Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance

PURPOSE

The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.

PATIENTS AND METHODS

PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher's exact tests or Kruskal-Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.

RESULTS

There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703-0.989.

CONCLUSION

Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions. Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively. Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC.

Pancreatic Ductal Adenocarcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis For Prediction Of Histopathological Grade

PURPOSE

To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC).

METHODS

From July 2012 to August 2017, this retrospective study comprised 56 patients with confirmed histopathological PDAC (32 men, 24 women, mean age 64.04±7.82 years) who had undergone preoperative contrast-enhanced CT imaging within 1 month before surgery. Two radiologists blinded to the histopathological outcome independently segmented lesions for quantitative texture analysis. Histogram features, co-occurrence, and run-length texture were calculated. A support-vector machine was constructed to predict the pathological grade of PDAC based on preoperative texture features.

RESULTS

Pathological analysis confirmed 37 low-grade PDAC (five well-differentiated/grade I and 32 moderately differentiated/grade II) and 19 high-grade PDAC (19 poorly differentiated/grade III) tumors. There were no significant differences in clinical or biological characteristics between patients with high-grade and low-grade tumors (P>0.05). There were significant differences between low-grade PDAC and high-grade PDAC on nine histogram features, seven run-length features, and two co-occurrence features. Cluster shade was the most important predictor (sensitivity 0.315). Using these texture features, the support-vector machine achieved 86% accuracy, 78% sensitivity, 95% and specificity.

CONCLUSION

Machine learning-based CT texture analysis accurately predicted histopathological differentiation grade of PDAC based on preoperative texture features, leading to maximization patient survival and achievement of personalized precision treatment.

Emodin suppresses cadmium-induced osteoporosis by inhibiting osteoclast formation

Environmental level of cadmium (Cd) exposure can induce bone loss. Emodin, a naturally compound found in Asian herbal medicines, could influence osteoblast/osteoclast differentiation. However, the effects of emodin on Cd-induced bone damage are not clarified. The aim of this study was to investigate the role of emodin on Cd-induced osteoporosis. Sprague-Dawley male rats were divided into three groups which were given 0mg/L, 50mg Cd/L and 50mg Cd/L plus emodin (50mg/kg body weight). Bone histological investigation, microCT analysis, metabolic biomarker determination and immunohistochemical staining were performed at the 12th week. The bone mass and bone microstructure index of rats treated with Cd were obviously lower than in control. Cd markedly enhanced the osteoclast formation compared with control. Emodin significantly abolished the Cd-induced bone microstructure damage (p<0.05), osteoclast formation and increase of tartrate-resistant acid phosphatase 5b level (p<0.05). Our data further showed that emodin attenuated the Cd-induced inhibition of osteoprotegerin expression and stimulation of receptor activator for nuclear factor-κ B ligand expression. Our data show that emodin suppresses the Cd-induced osteoporosis by inhibiting osteoclast formation.

Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma

PURPOSE

To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann-Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded.

RESULTS

We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann-Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model.

CONCLUSIONS

Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.

Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma

PURPOSE

The purpose was to assess the predictive ability of computed tomography (CT)-based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Eighty-one patients (63.6 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase (n = 396) and portal venous phase (n = 396) for each case. Significantly different features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method.

RESULTS

Seven radiomics features from late arterial phase images and three from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC, with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using the LGOCV method, with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV.

CONCLUSION

CT-based radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

BACKGROUND

Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.

METHODS

We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.

RESULTS

Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model.

CONCLUSION

The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.

Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer

BACKGROUND

Pancreatic cancer (PC) remains a difficult tumor to diagnose and treat. It is often diagnosed as advanced by reason of the anatomical structure of the deep retroperitoneal layer of the pancreas, lack of typical symptoms and effective screening methods to detect this malignancy, resulting in a low survival rate. Emodin (EMO) is an economical natural product with effective treatment and few side effects of cancer treatment. Magnetic nanoparticles (MNPs) can achieve multiplexed imaging and targeted therapy by loading a wide range of functional materials such as fluorescent dyes and therapeutic agents.

PURPOSE

The purpose of this study was to design and evaluate a multifunctional theranostic nanoplatform for PC diagnosis and treatment.

METHODS

In this study, we successfully developed EMO-loaded, Cy7-functionalized, PEG-coated Fe3O4 (Fe3O4-PEG-Cy7-EMO). Characteristics including morphology, hydrodynamic size, zeta potentials, stability, and magnetic properties of Fe3O4-PEG-Cy7-EMO were evaluated. Fluorescence imaging (FI)/magnetic resonance imaging (MRI) and therapeutic treatment were examined in vitro and in vivo.

