Deep causal learning for pancreatic cancer segmentation in CT sequences.

Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current deep-learning (DL) methods usually segment the pancreas or tumor independently using mixed image features, which are disrupted by surrounding complex and low-contrast background tissues. Here, we proposed a deep causal learning framework named CausegNet for pancreas and tumor co-segmentation in 3D CT sequences. Specifically, a causality-aware module and a counterfactual loss are employed to enhance the DL network's comprehension of the anatomical causal relationship between the foreground elements (pancreas and tumor) and the background. By integrating causality into CausegNet, the network focuses solely on extracting intrinsic foreground causal features while effectively learning the potential causality between the pancreas and the tumor. Then based on the extracted causal features, CausegNet applies a counterfactual inference to significantly reduce the background interference and sequentially search for pancreas and tumor from the foreground. Consequently, our approach can handle deformable pancreas and obscure tumors, resulting in superior co-segmentation performance in both public and real clinical datasets, achieving the highest pancreas/tumor Dice coefficients of 86.67%/84.28%. The visualized features and anti-noise experiments further demonstrate the causal interpretability and stability of our method. Furthermore, our approach improves the accuracy and sensitivity of downstream pancreatic cancer risk assessment task by 12.50% and 50.00%, respectively, compared to experienced clinicians, indicating promising clinical applications.

Association between cardiovascular disease and risk of female sexual dysfunction: a systematic review and meta-analysis.

AIMS: Female sexual dysfunction (FSD) is a considerably underestimated condition. It has been repeatedly reported that patients with cardiovascular diseases (CVD) may suffer from an increased risk of FSD. However, there is still a lack of comprehensive and systematic evaluation of various CVD and FSD. We aimed to elucidate the association between CVD and FSD through a comprehensive literature review and meta-analysis. METHODS AND RESULTS: The PubMed, Scopus, Embase, and Cochrane Library databases were systematically searched from inception to 28 February 2023. We identified all relevant studies reporting the risk of FSD in subjects with or without CVD. The associations between CVD and the risk of FSD were assessed by calculating pooled odds ratios (ORs) (cross-sectional studies) and risk ratios (RRs) (longitudinal studies) with 95% CIs. We employed random-effects models to account for potential heterogeneity, and the quality of the included studies was assessed using the Newcastle-Ottawa Scale. Fifty-four articles with 148 946 individuals were included in our meta-analysis. Compared with control subjects, subjects with CVD had a 1.51-fold increased risk of FSD (OR 1.51 95% CI, 1.34-1.69, P < 0.001, heterogeneity I2 = 91.4%, P < 0.001). Subgroup analyses indicated that the association between CVD and FSD remained significant in longitudinal studies (RR 1.50 95% CI, 1.21-1.86, P < 0.001, heterogeneity I2 = 86.7%, P < 0.001). Particularly, hypertension (OR 1.41 95% CI, 1.23-1.62, P < 0.001, heterogeneity I2 = 82.7%, P < 0.001), stroke (OR 1.81 95% CI, 1.54-2.12, P < 0.001, heterogeneity I2 = 0%, P < 0.423), and myocardial infarction (OR 2.07 95% CI, 1.60-2.67, P < 0.001 heterogeneity I2 = 82.4%, P < 0.001) were significantly associated with FSD. Meta-regression revealed that the primary sources of heterogeneity in FSD are attributable to adjustments for covariates, study design, and study population. CONCLUSION: Our meta-analysis indicated that patients with CVD suffer from a greater risk of developing FSD. Meanwhile, we validated these findings in longitudinal queues. Notably, conditions such as hypertension, stroke, and myocardial infarction demonstrated a significant association with the incidence of FSD.

Our study provides a significant advantage as the most comprehensive systematic analysis to date. It encompassed 45 cross-sectional and 11 longitudinal studies with 148 946 patients, aiming to investigate the relationship between various types of cardiovascular diseases (CVD) and female sexual dysfunction (FSD). Subgroup analyses were conducted to explore the impact of factors such as region and publication time.Accumulating evidence strongly supports a significant link between CVD and an increased risk of FSD, especially in cases of hypertension, stroke, and myocardial infarction. These findings indicate that more attention should be paid to women's sexual health, particularly in the presence of CVD.Future studies are warranted to investigate the effects of pharmacological interventions on the sexual function of women affected by CVD.

