Comprehensive analysis of PHF5A as a potential prognostic biomarker and therapeutic target across cancers and in hepatocellular carcinoma.

OBJECTIVE: Cancer is a predominant cause of death globally. PHD-finger domain protein 5 A (PHF5A) has been reported to participate in various cancers; however, there has been no pan-cancer analysis of PHF5A. This study aims to present a novel prognostic biomarker and therapeutic target for cancer treatment. METHODS: This study explored PHF5A expression and its impact on prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), functional status and tumor immunity across cancers using various public databases, and validated PHF5A expression and its correlation with survival, immune evasion, angiogenesis, and treatment response in hepatocellular carcinoma (HCC) using bioinformatics tools, qRT-PCR and immunohistochemistry (IHC). RESULTS: PHF5A was differentially expressed between tumor and corresponding normal tissues and was correlated with prognosis in diverse cancers. Its expression was also associated with TMB, MSI, functional status, tumor microenvironment, immune infiltration, immune checkpoint genes and tumor immune dysfunction and exclusion (TIDE) score in diverse malignancies. In HCC, PHF5A was confirmed to be upregulated by qRT-PCR and IHC, and elevated PHF5A expression may promote immune evasion and angiogenesis in HCC. Additionally, multiple canonical pathways were revealed to be involved in the biological activity of PHF5A in HCC. Moreover, immunotherapy and transcatheter arterial chemoembolization (TACE) worked better in the low PHF5A expression group, while sorafenib, chemotherapy and AKT inhibitor were more effective in the high expression group. CONCLUSIONS: This study provides a comprehensive understanding of the biological function of PHF5A in the carcinogenesis and progression of various cancers. PHF5A could serve as a tumor biomarker related to prognosis across cancers, especially HCC, and shed new light on the development of novel therapeutic targets.

Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.

BACKGROUND: Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. PURPOSE: To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC. STUDY TYPE: Retrospective. POPULATION: A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67). FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo. ASSESSMENT: Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence. STATISTICAL TESTS: The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance. RESULTS: Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found. DATA CONCLUSIONS: The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

Construction of a prediction model for postoperative prognosis in patients with resectable cholangiocarcinoma based on silence information regulator 2 expression. / æ²‰é»˜ä¿¡æ¯è°ƒèŠ‚å› å­2åœ¨èƒ†ç®¡ç™Œä¸­çš„è¡¨è¾¾åŠå ¶å¯¹å¯åˆ‡é™¤èƒ†ç®¡ç™Œæ‚£è€ é¢„åŽçš„é¢„æµ‹ä»·å€¼.

OBJECTIVES: To develop a prediction model for postoperative prognosis in patients with cholangiocarcinoma (CCA) based on the expression of silence information regulator 2 (SIRT2). METHODS: The differential expression of SIRT2 between CCA and normal tissues was analyzed using TCGA and GEO databases. Gene set enrichment analysis (GSEA) was used to explore potential mechanisms of SIRT2 in CCA. The expression of SIRT2 protein in CCA tissues and normal tissues (including 44 pairs of specimens) was also detected by immunohistochemistry (IHC) in 89 resectable CCA patients who underwent surgical treatment in the First Affiliated Hospital of Bengbu Medical College between January 2016 and December 2021. The relationship between SIRT2 expression and clinicopathological characteristics and prognosis of CCA patients was analyzed. A survival prediction model for patients with resectable CCA was constructed with COX regression results, the calibration curve and the time-dependent receiver operating characteristic curve (ROC) were used to evaluate the performance of the constructed model, and the predictive power between this model and the American Joint Committee on Cancer (AJCC)/TNM staging system (8th edition) was compared. RESULTS: SIRT2 mRNA was overexpressed in CCA tissues as shown in TCGA and GEO databases. IHC staining showed that SIRT2 protein expression in CCA tissues was significantly higher than that in adjacent non-tumor tissues. GSEA results showed that elevated SIRT2 expression may be involved in multiple metabolism-related signaling pathway, such as fatty acid metabolism, oxidative phosphorylation and amino acid metabolism. SIRT2 expression was related to serum triglycerides level, tumor size and lymph node metastasis (all P<0.05). The survival analysis results showed that patients with higher SIRT2 expression had a significantly lower overall survival (OS) than patients with lower SIRT2 expression (P<0.05). Univariate COX regression analysis suggested that pathological differentiation, clinical stage, postoperative treatment and SIRT2 expression level were associated with the prognosis of CCA patients (all P<0.05). Multivariate regression analysis confirmed that clinical stage and SIRT2 expression level were independent predictors of OS in postoperative CCA patients (both P<0.05). A nomogram based on SIRT2 for prediction of survival in postoperative CCA patients was constructed. The C-index of the model was 0.675, and the area under the time-dependent ROC curve (AUC) for predicting survival in the first, second, and third years was 0.879, 0.778, and 0.953, respectively, which were superior to those of AJCC/TNM staging system (8th Edition). CONCLUSIONS: SIRT2 is highly expressed in CCA tissues, which is associated with poor prognosis in patients with resectable CCA. The nomogram developed based on SIRT2 may have better predictive power than the AJCC/TNM staging system (8th edition) in prediction of survival of postoperative CCA patients.

Demonstrating the effectiveness of intra-articular prolotherapy combined with peri-articular perineural injection in knee osteoarthritis: a randomized controlled trial.

