ability of the models to differentiate between the two cancers was assessed by ROC analysis. A nomogram based on the best model was constructed for clinical application. The three models showed good performance in differentiating between OF and BLM. The areas under the curve (AUC) of the models based on quantitative and qualitative variables were 0.88 (95% CI: 0.79–0.96) and 0.85 (95% CI: 0.76–0.93), respectively. The combined model designed from the significant variables exhibited the best diagnostic performance with the highest AUC of 0.92 (95% CI: 0.86–0.98). Calibration of the nomogram showed that the predicted probability matched the actual probability well. Analysis of the decision curve demonstrated that the nomogram was clinically useful. Relative T1 value, stone paving sign, enhancement patterns, and ascites were identified as valuable predictors for identifying OF or BLM. The MRI-based nomogram can serve as a preoperative tool to differentiate OF from BLM.

Phosphatidylinositol-4-phosphate 5-kinase-like 1(PIP5KL1) is the basis of localization and activation of PIP5Ks and regulates cell activity. Nevertheless, the effect of PIP5KL1 on the growth of colorectal cancer remains unclear. First, we analyzed PIP5KL1 expression and gene regulatory networks in colorectal cancer using sequencing information from the Cancer Genome Atlas Database. The potential biological function of PIP5KL1 in colorectal cancer was evaluated by WGCNA analysis, co-expression analysis, immunocell infiltration analysis, drug susceptibility analysis, GSVA and GSEA analysis, and Nomogram model construction. Secondly, the PIP5KL1 mRNA and protein levels in human colorectal cancer cell lines (HCT116 and SW480) were detected by immunofluorescence imaging discussion and reverse transcription polymerase chain reaction (RT-PCR). The high expression of PIP5KL1 is made in colorectal cancer, and survival analysis suggests that those with high PIP5KL1 expression have a worse prognosis. Both univariate and multivariate Cox regression models suggested that PIP5KL1 was a separate prognostic element for colorectal cancer. Co-expression network analysis screened out genes significantly related to PIP5KL1 expression (CD164L2, LHB, AC008687.4, KLC3, DNAAF3). CIBERSORT analysis of immune cell infiltration in colorectal cancer. PIP5KL1 affects tumor progression through a variety of signaling pathways. According to GSVA results, Epithelial-mesenchymal transition (EMT), angiogenesis and hypoxia pathways are abundant in patients with high PIP5KL1 expression. WGCNA analysis of gene modules suggested the enrichment of genes in type I interferon signaling pathway, cellular reaction to type I interferon and reaction to type I interferon, Epstein−Barr virus infection, Antigen processing and presentation, Influenza A and other channels. To sum up, the overexpression of PIP5KL1 was found in colorectal cancer tissues, and according to survival analysis, the diversity was statistically great and affected the progression of colorectal cancer through tumor-related signaling pathways. Our results show that data mining can effectively reveal the expression and hidden regulatory network information of PIP5KL1 in colorectal cancer, laying a basis for further study of the effect of PIP5KL1 on the growth and progression of colorectal cancer.

BackgroundMagnetic resonance imaging (MRI) and immunohistochemical (IHC) examination provides useful information for the risk stratification of endometrial cancer (EC). However, the use of the combination of MRI and IHC for the prediction of high-risk EC is controversial. The aim of this study was to evaluate the value of preoperative MRI and IHC examination in prediction of patients with high-risk EC.MethodsThis retrospective case-control study was conducted from January 1, 2018 to May 1, 2021 at two hospitals. A primary cohort (n=102) comprised patients with histologically confirmed EC in one hospital between January 1, 2018 and May 31, 2020. An additional external cohort (n=35) comprising patients with histologically confirmed EC in a different hospital from January 1, 2020 to May 1, 2021 was included for validation. Imaging features including tumor size, tumor margin, relative T2 value, tumor signal intensity on diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) were determined from preoperative MRI images. IHC markers including ER, PR, p53 and Ki67 were determined through IHC staining of preoperative curettage specimen. Patients were divided into high-risk and low-intermediate- risk group based on the final histological results. Differences between categorical and numerical variables were assessed using chi-square test and independent-sample t-test, respectively. Multivariate binary logistic regression analyses were used for construction of the prediction model A fusion prediction model was constructed by combining MRI features and IHC markers. The predictive performance of the model was then validated using the external cohort.ResultsImaging and IHC markers were significantly associated with risk ranks. Model 1 based on MRI features showed an area under the curve (AUC) of 0.822 [95% confidence interval (CI), 0.741-0.903] whereas Model 2 based on IHC markers showed an AUC of 0.894 (95% CI, 0.829-0.960). Notably, model 3 integrating independent MRI and IHC risk factors demonstrated good calibration and high differentiation ability with an AUC of 0.958 (95% CI, 0.923-0.993), and showed good discrimination with an AUC of 0.84 (95% CI, 0.677-0.942) using the external validation set.ConclusionsThis study proposes a comprehensive predictive model comprising MRI and IHC features as a powerful tool for preoperative risk stratification to assist in clinical decision-making for EC patients.