A metric learning-based method using graph neural network for pancreatic cystic neoplasm classification from CTs.

PURPOSE: Pancreatic cystic neoplasms (PCNs) are relatively rare neoplasms and difficult to be classified preoperatively. Ordinary deep learning methods have great potential to provide support for doctors in PCNs classification but require a quantity of labeled samples and exact segmentation of neoplasm. The proposed metric learning-based method using graph neural network (GNN) aims to overcome the limitations brought by small and imbalanced dataset and get fast and accurate PCNs classification result from computed tomography (CT) images. METHODS: The proposed framework applies GNN. GNNs perform well in fusing information and modeling relational data and get better results on dataset with small size. Based on metric learning strategy, model learns distance from the data. The similarity-based algorithm enhances the classification performance, and more characteristic information is found. We use a convolutional neural network (CNN) to extract features from given images. Then GNN is used to find the similarity between each two feature vectors and complete the classification. Several subtasks consisting of randomly selected images are established to improve generalization of the model. The experiments are carried out on the dataset provided by Huashan Hospital. The dataset is labeled by postoperative pathological analysis and contains region of interest (ROI) information calibrated by experts. We set two tasks based on the dataset: benign or malignant diagnosis of PCNs and classification of specific types. RESULTS: Our model shows good performance on the two tasks with accuracies of 88.926% and 74.497%. The comparison of different methods' F1 scores in the benign or malignant diagnosis shows that the proposed GNN-based method effectively reduces the negative impact brought by imbalanced dataset, which is also verified by the macroaverage comparison in the four-class classification task. CONCLUSIONS: Compared with existing models, the proposed GNN-based model shows better performance in terms of imbalanced dataset with small size while reducing labeling cost. The result provides a possibility for its application into the computer-aided diagnosis of PCNs.

Multi-Dimensional Cascaded Net with Uncertain Probability Reduction for Abdominal Multi-Organ Segmentation in CT Sequences.

BACKGROUND AND OBJECTIVE: Deep learning abdominal multi-organ segmentation provides preoperative guidance for abdominal surgery. However, due to the large volume of 3D CT sequences, the existing methods cannot balance complete semantic features and high-resolution detail information, which leads to uncertain, rough, and inaccurate segmentation, especially in small and irregular organs. In this paper, we propose a two-stage algorithm named multi-dimensional cascaded net (MDCNet) to solve the above problems and segment multi-organs in CT images, including the spleen, kidney, gallbladder, esophagus, liver, stomach, pancreas, and duodenum. METHODS: MDCNet combines the powerful semantic encoder ability of a 3D net and the rich high-resolution information of a 2.5D net. In stage1, a prior-guided shallow-layer-enhanced 3D location net extracts entire semantic features from a downsampled CT volume to perform rough segmentation. Additionally, we use circular inference and parameter Dice loss to alleviate uncertain boundary. The inputs of stage2 are high-resolution slices, which are obtained by the original image and coarse segmentation of stage1. Stage2 offsets the details lost during downsampling, resulting in smooth and accurate refined contours. The 2.5D net from the axial, coronal, and sagittal views also compensates for the missing spatial information of a single view. RESULTS: The experiments on the two datasets both obtained the best performance, particularly a higher Dice on small gallbladders and irregular duodenums, which reached 0.85±0.12 and 0.77±0.07 respectively, increasing by 0.02 and 0.03 compared to the state-of-the-art method. CONCLUSION: Our method can extract all semantic and high-resolution detail information from a large-volume CT image. It reduces the boundary uncertainty while yielding smoother segmentation edges, indicating good clinical application prospects.

