Comparison between solid pseudopapillary neoplasms of the pancreas and pancreatic ductal adenocarcinoma with cystic changes using computed tomography.

BACKGROUND: Solid pseudopapillary neoplasms of the pancreas (SPN) share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes (PDAC with cystic changes), which may result in unnecessary surgery. AIM: To investigate the value of computed tomography (CT) in differentiation of SPN from PDAC with cystic changes. METHODS: This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes, confirmed through pathological diagnosis. Quantitative and qualitative analysis was performed, including assessment of age, sex, tumor size, shape, margin, density, enhancement pattern, CT values of tumors, CT contrast enhancement ratios, "floating cloud sign," calcification, main pancreatic duct dilatation, pancreatic atrophy, and peripancreatic invasion or distal metastasis. Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes, and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination. RESULTS: When compared to PDAC with cystic changes, SPN had a lower age (32 years vs 64 years, P < 0.05) and a slightly larger size (5.41 cm vs 3.90 cm, P < 0.05). SPN had a higher frequency of "floating cloud sign" and peripancreatic invasion or distal metastasis than PDAC with cystic changes (both P < 0.05). No significant difference was found with respect to sex, tumor location, shape, margin, density, main pancreatic duct dilatation, calcification, pancreatic atrophy, enhancement pattern, CT values of tumors, or CT contrast enhancement ratios between the two groups (all P > 0.05). The area under the receiver operating characteristic curve of the combination was 0.833 (95% confidence interval: 0.708-0.957) with 78.6% sensitivity, 81.3% specificity, and 80.4% accuracy in differentiation of SPN from PDAC with cystic changes. CONCLUSION: A larger tumor size, "floating cloud sign," and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.

Enhanced MRI Radiomics Based Model for Predicting Recurrence or Metastasis of Nasopharyngeal Cancer (NC) Undergoing Concurrent Chemoradiotherapy: A Retrospective Study.

Nasopharyngeal Carcinoma (NC) refers to the malignant tumor that occurs at the top and side walls of the nasopharyngeal cavity. The NC incidence rate always dominates the first among the malignant tumors of the ear, nose and throat, and mainly occurs in Asia. NC cases are mainly concentrated in southern provinces in China, with about 4 million existing NC. With the pollution of environment and pickled diet, and the increase of life pressure, the domestic NC incidence rate has reached 4.5-6.5/100000 and is increasing year by year. It was reported that the known main causes of NC include hereditary factor, genetic mutations, and EB virus infection, common clinical symptoms of NC include nasal congestion, bloody mucus, etc. About 90% of NC is highly sensitive to radiotherapy which is regard as the preferred treatment method; However, for NC with lower differentiation, larger volume, and recurrence after treatment, surgical resection and local protons and heavy ions therapy are also indispensable means. According to reports, the subtle heterogeneity and diversity exists in some NC, with about 80% of NC undergone radiotherapy and about 25% experienced recurrence and death within five years after radiotherapy in China. Therefore, screening the NC population with suspected recurrence after concurrent chemoradiotherapy may improve survival rates in current clinical decision-making.

NC is one of the prevalent malignancies of the head and neck region with poor prognosis. The aim of this study is to establish a predictive model for assessing NC prognosis using clinical and MR radiomics data.

PTPN23[Thr] variant reduces susceptibility and tumorigenesis in esophageal squamous cell carcinoma through dephosphorylation of EGFR.

Post-translational modifications (PTMs) have emerged as pivotal regulators of the development of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we conducted a comprehensive analysis of PTM-related genetic variants associated with ESCC risk using large-scale genome-wide and exome-wide association datasets. We observed significant enrichment of PTM-related variants in the ESCC risk loci and identified five variants that were significantly associated with ESCC risk. Among them, rs6780013 in PTPN23 exhibited the highest level of significance in ESCC susceptibility in 9,728 ESCC cases and 10,977 controls (odds ratio [OR] = 0.85, 95 % confidence interval [CI] = 0.81- 0.89, P = 9.77 × 10-14). Further functional investigations revealed that PTPN23[Thr] variant binds to EGFR and modulates its phosphorylation at Thr699. PTPN23[Thr] variant substantially inhibited ESCC cell proliferation both in vitro and in vivo. Our findings underscore the critical role of PTPN23[Thr]-EGFR interaction in ESCC development, providing more insights into the pathogenesis of this cancer.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma.