RESULTS

Fe3O4-PEG-Cy7-EMO nanoparticles had a core size of 9.9 ± 1.2 nm, which showed long-time stability and FI/MRI properties. Bio-transmission electron microscopy (bio-TEM) results showed that Fe3O4-PEG-Cy7-EMO nanoparticles were endocytosed into BxPC-3 cells, while few were observed in hTERT-HPNE cells. Prussian blue staining also confirmed that BxPC-3 cells have a stronger phagocytic ability as compared to hTERT-HPNE cells. Additionally, Fe3O4-PEG-Cy7-EMO had a stronger inhibition effect on BxPC-3 cells than Fe3O4-PEG and EMO. The hemolysis experiment proved that Fe3O4-PEG-Cy7-EMO can be used in vivo experiments. In vivo analysis demonstrated that Fe3O4-PEG-Cy7-EMO enabled FI/MRI dual-modal imaging and targeted therapy in pancreatic tumor xenografted mice.

CONCLUSION

Fe3O4-PEG-Cy7-EMO may serve as a potential theranostic nanoplatform for PC.

Differentiation of hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

Hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) are often misdiagnosed as pancreatic ductal adenocarcinoma (PDAC). However, the treatment options and prognosis of PNETs and PDAC are substantially different. This retrospective study differentiated hypo-PNETs from PDAC using contrast-enhanced CT (CE-CT). Clinical data and CE-CT findings, including tumor location, size, boundary, pancreatic duct dilatation, local invasion or metastases, tumor contrast enhancement, and tumor-to-pancreas enhancement ratio, were compared between 39 PDACs and 18 hypo-PNETs. At CT imaging, hypo-PNETs showed a higher frequency of a well-defined margin and lower frequencies of pancreatic duct dilatation and local invasion or metastasis when compared with PDAC (p < 0.05 for all). The mean attenuation of hypo-PNETs at the arterial and portal venous phase was significantly higher than that of PDAC (p < 0.001, p = 0.003, respectively). Similar results were observed in tumor-to-pancreas enhancement ratio. Tumor attenuation and tumor-to-pancreas enhancement ratio at the arterial phase showed the largest area under the curve (AUC) of 0.888 and 0.812 with 83.3-88.9% of sensitivity and 61.6-77.0% of specificity. Pancreatic duct dilatation, local invasion or metastasis, and tumor attenuation at the portal venous phase also showed acceptable AUC (0.703-0.748). Thus CE-CT features, especially the enhancement degree at the arterial phases, may be useful for differentiating hypo-PNETs from PDAC using CE-CT.

A GPC1-targeted and gemcitabine-loaded biocompatible nanoplatform for pancreatic cancer multimodal imaging and therapy

Aim: Biomarker-targeted nanocarrier holds promise for early diagnosis and effective therapy of cancer. Materials & methods: This work successfully designs and evaluates GPC1-targeted, gemcitabine (GEM)-loaded multifunctional gold nanocarrier for near-infrared fluorescence (NIRF)/MRI and targeted chemotherapy against pancreatic cancer in vitro and in vivo. Results: Blood biochemical and histological analyses show that the in vivo toxicity of GPC1-GEM-nanoparticles (NPs) was negligible. Both in vitro and in vivo studies demonstrate that GPC1-GEM-NPs can be used as NIRF/MR contrast agent for pancreatic cancer detection. Treatment of xenografted mice with GPC1-GEM-NPs shows a higher tumor inhibitory effect compared with controls. Conclusion: This novel theranostic nanoplatform provides early diagnostic and effective therapeutic potential for pancreatic cancer.

Exogenous HMGB1 Promotes the Proliferation and Metastasis of Pancreatic Cancer Cells

Background: Exogenous HMGB1 plays a vital role in tumor recurrence, and HMGB1 is ubiquitous in the tumor microenvironment. However, the mechanism of action is still unclear. We investigated the role of exogenous HMGB1 in tumor proliferation and metastasis using human SW1990 and PANC-1 cells after radiotherapy and explored the possible molecular mechanism. Materials and Methods: Residual PANC-1 cells and SW1990 cells were isolated after radiotherapy. The supernatant after radiotherapy was collected. The relative expression of HMGB1 was evaluated by Enzyme Linked Immunosorbent Assay (ELISA). Electron microscope (EMS) was used to collect the images of pancreatic cancer cells pre and post radiotherapy treatment. The proliferation of pancreatic cancer cells which were treated with different radiation doses was measured by Carboxy Fluorescein Succinimidyl Ester (CFSE). The migration rates of pancreatic cancer cells were measured by wound healing assays. Subsequently, the expression of related proteins was detected by Western Blot. In vivo, the subcutaneous pancreatic tumor models of nude mice were established, and therapeutic capabilities were tested. Results: HMGB1 was detected in the supernatant of pancreatic cancer cells after radiotherapy. The results of CFSE showed that exogenous HMGB1 promotes the proliferation and metastasis of pancreatic cancer cells. The western blot results showed activation of p-GSK 3β and up-regulation of N-CA, Bcl-2, and Ki67 in response to HMGB1 stimulation, while E-CA expression was down-regulated in pancreatic cancer cells in response to HMGB1 stimulation. In vivo, ethyl pyruvate (EP, HMGB1 inhibitor) inhibits the growth of tumors and HMGB1 promotes the proliferation of tumors after radiation. Conclusion: Radiotherapy induces HMGB1 release into the extracellular space. Exogenous HMGB1 promotes the proliferation and metastasis of PANC-1 cells and SW1990 cells by activation of p-GSK 3β which is mediated by Wnt pathway.

Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

PURPOSE

Both chronic mass-forming pancreatitis (CMFP) and pancreatic ductal adenocarcinoma (PDAC) are focal pancreatic lesions and share very similar clinical symptoms and imaging performance. There is great clinical value in preoperative differentiation of those two lesions. The purpose of this study was to investigate the value of computed tomography (CT) features in discriminating CMFP from PDAC.

PATIENTS AND METHODS

Forty-seven patients with pathologically confirmed PDAC and 21 patients with CMFP were included in this study. Demographic and CT features, including tumor location, size, margin, pancreatic or bile duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement, were retrospectively analyzed and compared. Multivariate logistic regression analyses were adopted to identify relevant CT imaging features to discriminate CMFP from PDAC.

RESULTS

There were significant differences between CMFP and PDAC with respect to main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement. Delayed contrast enhancement (>70.5 Hounsfield units) showed high sensitivity and specificity of 84.2% and 84.7%. The areas under the curve (AUCs) of the predicting models based on qualitative and quantitative variables were 0.770 (95% CI: 0.660-0.880) and 0.943 (95% CI: 0.888-0.999), respectively. When all significant variables were used in combination to build a predicting model, the AUC was 0.969 (95% CI: 0.930-1.000) with 84.2% sensitivity and 94.7% specificity.

CONCLUSION

Main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, tumor size, and tumor contrast enhancement were shown to be useful CT imaging features in discriminating CMFP from PDAC.

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

Purpose: To determine the potential of mammography (MG) and mammographic texture analysis in differentiation between Grade 1 (G1) and Grade 2/ Grade 3 (G2/G3) phyllodes tumors (PTs) of breast. Materials and methods: A total of 80 female patients with histologically proven PTs were included in this study. 45 subjects who underwent pretreatment MG from 2010 to 2017 were retrospectively analyzed, including 14 PTs G1 and 31 PTs G2/G3. Tumor size, shape, margin, density, homogeneity, presence of fat, or calcifications, a halo-sign as well as some indirect manifestations were evaluated. Texture analysis features were performed using commercial software. Receiver operating characteristic curve (ROC) was used to determine the sensitivity and specificity of prediction. Results: G2/G3 PTs showed a larger size (>4.0 cm) compared to PTs G1 (64.52 vs. 28.57%, p = 0.025). A strong lobulation or multinodular confluent was more common in G2/G3 PTs compared to PTs G1 (64.52 vs. 14.29%, p = 0.004). Significant differences were also observed in tumors' growth speed and clinical manifestations (p = 0.007, 0.022, respectively). Ten texture features showed significant differences between the two groups (p < 0.05), Correlation_AllDirection_offset7_SD and ClusterProminence_AllDirection_offset7_SD were independent risk factors. The area under the curve (AUC) of imaging-based diagnosis, texture analysis-based diagnosis and the combination of the two approaches were 0.805, 0.730, and 0.843 (90.3% sensitivity and 85.7% specificity). Conclusions: Texture analysis has great potential to improve the diagnostic efficacy of MG in differentiating PTs G1 from PTs G2/G3.

Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram

PURPOSE

To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs.

MATERIAL AND METHODS

A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions.

RESULTS

The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis.

CONCLUSION

A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.

Differentiation of aggressive from non-aggressive pancreatic solid pseudopapillary neoplasms using computed tomography

PURPOSES

Microscopic aggressive behaviors may be related with the prognosis of solid pseudopapillary neoplasms (SPNs). In this study, we investigate computed tomography (CT) features and differential diagnosis of aggressive and non-aggressive SPNs in pancreas.

MATERIALS AND METHODS

122 patients with pathologically proven SPNs in pancreas were included. Patients' age, tumor site, texture, shape, margins, exophytic growth, capsule, calcification, hemorrhage, pancreatic duct dilatation or pancreatic parenchyma atrophy, peripancreatic infiltration or metastases, vascular encasement, and enhancement pattern were assessed. The diagnostic accuracy was analyzed by using the receiver operating characteristic curve (ROC).