Exosome-delivered CD44v6/C1QBP complex drives pancreatic cancer liver metastasis by promoting fibrotic liver microenvironment.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) shows a remarkable predilection for liver metastasis. Pro-oncogenic secretome delivery and trafficking via exosomes are crucial for pre-metastatic microenvironment formation and metastasis. This study aimed to explore the underlying mechanisms of how PDAC-derived exosomes (Pex) modulate the liver microenvironment and promote metastasis. DESIGN: C57BL/6 mice were 'educated' by tail vein Pex injection. The intrasplenic injection liver metastasis and PDAC orthotopic transplantation models were used to evaluate liver metastasis. Stable cell lines CD44v6 (CD44 variant isoform 6) or C1QBP (complement C1q binding protein) knockdown or overexpression was established using lentivirus transfection or gateway systems. A total of 142 patients with PDAC in Huashan Hospital were retrospectively enrolled. Prognosis and liver metastasis were predicted using Kaplan-Meier survival curves and logistic regression models. RESULTS: Pex tail vein injection induced the deposition of liver fibrotic extracellular matrix, which promoted PDAC liver metastasis. Specifically, the exosomal CD44v6/C1QBP complex was delivered to the plasma membrane of hepatic satellite cells (HSCs), leading to phosphorylation of insulin-like growth factor 1 signalling molecules, which resulted in HSC activation and liver fibrosis. Expression of Pex CD44v6 and C1QBP in PDAC patients with liver metastasis was significantly higher than in PDAC patients without liver metastasis, and simultaneous high expression of exosomal CD44v6 and C1QBP correlated with a worse prognosis and a higher risk of postoperative PDAC liver metastasis. CONCLUSION: The Pex-derived CD44v6/C1QBP complex is essential for the formation of a fibrotic liver microenvironment and PDAC liver metastasis. Highly expressed exosomal CD44v6 and C1QBP are promising biomarkers for predicting prognosis and liver metastasis in patients with PDAC.

Evaluating perioperative glycemic status after different types of pancreatic surgeries via continuous glucose monitoring system: a pilot study.

BACKGROUND: Perioperative glycemic status after pancreatic surgery has never been described. However, it's essential for optimal perioperative glucose management and understanding the pathogenesis of new-onset diabetes mellitus (NODM) after pancreatectomy. Continuous glucose monitoring (CGM) system provides us a helpful tool for closely monitoring and studying perioperative glucose change. This study tried to describe and compare perioperative glucose level and glycemic variability between different types of pancreatic surgeries via CGM device. METHODS: This study was designed as a prospective observational study. Eighteen patients were enrolled and were grouped by different types of surgery received: control group (CTRL), pancreaticoduodenectomy (PD), distal pancreatectomy (DP), and total pancreatectomy (TP). CGM devices were implanted and initiated right after the surgery. Mean glucose value (MGV), coefficient of variation (CV), mean of daily difference (MODD), continuous overall net glycemic action (CONGA), and time above range (TAR)/time below range (TBR) was compared between groups to assess glucose level and glycemic variability. RESULTS: TP showed the highest MGV and CV among all groups (P<0.001), while CTRL showed the lowest (P<0.001). PD and DP had similar MGV and CV lower than TP but higher than CTRL (P<0.001). TP had the highest MODD and CONGA, CTRL had the lowest, but no significant differences were found between groups. TP had the highest TAR (24.29%) and the lowest TBR (1.28%), while the control group showed the opposite. The differences in TAR/TBR between groups were all significant (P<0.05). CONCLUSIONS: TP had the highest mean glucose level and the greatest glycemic variability. PD and DP had similar results: a higher mean glucose level than control but lower than TP. For glycemic variability, PD and DP seemed to have a near-normal result resembling the control group. CGM is useful for glucose monitoring in the perioperative management of pancreatic surgery.

Computer-aided differentiates benign from malignant IPMN and MCN with a novel feature selection algorithm.

In clinical practice, differentiating benign from malignant intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) preoperatively is crucial for deciding future treating algorithm. However, it remains challenging as benign and malignant lesions usually show similarities in both imaging appearances and clinical indices. Therefore, a robust and accurate computer-aided diagnosis (CAD) system based on radiomics and clinical indices was proposed in this paper to solve this dilemma. In the proposed CAD system, 107 patients were enrolled, where 90 cases were randomly selected for the training set with 5-fold cross validation to build the diagnostic model, while 17 cases were remained for an independent testing set to validate the performance. 436 high-throughput radiomics features while 9 clinical indices were designed and extracted. A novel feature selection algorithm named BLR (Bootstrapping repeated LASSO with Random selections) was proposed to select the most effective features. Then the selected features were sent to Support Vector Machine (SVM) to differentiate the benign or malignant. In the cross-validation cohort and independent testing cohort, the area under receiver operating characteristic curve (AUC) of CAD scheme were 0.83 and 0.92, respectively. The results fully prove the proposed CAD system achieves significant effect in tumors diagnosis.