BACKGROUND: One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages. AIM: To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. METHODS: A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model. RESULTS: A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models. CONCLUSION: The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

Serum Levels of Intercellular Adhesion Molecule 1 and Vascular Cell Adhesion Molecule 1 as Biomarkers to Predict Radiotherapy Sensitivity in Cervical Cancer.

Background: Cervical cancer is a significant global health burden, and individualized treatment approaches are necessary due to its heterogeneity. Radiotherapy is a common treatment modality; however, the response varies among patients. The identification of reliable biomarkers to predict radiotherapy sensitivity is crucial. Methods: A cohort of 189 patients with stage IB2-IVA cervical cancer, treated with radiotherapy alone or concurrent chemoradiotherapy, was included. Serum samples were collected before treatment, and intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) concentrations were determined. Patients were categorized into radiotherapy-sensitive (RS) and radiotherapy-resistant (RR) groups based on treatment response. Clinicopathological characteristics and survival rates were analyzed. Results: The analysis of clinicopathological characteristics showed that age, family history of cervical cancer and post-menopausal status did not significantly differ between RS and RR groups. Tumor size demonstrated a borderline significant association with radiotherapy response, while differentiation degree was significantly associated. Serum ICAM-1 and VCAM-1 concentrations were significantly higher in the RR group compared to the RS group. Combined detection of ICAM-1 and VCAM-1 improved the predictive ability for radiotherapy sensitivity. Higher serum ICAM-1 and VCAM-1 levels were observed in patients with lower tumor differentiation. Five-year overall survival rates differed significantly between patients with high and low ICAM-1 and VCAM-1 levels. Conclusion: Serum ICAM-1 and VCAM-1 levels show potential as predictive biomarkers for radiotherapy sensitivity in cervical cancer.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.

Background: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results: Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. Conclusions: The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.

Prognostic modeling for nasopharyngeal carcinoma (NC) undergoing concurrent chemoradiotherapy using clinical and enhanced MRI-Delta radiomics data: A preliminary study.

BACKGROUND: Nasopharyngeal carcinoma (NC) is one of the prevalent malignancies of the head and neck region with poor prognosis. OBJECTIVE: The aim of this study is to establish a predictive model for assessing NC prognosis based on clinical and MR radiomics data, subsequently to develop a nomogram for practical application. METHODS: Retrospective analysis was conducted on clinical and imaging data collected between May 2010 and August 2018, involving 211 patients diagnosed with histologically confirmed NC who received concurrent chemoradiotherapy or radical surgery in Xiangyang No. 1 People's Hospital. According to 5-10 years of follow-up results, the patients were divided into two groups: the study group (n= 76), which experienced recurrence, metastasis, or death, and the control group (n= 135), characterized by normal survival. Training and testing subsets were established at a 7:3 ratio, with a predefined time cutoff. In the training set, three prediction models were established: a clinical data model, an imaging model, and a combined model using the integrated variation in clinical characteristics along with MR radiomics parameters (Delta-Radscore) observed before and after concurrent chemoradiotherapy. Model performance was compared using Delong's test, and net clinical benefit was assessed via decision curve analysis (DCA). Then, external validation was conducted on the test set, and finally a nomogram predicting NC prognosis was created. RESULTS: Univariate analysis identified that the risk factors impacting the prognosis of NC included gender, pathological type, neutrophil to lymphocyte ratio (NLR), degree of tumor differentiation, MR enhancement pattern, and Delta-Radscore (P< 0.05). The combined model established based on the abovementioned factors exhibited significantly higher predictive performance [AUC: 0.874, 95% CI (0.810-0.923)] than that of the clinical data model [AUC: 0.650, 95% CI (0.568-0.727)] and imaging model [AUC: 0.824, 95% CI (0.753-0.882)]. DCA also demonstrated superior clinical net benefit in the combined model, a finding further verified by results from the test set. The developed nomogram, based on the combined model, exhibited promising performance in clinical applications. CONCLUSION: The Delta-Radscore derived from MR radiomics data before and after concurrent chemoradiotherapy helps enhance the performance of the NC prognostic model. The combined model and resultant nomogram provide valuable support for clinical decision-making in NC treatment, ultimately contributing to an improved survival rate.

Ex vivo drug testing of patient-derived lung organoids to predict treatment responses for personalized medicine.

