A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma.

Objectives: Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS). Methods: In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284). Results: Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI: 0.64, 0.85) and 0.76 (95% CI: 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI: 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI: 0.75, 0.88). Conclusion: Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma.

The Effects of Radiotherapy on Pancreatic Ductal Adenocarcinoma in Patients with Liver Metastases.

BACKGROUND: While radiotherapy has been studied in the treatment of locally advanced pancreatic ductal adenocarcinoma (PDAC), few studies have analyzed the effects of radiotherapy on PDAC in patients with liver metastases. This study aimed to determine whether PDAC patients with liver metastases have improved survival after radiotherapy treatment. METHODS: The data of 8535 patients who were diagnosed with PDAC with liver metastases between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis and Cox proportional hazards regression analysis of cancer-specific mortality and overall survival were performed, and propensity score matching (PSM) was used to reduce selection bias. RESULTS: After PSM, the median overall survival (mOS) and median cancer-specific survival (mCSS) in the radiotherapy group were longer than those in the nonradiotherapy group (OS: 6 months vs. 4 months; mCSS: 6 months vs. 5 months, both p < 0.05), respectively. The multivariate analysis showed that cancer-specific mortality rates were higher in the nonradiotherapy group than in the radiotherapy group (HR: 1.174, 95% CI: 1.035-1.333, p = 0.013). The Cox regression analysis according to subgroups showed that the survival benefits (OS and CSS) of radiotherapy were more significant in patients with tumor sizes greater than 4 cm (both p < 0.05). CONCLUSIONS: PDAC patients with liver metastases, particularly those with tumor sizes greater than 4 cm, have improved cancer-specific survival (CSS) rates after receiving radiotherapy.

Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome.

OBJECTIVES: To construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival. METHODS: A total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups. RESULTS: The AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001). CONCLUSIONS: We propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.

Quantitative dual-energy CT for evaluating hepatocellular carcinoma after transarterial chemoembolization.

We aimed to investigate the role of the quantitative parameters of dual-energy computed tomography (DECT) in evaluating patients with hepatocellular carcinoma (HCC) treated by transarterial chemoembolization (TACE). We retrospectively identified 80 HCC patients (mean age, 56 years; 61 men) treated by TACE who received contrast-enhanced DECT and were retreated by TACE within 7 days between November 2018 and December 2019. Taking digital subtraction angiography (DSA) and CT images as reference standard, two readers measured and calculated the values of normalized iodine concentration at arterial phase (NICAP), normalized iodine concentration at portal venous phase (NICPP), iodine concentration difference (ICD), arterial iodine fraction (AIF) and slope of the spectral Hounsfield unit curve (λHu) by placing matched regions of interests (ROIs) within the tumor active area (TAA), adjacent normal hepatic parenchyma (ANHP) and tumor necrotic area (TNA). Differences between the parameters were analyzed by the Kruskal-Wallis H test. Receiver operating characteristic analysis of the parameters performance in differentiating the three tissues types was performed. AIF exhibited a good performance in distinguishing TAA (0.93 ± 0.31) and ANHP (0.18 ± 0.14), the areas under the receiver operating characteristic curve (AUC) was 0.989, while the λHu exhibited an excellent performance in distinguishing TAA (3.32 ± 1.24) and TNA (0.29 ± 0.27), with an AUC of 1.000. In conclusion, quantitative DECT can be effectively used to evaluate the tumor viability in HCC patients treated by TACE.

Relationship between pancreatic parenchyma loss and early postoperative hyperglycemia in patients with benign pancreatic diseases.

OBJECTIVE: To evaluate the relationship between pancreatic parenchyma loss and early postoperative hyperglycemia in patients with benign pancreatic diseases. METHODS: A total of 171 patients with benign pancreatic tumors or chronic pancreatitis, whose preoperative fasting blood glucose (FBG) was normal and who underwent partial pancreatectomy were reviewed. The pancreatic volume was measured by CT imaging before and after the operation. According to their different pancreatic resection volume (PRV), 171 patients were divided into five groups: < 30%, 30%-39%, 40%-49%, 50%-59%, and ≥ 60%. The correlation between the PRV and postoperative FBG was investigated. According to the postoperative FBG value, the patients were divided into a hyperglycemia group (HG) and nonhyperglycemia group (non-HG) to explore the best cutoff value of the PRV between the two groups. RESULTS: There were significant differences in the postoperative FBG among the five groups (PRV < 30%, 30%-39%, 40%-49%, 50%-59%, and ≥ 60%). The PRV was positively correlated with postoperative FBG in the benign tumor group and chronic pancreatitis group (R = 0.727 and 0.651, respectively). ROC curve analysis showed that the best cutoff value of the PRV between the HG (n = 84) and non-HG (n = 87) was 39.95% with an AUC = 0.898; the sensitivity was 89.29%, and the specificity was 82.76%. CONCLUSION: There was a linear positive correlation between the postoperative FBG level and PRV. Patients with a PRV ≥ 40% are more likely to develop early postoperative hyperglycemia.

Assessment of the embolization effect of temperature-sensitive p(N-isopropylacrylamide-co-butyl methylacrylate) nanogels in the rabbit renal artery by CT perfusion and confirmed by macroscopic examination.