WD repeat-containing protein 1 maintains ß-Catenin activity to promote pancreatic cancer aggressiveness.

BACKGROUND: The molecular signature underlying pancreatic ductal adenocarcinoma (PDAC) progression may include key proteins affecting the malignant phenotypes. Here, we aimed to identify the proteins implicated in PDAC with different tumour-node-metastasis (TNM) stages. METHODS: Eight-plex isobaric tags coupled with two-dimensional liquid chromatography-tandem mass spectrometry were used to analyse the proteome of PDAC tissues with different TNM stages. A loss-of-function study was performed to evaluate the oncogenic roles of WD repeat-containing protein 1 (WDR1) in PDAC. The molecular mechanism by which WDR1 promotes PDAC progression was studied by real-time qPCR, Western blotting, proximity ligation assay and co-immunoprecipitation. RESULTS: A total of 5036 proteins were identified, and 4708 proteins were quantified with high confidence. Compared with normal pancreatic tissues, 37 proteins were changed significantly in PDAC tissues of different stages. Moreover, 64 proteins were upregulated or downregulated in a stepwise manner as the TNM stages of PDAC increased, and 10 proteins were related to tumorigenesis. The functionally uncharacterised protein, WDR1, was highly expressed in PDAC and predicted a poor prognosis. WDR1 knockdown suppressed PDAC tumour growth and metastasis in vitro and in vivo. Moreover, WDR1 knockdown repressed the activity of the Wnt/ß-Catenin pathway; ectopic expression of a stabilised form of ß-Catenin restored the suppressive effects of WDR1 knockdown. Mechanistically, WDR1 interacted with USP7 to prevent ubiquitination-mediated degradation of ß-Catenin. CONCLUSION: Our study identifies several previous functional unknown proteins implicated in the progression of PDAC, and provides new insight into the oncogenic roles of WDR1 in PDAC development.

Controlling Nutritional Status score is superior to Prognostic Nutritional Index score in predicting survival and complications in pancreatic ductal adenocarcinoma: a Chinese propensity score matching study.

Preoperative nutritional status plays an important role in predicting postoperative outcomes. Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) are good tools to assess patients' nutritional status. They have been used in predicting outcomes in various malignancies, but few studies have focused on pancreatic adenocarcinoma (PDAC) patients. Totally, 306 PDAC patients were enrolled. The propensity score matching (PSM) method was introduced to eliminate the baseline inequivalence. Patients with different PNI (or CONUT) scores showed inequivalence baseline characteristics, and patients with compromised nutritional status were related with a more advanced tumour stage. After PSM, the baseline characteristics were well balanced. Both low PNI (≤45) and high CONUT (≥3) were independent risk factors for poor overall survival (P < 0·05), and the result remained the same after PSM. Survival analysis demonstrated both patients with low PNI and high CONUT score were associated with poorer survival, and the result remained the same after PSM. The results of AUC indicated that CONUT might have a higher sensitivity and specificity in predicting complications and survival. Preoperative low PNI (≤45) and high CONUT (≥3) scores might be reliable predictors of prognosis and surgical complications in PDAC patients. Compared with PNI, CONUT might be more effective.

Partial pancreatic tail preserving subtotal pancreatectomy for pancreatic cancer: Improving glycemic control and quality of life without compromising oncological outcomes.