Lung cancer is the leading cause of global cancer-related mortality resulting in âˆ¼ 1.8 million deaths annually. Systemic, molecular targeted, and immune therapies have provided significant improvements of survival outcomes for patients. However, drug resistance usually arises and there is an urgent need for novel therapy screening and personalized medicine. 3D patient-derived organoid (PDO) models have emerged as a more effective and efficient alternative for ex vivo drug screening than 2D cell culture and patient-derived xenograft (PDX) models. In this review, we performed an extensive search of lung cancer PDO-based ex vivo drug screening studies. Lung cancer PDOs were successfully established from fresh or bio-banked sections and/or biopsies, pleural effusions and PDX mouse models. PDOs were subject to ex vivo drug screening with chemotherapy, targeted therapy and/or immunotherapy. PDOs consistently recapitulated the genomic alterations and drug sensitivity of primary tumors. Although sample sizes of the previous studies were limited and some technical challenges remain, PDOs showed great promise in the screening of novel therapy drugs. With the technical advances of high throughput, tumor-on-chip, and combined microenvironment, the drug screening process using PDOs will enhance precision care of lung cancer patients.

Single-cell analysis identified POSTN+ cells associated with the aggressive phenotype and risk of esophageal squamous cell carcinoma.

Tumors are intricate and heterogeneous systems characterized by mosaic cancer cell populations with diverse expression profiles. Leveraging single-cell technologies, we employed the Scissor algorithm to delineate an epithelial subpopulation associated with the aggressive phenotype in esophageal squamous cell carcinoma (ESCC). This identified subpopulation exhibited elevated expression of genes involved in critical pathways, such as epithelial-mesenchymal transition and PI3K-Akt. Key signature genes within this subpopulation, namely CAV1, COL3A1, COL6A1, POSTN, and TAGLN, demonstrated significant upregulation concomitant with both tumorigenesis and tumor progression across independent single-cell datasets. Furthermore, we selected 1,450 expression quantitative trait loci of the top 62 signature genes of this cell subpopulation to investigate their potential in predicting ESCC risk. The results showed that the POSTN loci were predominantly associated with ESCC susceptibility. Through functional annotation and replication analyses, we identified that the rs1028728 in the POSTN promoter was significantly associated with increased ESCC risk in 7,049 ESCC cases and 8,063 controls (odds ratio = 1.29, 95% confidence interval: 1.18-1.42, p = 4.03 × 10-8). Subsequent biochemical experiments showed that the rs1028728[T] allele enhanced POSTN expression by affecting the binding of PRRX1 in the POSTN promoter. In summary, our meticulous single-cell analysis delineates an invasive epithelial subpopulation in ESCC, with POSTN emerging as an important marker for the aggressive phenotype. These findings offer more insights into potential strategies for the prevention and intervention of ESCC, enriching our understanding of this complex cancer landscape.

Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models.

BACKGROUND: We aimed to redefine Immune checkpoint inhibitors (ICIs)-responsive "hot" TME and develop a corresponding stratification model to maximize ICIs-efficacy in Hepatocellular Carcinoma (HCC). METHODS: Hypoxic scores were designed, and the relevance to immunotherapy responses were validated in pan-cancers through single cell analysis. Multi-omics analysis using the hypoxic scores and immune infiltrate abundance was performed to redefine the ICIs-responsive TME subtype in HCC patients from TCGA (n = 363) and HCCDB database (n = 228). The immune hypoxic stress index (IHSI) was constructed to stratify the ICIs-responsive TME subtype, with exploring biological mechanism in vitro and in vivo. MRI-radiomics models were built for clinical applicability. RESULTS: The hypoxic scores were lower in the dominant cell-subclusters of responders in pan-cancers. The higher immune infiltrate-normoxic (HIN) subtype was redefined as the ICIs-responsive TME. Stratification of the HIN subtype using IHSI effectively identified ICIs-responders in Melanoma (n = 122) and urological cancer (n = 22). TRAF3IP3, the constituent gene of IHSI, was implicated in ICIs-relevant "immune-hypoxic" crosstalk by stimulating MAVS/IFN-I pathway under normoxic condition. MRI-radiomics models assessing TRAF3IP3 with HIF1A expression (AUC > 0.80) screened ICIs-Responders in HCC cohort (n = 75). CONCLUSION: The hypoxic-immune stratification redefined ICIs-responsive TME and provided MRI-Radiomics models for initial ICIs-responders screening, with IHSI facilitating further identification.