BACKGROUND: This study aimed to compare the efficacy of intra-articular prolotherapy (IG) combined with peri-articular perineural injection (PG) in the management of knee osteoarthritis (KOA). METHODS: A total of 60 patients with the diagnosis of KOA were included in this double-blinded randomized controlled clinical trials. The inclusion criteria were as follow: (1) 48-80 years old; (2) the diagnose of KOA; (3) the grade 2 and 3 of the Kellgern-Lawrence grading scale; (4) the pain, crepitation, and knee joint stiffness continuing for 3 months at least. The main exclusion criteria were as follow: (1) any infection involving the knee skin; (2) history of any Influencing factors of disease. All patients were divided into three groups and received either IG, PG and I + PG under the ultrasound guidance and the 2, 4 and 8 weeks follow-up data of patients were available. (IG n = 20 or PG n = 20, I + PG n = 20). Visual Analogue Scale (VAS), The Western Ontario McMaster University Osteoarthritis Index (WOMAC) and the pressure pain threshold (PPT) were used as outcome measures at baseline, 2, 4 and 8 weeks. RESULTS: There were no statistically significant differences in terms of age, sex, BMI, duration of current condition and baseline assessments of pain intensity, WOMAC scores and PPT. After treatment, the improvement of VAS activity, WOMAC and PPT values was showed compared with pre-treatment in all groups (p < 0.05). At 4 and 8 weeks after treatment, the VAS and WOMAC scores of the I + PG were significantly lower than those of the PG or IG, and the difference was statistically significant (p < 0.05). The PPT values of PG and I + PG were significantly improved compared to IG at 2, 4, and 8 weeks after treatment. CONCLUSION: The ultrasound guided I + PG of 5% glucose seem to be more effective to alleviate pain and improve knee joint function than single therapy in short term. Clinical rehabilitators could clinically try this combination of I + PG to improve clinical symptoms in patients with KOA.

Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma.

Background: Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management. Methods: HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically. Results: A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors. Conclusions: The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images.

Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional neural networks (CNNs) have been successful in medical image segmentation, they face challenges in capturing long-term dependencies among pixels. On the other hand, Transformer-based models demand a high number of parameters and involve significant computational costs. To address these issues, we propose the Spatial and Spectral-learning Double-branched Aggregation Network (S2DA-Net) for liver tumor segmentation. S2DA-Net consists of a double-branched encoder and a decoder with a Group Multi-Head Cross-Attention Aggregation (GMCA) module, Two branches in the encoder consist of a Fourier Spectral-learning Multi-scale Fusion (FSMF) branch and a Multi-axis Aggregation Hadamard Attention (MAHA) branch. The FSMF branch employs a Fourier-based network to learn amplitude and phase information, capturing richer features and detailed information without introducing an excessive number of parameters. The FSMF branch utilizes a Fourier-based network to capture amplitude and phase information, enriching features without introducing excessive parameters. The MAHA branch incorporates spatial information, enhancing discriminative features while minimizing computational costs. In the decoding path, a GMCA module extracts local information and establishes long-term dependencies, improving localization capabilities by amalgamating features from diverse branches. Experimental results on the public LiTS2017 liver tumor datasets show that the proposed segmentation model achieves significant improvements compared to the state-of-the-art methods, obtaining dice per case (DPC) 69.4 % and global dice (DG) 80.0 % for liver tumor segmentation on the LiTS2017 dataset. Meanwhile, the pre-trained model based on the LiTS2017 datasets obtain, DPC 73.4 % and an DG 82.2 % on the 3DIRCADb dataset.

Using teach-back in patient education to improve patient satisfaction and the clarity of magnetic resonance imaging.

OBJECTIVE: To explore the effects of using the teach-back method prior to contrast-enhanced magnetic resonance imaging (MRI) on patients' knowledge and satisfaction as well as the clarity of the resulting scans. METHODS: A total of 254 patients who underwent contrast-enhanced MRI examination from July 4, 2022 to September 19, 2022 were enrolled and assigned to the intervention and control groups. Patients in the intervention group received education using the teach-back method, while those in the control group were given routine health education. A questionnaire that included patients' knowledge of contrast-enhanced MRI examination was answered before and after patient education. Data on patient satisfaction with nursing services were also collected. The clarity of the MRI images of all patients was assessed. RESULTS: The scores of knowledge related to MRI after receiving education were significantly higher than those before receiving education (P < 0.001), and there were no significant differences between the intervention and control groups (11.27 ± 9.74 vs. 12.07 ± 8.71, P = 0.498). The score of satisfaction with nursing service in the teach-back group was significantly higher than that in the control group (39.82 ± 0.86 vs. 38.59 ± 3.73, P < 0.001), as was the image clarity score (96.4 ± 0.5 vs. 95.0 ± 0.4, P = 0.039). CONCLUSION: Teach-back improves patient satisfaction and contrast-enhanced MRI clarity. PRACTICE IMPLICATIONS: Including teach-back in patient education improves patient satisfaction and contrast-enhanced MRI clarity.

A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma.

PURPOSE: This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC). METHODS: 320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis. RESULTS: Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05). CONCLUSIONS: A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.

Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model. RESULTS: In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients. CONCLUSIONS: The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.