BACKGROUND: Total pancreatectomy (TP) is usually considered a therapeutic option for pancreatic cancer in which Whipple surgery and distal pancreatectomy are undesirable, but brittle diabetes and poor quality of life (QoL) remain major concerns. A subset of patients who underwent TP even died due to severe hypoglycemia. For pancreatic cancer involving the pancreatic head and proximal body but without invasion to the pancreatic tail, we performed partial pancreatic tail preserving subtotal pancreatectomy (PPTP-SP) in selected patients, in order to improve postoperative glycemic control and QoL without compromising oncological outcomes. AIM: To evaluate the efficacy of PPTP-SP for patients with pancreatic cancer. METHODS: We retrospectively reviewed 56 patients with pancreatic ductal adenocarcinoma who underwent PPTP-SP (n = 18) or TP (n = 38) at our institution from May 2014 to January 2019. Clinical outcomes were compared between the two groups, with an emphasis on oncological outcomes, postoperative glycemic control, and QoL. QoL was evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30 and EORTC PAN26). All patients were followed until May 2019 or until death. RESULTS: A total of 56 consecutive patients were enrolled in this study. Perioperative outcomes, recurrence-free survival, and overall survival were comparable between the two groups. No patients in the PPTP-SP group developed cancer recurrence in the pancreatic tail stump or splenic hilum, or a clinical pancreatic fistula. Patients who underwent PPTP-SP had significantly better glycemic control, based on their higher rate of insulin-independence (P = 0.014), lower hemoglobin A1c (HbA1c) level (P = 0.046), lower daily insulin dosage (P < 0.001), and less frequent hypoglycemic episodes (P < 0.001). Global health was similar in the two groups, but patients who underwent PPTP-SP had better functional status (P = 0.036), milder symptoms (P = 0.013), less severe diet restriction (P = 0.011), and higher confidence regarding future life (P = 0.035). CONCLUSION: For pancreatic cancer involving the pancreatic head and proximal body, PPTP-SP achieves perioperative and oncological outcomes comparable to TP in selected patients while significantly improving long-term glycemic control and QoL.

LRG-1 promotes pancreatic cancer growth and metastasis via modulation of the EGFR/p38 signaling.

BACKGROUND: The abnormal expression of leucine-rich-alpha-2-glycoprotein 1 (LRG-1) is reported to be associated with multiple malignancies, but its role in the progression of pancreatic ductal adenocarcinoma (PDAC) remains to be determined. METHODS: The expression of LRG-1 was assessed in PDAC tissues by RT-PCR, Western blot and immunohistochemistry. LRG-1-silenced or overexpressed cell lines were constructed using shRNA or LRG-1-overexpressing plasmids. EdU incorporation assay, Transwell invasion and wound-healing assays were performed to evaluate the proliferation, invasion and migration of PDAC cells. In addition, protein expression of the mitogen-activated protein kinase (MAPK) pathway was detected using Western blot. Finally, Co-immunoprecipitation assay was conducted in search of the potential interaction between LRG-1 and epidermal growth factor receptor (EGFR). RESULTS: The expression of LRG-1 in PDAC tissue was significantly higher than that in adjacent normal tissue, and high LRG-1 expression predicted poor survival and a late tumor stage. In addition, LRG-1 markedly promoted the viability, proliferation, migration and invasion of PDAC cells in vitro and facilitated tumor growth in vivo. More importantly, we revealed that these bioactivities of LRG-1 might result from its selective interaction with EGFR, which might further activate the p38/MAPK signaling pathways. CONCLUSION: LRG-1 may prove to be a promising biomarker for predicting prognosis of PDAC patients. Inhibition of LRG-1 or its downstream pathway could be a potential therapeutic target for the treatment of PDAC.

Total pancreatectomy for pancreatic malignancy with preservation of the spleen.