Rationale: Giant multilocular prostatic cystadenoma (GMPC) is a rare type of prostatic epithelial neoplasm. Thus, the imaging features of this condition are not well known. We report the imaging and clinical manifestations of a case of GMPC. Patient concerns: The case reported here relates to a 71-year-old man who complained of urination frequency and excessive urination at night. He underwent computed tomography (CT) and magnetic resonance imaging (MRI) examination before surgery, both tests revealed a mass body in the prostate. Diagnosis: Ultrasound-guided fine needle aspiration was performed and a diagnosis of GMPC was made by histological examination. Interventions: The patient received radical pelvic tumor resection successfully. Outcomes: Two months after surgery, the follow-up CT and magnetic MRI re-examination found no signs of recurrence. Lessons: GMPC is a rare prostatic neoplasm with atypical clinical symptoms. MRI provides valuable information about GMPC. In case of a giant multilocular prostatic mass with well-defined boundary and abundant vascularity, benign feature on diffusion-weighted imaging, GMPC should be considered.

Mutation or downregulation of p53 (encoded by TP53) accelerates tumorigenesis and malignant progression in esophageal squamous cell carcinoma (ESCC). However, it is still unknown whether circular RNAs (circRNAs), a novel class of endogenous noncoding RNAs, participate in the regulation of this progress. In this study, we explored the expression profiles of circRNAs in three paired samples of ESCC and identified cCNTNAP3, which is a circRNA that originates from the CNTNAP3 gene transcript and is highly expressed in normal human esophageal tissue. However, we found that the cCNTNAP3 expression level was significantly downregulated in ESCC tissues. In vitro and in vivo studies revealed that cCNTNAP3 inhibited proliferation and increased apoptosis in p53 wild-type ESCC cells, but not in mutant cells. Mechanistically, we found that cCNTNAP3 promotes the expression of p53 by sponging miR-513a-5p. Rescue assay confirmed that the suppressive function of cCNTNAP3 was dependent on miR-513a-5p. We also observed that p53/RBM25 participated in the formation of cCNTNAP3, which implied the existence of a positive feedback loop between cCNTNAP3 and p53. Furthermore, the downregulation of cCNTNAP3 was significantly correlated with later T stage and thus can serve as an independent risk factor for the overall survival of patients with p53 wild-type ESCC. In conclusion, the cCNTNAP3-TP53 positive feedback loop may provide a potential target for the management of ESCC, which also reveals the important role of circRNAs in the regulation of p53.

Objective To investigate the effect and its molecular mechanism of celecoxib on anoikis resistance in non-small cell lung cancer cell line A549 induced by lung cancer surgical stress.Methods A549 cells were cultured using cell culture plate covering poly-(2-hydroxyethyl methacrylate) HEMA.The control group,prostaglandin E2 (PGE2) treatment group,and PGE2 combined celecoxib treatment group were established.Anoikis of cells was assessed by flow cytometry after treatment for 48 h.Western blotting was used to detect the activation of mitogen extracellular kinase (MEK)/extracellular signal-regulated kinase 1/2 (ERK1/2) pathway and phosphatidylinositol 3 kinase (PI3 K)/protein kinase B (Akt) pathway and the expression of apoptosis related proteins,Mcl-1 and Bim,in each group.BALB/c mice were used to simulate the surgical stress,and A549 cells were injected into the tail vein to establish the animal model of lung metastasis.Mice were divided into control group,surgery group and surgery plus celecoxib group (completely randomized grouping),the mice were killed after 4 weeks and lung metastasis was observed,and the expression of Mcl-1 and Bim in pulmonary nodules was detected by immunohistochemical method.Results Flow cytometry results showed that apoptosis rate of A549 cells cultured in poly-HEMA was (41.0 ± 2.4) ％,that in PGE2 treated group was (22.8 ± 3.2) ％ (as compared with the control group,P =0.012),and that in the PGE2 combined with celecoxib treatment group was (36.6 ± 2.4) ％ (as compared with the PGE2 group,P =0.034).The results of Western blotting showed that PGE2 could activate the MEK/ERK1/2 pathway,up-regulate the phosphorylation level of ERK1/2,and down-regulate the expression of Bim,while celecoxib could significantly inhibit the effects of PGE2.In addition,PGE2 had no obvious effect on PI3K/Akt pathway,but celecoxib could inhibit the pathway,and down-regulate the phosphorylation level of Akt and inhibit the expression of Mcl-1.Immunohistochemically,the expression level of Mcl-1 and Bim protein in the pulmonary nodules was consistent with that of experiments in vitro.Conclusion PGE2 is the main cytokine production in the surgical stress.This study shows that PGE2 has obvious effect in vitro on anoikis resistance,and celecoxib inhibits the MEK/ERK1/2 pathway and the PI3K/Akt pathway to increase the expression of pro-apoptotic protein Bim and decrease the expression of anti-apoptotic protein Mcl-1 in vitro and in vivo,which can promote anoikis to reduce lung cancer cells metastasis.