Transcatheter embolization is an important treatment method in clinical therapy, and vascular embolization material plays a key role in embolization. The temperature-sensitive p(N-isopropylacrylamide-co-butyl methylacrylate) (PIB) nanogel is a novel embolic agent. To evaluate the feasibility of the nanogel as a blood vessel embolization agent, we aimed to assess the effect of embolization with PIB nanogels in the rabbit renal artery by non-invasive computed tomography (CT) perfusion, macroscopic and histological examination. Ten healthy adult Japanese rabbits were used to implement RAE of PIB nanogels in their right kidneys. CT perfusion scans were performed pre- and post-treatment at various time-points (1, 4, 8, and 12 weeks). Two rabbits were euthanized and histologically examined at each time-point, and the remaining rabbits were euthanized at 12 weeks after embolization. The RAE efficacy of the nanogels was further confirmed by macroscopic and histological examination. The renal volume and renal blood flow (BF) of the right kidney were significantly decreased post-treatment compared with those pre-treatment (volume: pre, 9278 ± 1736 mm3; post 1 week, 5155 ± 979 mm3, P < 0.0001; post 4 weeks, 3952 ± 846 mm3, P < 0.0001; post 8 weeks, 3226 ± 556 mm3, P < 0.0001; post 12 weeks, 2064 ± 507 mm3, P < 0.0001. BF: pre, 530.81 ± 51.50 ml/min/100 ml; post 1 week, 0 ml/min/100 ml, P < 0.0001; post 4 weeks, 0 ml/min/100 ml, P < 0.0001; post 8 weeks, 0 ml/min/100 ml, P < 0.0001; post 12 weeks, 0 ml/min/100 ml, P < 0.0001). No revascularization or collateral circulation was observed on histological examination during this period, and PIB nanogels were dispersed in all levels of the renal arteries. Twelve weeks after embolization, CT perfusion showed no BF in the right renal artery and renal tissue, a finding that was consistent with histological examination showing complete embolization of the right renal artery with a lack of formation of collateral vessels. The effect of embolization on PIB was adequate, with good dispersion and permanency, and could be evaluated by non-invasive and quantitative CT perfusion.

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms. Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89-0.95] and 0.86 [95% CI: 0.80-0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. -1.60 and 2.35 vs. -1.01 on training and test cohorts, respectively, p < 0.001). Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor with rapid progression and poor prognosis. In the present study, 11 high­quality microarray datasets, comprising 334 tumor samples and 151 non­tumor samples from the Gene Expression Omnibus, were screened, and integrative meta­analysis of expression data was used to identify gene signatures that differentiate between PDAC and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two­way hierarchical clustering analysis was performed for all DEGs using the gplots package in R software. Hub genes were then determined through protein­protein interaction network analysis using NetworkAnalyst. In addition, functional annotation and pathway enrichment analyses of all DEGs were conducted in the Database for Annotation, Visualization, and Integrated Discovery. The expression levels and Kaplan­Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing PDAC with normal tissues. Of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium­binding protein A6 (S100A6) were the top upregulated genes, and kinesin family member 1A, Dymeclin and ß­secretase 1 were the top downregulated genes. Reverse transcription­quantitative PCR was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in PDAC compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member C overexpression was strongly associated with longer overall survival times. In addition, network­based meta­analysis confirmed growth factor receptor­bound protein 2 and histone deacetylase 5 as pivotal hub genes in PDAC compared with normal tissue. In conclusion, the results of the present meta­analysis identified PDAC­related gene signatures, providing new perspectives and potential targets for PDAC diagnosis and treatment.

Integrated analysis of 34 microarray datasets reveals CBX3 as a diagnostic and prognostic biomarker in glioblastoma.

BACKGROUND: Glioblastomas have a high degree of malignancy, high recurrence rate, high mortality rate, and low cure rate. Searching for new markers of glioblastomas is of great significance for improving the diagnosis, prognosis and treatment of glioma. METHODS: Using the GEO public database, we combined 34 glioma microarray datasets containing 1893 glioma samples and conducted genetic data mining through statistical analysis, bioclustering, and pathway analysis. The results were validated in TCGA, CGGA, and internal cohorts. We further selected a gene for subsequent experiments and conducted cell proliferation and cell cycle analyses to verify the biological function of this gene. RESULTS: Eight glioblastoma-specific differentially expressed genes were screened using GEO. In the TCGA and CGGA cohorts, patients with high CBX3, BARD1, EGFR, or IFRD1 expression had significantly shorter survival but patients with high GUCY1A3 or MOBP expression had significantly longer survival than patients with lower expression of these genes. After reviewing the literature, we selected the CBX3 gene for further experiments. We confirmed that CBX3 was overexpressed in glioblastoma by immunohistochemical analysis of tissue microarrays and qPCR analysis of surgical specimens. The functional assay results showed that silencing CBX3 arrests the cell cycle in the G2/M phase, thereby weakening the cell proliferation ability. CONCLUSIONS: We used a multidisciplinary approach to analyze glioblastoma samples in 34 microarray datasets, revealing novel diagnostic and prognostic biomarkers in patients with glioblastoma and providing a new direction for screening tumor markers.