BACKGROUND: Total pancreatectomy may be necessary to achieve margin-negative resection for pancreatic cancer. However, despite the desirability of saving the spleen, the feasibility, safety, and oncological outcomes of spleen-preserving total pancreatectomy have not been studied in patients with malignancy involving the pancreatic neck or proximal body. The aim of this study was to report the efficacy of spleen-preserving total pancreatectomy using the Warshaw technique for patients with pancreatic malignancies. METHODS: A retrospective analysis of patients who underwent total pancreatectomy for malignant pancreatic diseases between December 2006 and January 2018 focused on comparing the clinical outcomes between conventional operations with splenectomy and spleen-preserving total pancreatectomy using the Warshaw technique. RESULTS: Thirty-eight patients among a total of 59 total pancreatectomies had the spleen preservation by the Warshaw operation. In this series, the pancreatic ductal adenocarcinomas resected with the Warshaw technique were of smaller tumor size but had greater rates of vascular invasion, resulting in the more frequent vascular resection. No patients had splenic complications requiring splenectomy, but two patients intended to have the Warshaw operation were converted to splenectomy because of splenic malperfusion. Asymptomatic perigastric varices were noted in 4 patients. Postoperative morbidity and mortality were comparable between the Warshaw and conventional operation groups. Recurrence-free and overall survival was similar in both groups. CONCLUSION: In patients with pancreatic malignancy, total pancreatectomy with preservation of the spleen using the Warshaw technique achieves outcomes comparable with conventional total pancreatectomy with splenectomy in selected patients.

A Comprehensive Proteome Analysis of Peripheral Blood Mononuclear Cells (PBMCs) to Identify Candidate Biomarkers of Pancreatic Cancer.

BACKGROUND/AIM: Pancreatic cancer (PC) is currently the fourth leading cause of cancer-related mortality worldwide. Peripheral blood mononuclear cells (PBMCs) is a subpopulation of accessible and functional immune cells. Comparative analysis of the proteome of PBMCs can help us elucidate the mechanism of disease and find potential biomarkers for diagnosis. MATERIALS AND METHODS: PBMCs were collected from healthy individuals, patients with benign diseases, and pancreatic cancer. iTRAQ-2DLC-MS/MS and SWATH methodologies were applied to make a comparative proteomics analysis of PBMCs. RESULTS: A total of 3,357 proteins with a false discovery rate (FDR) <1% were identified, of which 114 proteins were found dysregulated in the PC group. An extensive SWATH library was constructed which showed a potential application for large scale clinical sample analysis. CONCLUSION: A PBMCs proteome with extensive protein representation was achieved, which will potentially allow the identification of novel biomarkers for PC.

Dynamic quantitative detection of ABC transporter family promoter methylation by MS-HRM for predicting MDR in pancreatic cancer.

The main focus of the present study was to evaluate whether ABC transporter family promoter methylation predicted multidrug resistance in gemcitabine-resistant cancer cell lines (BxPC-3/Gem and PANC-1/Gem). Using low concentrations of gemcitabine, the cell lines acquired drug resistance with different initial gemcitabine concentrations. A novel technology, methylation-sensitive high-resolution melting, was used to monitor the dynamic changes of ABC transporter family promoter methylation, including ATP binding cassette subfamily B member 1 (ABCB1), ATP binding cassette subfamily C (ABCC) and ATP binding cassette subfamily G member 2 (ABCG2) mRNA expression. It was revealed that, with elevation of initial gemcitabine concentration, expression of ABCB1, ABCC and ABCG2 mRNA and corresponding downstream proteins was increased while promoter methylation was decreased. These discoveries indicate that promoter methylation of ABCB1, ABCC and ABCG2 may be a valuable indicator of drug-resistance characteristics in BxPC-3/Gem and PANC-1/Gem cells via quantitative and simultaneous detection. These results also implied that MDR in pancreatic cancer not only arises from gene mutation, but also originates from promoter methylation.

Functional engineered human cardiac patches prepared from nature's platform improve heart function after acute myocardial infarction.

With the advent of induced pluripotent stem cells and directed differentiation techniques, it is now feasible to derive individual-specific cardiac cells for human heart tissue engineering. Here we report the generation of functional engineered human cardiac patches using human induced pluripotent stem cells-derived cardiac cells and decellularized natural heart ECM as scaffolds. The engineered human cardiac patches can be tailored to any desired size and shape and exhibited normal contractile and electrical physiology in vitro. Further, when patching on the infarct area, these patches improved heart function of rats with acute myocardial infarction in vivo. These engineered human cardiac patches can be of great value for normal and disease-specific heart tissue engineering, drug screening, and meet the demands for individual-specific heart tissues for personalized regenerative therapy of myocardial damages in the future.