Systematical identification of key genes and regulatory genetic variants associated with prognosis of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) stands as a highly lethal malignancy characterized by pronounced recurrence and metastasis, resulting in a bleak 5-year survival rate. Despite extensive investigations, encompassing genome-wide association studies, the identification of robust prognostic markers has remained elusive. In this study, leveraging four independent data sets comprising 404 ESCC patients, we conducted a systematic analysis to unveil pivotal genes influencing overall survival. our meta-analysis identified 278 genes significantly associated with ESCC prognosis. Further exploration of the prognostic landscape involved an examination of expression quantitative trait loci for these genes, leading to the identification of six tag single nucleotide polymorphisms predictive of overall survival in a cohort of 904 ESCC patients. Notably, functional annotation spotlighted rs11227223, residing in the enhancer region of nuclear paraspeckle assembly transcript 1 (NEAT1), as a crucial variant likely exerting a substantive biological role. Through a series of biochemistry experiments, we conclusively demonstrated that the rs11227223-T allele, indicative of a poorer prognosis, augmented NEAT1 expression. Our results underscore the substantive role of NEAT1 and its regulatory variant in prognostic predictions for ESCC. This comprehensive analysis not only advances our comprehension of ESCC prognosis but also unveils a potential avenue for targeted interventions, offering promise for enhanced clinical outcomes.

Multimodal Imaging-Guided Photoimmunotherapy of Pancreatic Cancer by Organosilica Nanomedicine.

Immune checkpoint blockade (ICB) treatments have contributed to substantial clinical progress. However, challenges persist, including inefficient drug delivery and penetration into deep tumor areas, inadequate response to ICB treatments, and potential risk of inflammation due to over-activation of immune cells and uncontrolled release of cytokines following immunotherapy. In response, this study, for the first time, presents a multimodal imaging-guided organosilica nanomedicine (DCCGP) for photoimmunotherapy of pancreatic cancer. The novel DCCGP nanoplatform integrates fluorescence, magnetic resonance, and real-time infrared photothermal imaging, thereby enhancing diagnostic precision and treatment efficacy for pancreatic cancer. In addition, the incorporated copper sulfide nanoparticles (CuS NPs) lead to improved tumor penetration and provide external regulation of immunotherapy via photothermal stimulation. The synergistic immunotherapy effect is realized through the photothermal behavior of CuS NPs, inducing immunogenic cell death and relieving the immunosuppressive tumor microenvironment. Coupling photothermal stimulation with αPD-L1-induced ICB, the platform amplifies the clearance efficiency of tumor cells, achieving an optimized synergistic photoimmunotherapy effect. This study offers a promising strategy for the clinical application of ICB-based combined immunotherapy and presents valuable insights for applications of organosilica in precise tumor immunotherapy and theranostics.

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.

Development and external validation of a clinical-radiomics nomogram for preoperative prediction of LVSI status in patients with endometrial carcinoma.

PURPOSE: To develop and validate a model that incorporates radiomics based on MRI scans and clinical characteristics to predict lymphovascular invasion (LVSI) in endometrial cancer (EC) patients. METHODS: There were 332 patients with EC enrolled retrospectively in this multicenter study. Radiomics score (Radscore) were computed using the valuable radiomics features. The independent predictors of LVSI were identified by univariate logistic analysis. Multivariate logistic regression was used to develop a clinical-radiomics predictive model. Based on the model, a nomogram was developed and validated internally and externally. The nomogram was evaluated with discrimination, calibration, decision curve analysis (DCA), and clinical impact curves (CIC). RESULTS: Three predictive models were constructed based on clinicopathological features, radiomic factors and a combination of them, and that the clinic-radiomic model performed best among the three models. Four independent factors comprised the clinical-radiomics model: dynamic contrast enhancement rate of late arterial phase (DCE2), deep myometrium invasion (DMI), lymph node metastasis (LNM), and Radscore. Clinical-radiomics model performance was 0.901 (95% CI 0.84-0.96) in the training cohort, 0.80 (95% CI 0.68-0.92) in the internal validation cohort, and 0.81 (95% CI 0.73-0.9) in the external validation cohort for identifying patients with LVSI, respectively. The model is used to develop a nomogram for clinical use. CONCLUSIONS: The MRI-based radiomics nomogram could serve as a noninvasive tool to predict LVSI in EC patients.

Exploring the key genetic association between chronic pancreatitis and pancreatic ductal adenocarcinoma through integrated bioinformatics.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma. Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis. Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME. Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.