RESULTS

There were 30 aggressive SPNs and 92 non-aggressive SPNs. Aggressive SPNs showed significantly higher frequencies of an ill-defined margin, patient age > 40.5 years, and tumor size < 42.1 mm, but lower frequencies of complete capsule, hemorrhage compared with non-aggressive SPNs (p < 0.05). Lack of complete capsule and age > 40.5 years were independent risk factors of aggressive SPNs (odd ratio 7.08 and 3.1, respectively). When we applied the two predictors in the logistic regression model, the area under the curve (AUC) was 0.77 with sensitivity of 86.7% and specificity of 55.4%.

CONCLUSION

Size less than 42.1 mm, lack of complete capsule, ill-defined, and absent bleeding are useful CT imaging features for predicating aggressive SPNs. Patient age > 40.5 years and lack of complete capsule showed acceptable diagnostic performance for discriminating aggressive from non-aggressive SPNs.

CT and MR imaging features of pancreatic adenosquamous carcinoma and their correlation with prognosis

PURPOSE

To retrospectively investigate the computed tomography (CT) and magnetic resonance (MR) imaging features of pancreatic adenosquamous carcinoma (PASC) and the association between imaging findings and prognosis.

MATERIALS AND METHODS

CT, MR images of 26 patients with PASC were analyzed. Clinical symptoms, tumor markers, and patients' survival were recorded. Tumor attenuation, enhancement pattern and degree, vessel involvement, adjacent tissue invasion and metastasis were evaluated. The association between imaging features and overall survival (OS) were also assessed using Cox proportional hazards ratio model.

RESULTS

Fourteen masses were found in the head of the pancreas and 12 in the body/tail. The mean tumor size was 4.47 ± 1.76 cm. PASC usually showed ill-defined (96.2%), lobulated (76.9%) and predominantly solid mass (92.3%). Ring enhancement in the peripheral area of the tumor was commonly seen (76.9%). Vessel invasion was seen in 17 cases (65.4%), encasement of adjacent arteries in 7 cases (26.9%), upstream main pancreatic duct (MPD) dilatation in 16 cases (61.5%) and double duct sign in 9 cases (34.6%). Multivariate Cox proportional hazards model demonstrated that patients with vessel invasion may predict a poor prognosis (p = 0.037).

CONCLUSION

PASC tends to be an ill-defined solid mass with peripheral ring enhancement, and relatively poor enhancement in the central area. PASC may also show vessel invasion, vessel encasement and upstream MPD dilatation. Vessel invasion may indicate a poor prognosis.

Imaging findings of intraductal tubulopapillary neoplasm (ITPN) of the pancreas: Two case reports and literature review

RATIONALE

Intraductal tubulopapillary neoplasm (ITPN) is a rare type of pancreatic epithelial neoplasm. We report 2 cases of ITPN and detail the imaging findings.

PATIENT CONCERNS

The 1st case was a 36-year-old woman who complained of jaundice, yellow urine and diarrhea. She accepted ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) examination before surgery, which all revealed a mass in the pancreatic head. The 2nd case was a 62-year-old woman who was admitted to our hospital for the treatment of a pancreatic tumor. The MRI showed a mass filled the mian pancreatic duct in the head and neck.

DIAGNOSIS

The ITPN is an intraductal, grossly visible, tubule-forming epithelial neoplasm with high-grade dysplasia and ductal differentiation without overt mucin production.

INTERVENTIONS

The 1st patient received percutaneous transhepatic cholangial drainage procedure, endoscopic ultrasound guided fine needle aspiration, pancreatoduodenectomy, cholecystectomy, and lymphadenectomy successively. The 2nd patient received pancreaticoduodenectomy, cholecystectomy, and partial gastrectomy.

OUTCOMES

Two months after surgery, the follow-up MRI revealed hepatic metastasis of the 1st patient. She is still alive now. The 2nd patient was lost to follow-up.

LESSONS

The ITPN is a rare pancreatic neoplasm and its clinical symptoms are atypical. It is difficult to make accurate diagnosis of ITPN before surgery even though various imaging modalities are used in combination. When a solid mass growing in the lumen of the pancreatic duct, ITPN should be taken into consideration.

Evaluation of contrast-enhanced computed tomography for the differential diagnosis of hypovascular pancreatic neuroendocrine tumors from chronic mass-forming pancreatitis

PURPOSE

To assess the role of contrast-enhanced computed tomography (CECT) for differentiation of hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) from chronic mass-forming pancreatitis (CMFP).

METHODS

A retrospective study of 59 patients (27 hypo-PNETs patients vs 32 CMFP patients) who underwent preoperative CECT between July 2012 and July 2019 was performed. Qualitative and quantitative analysis was performed, including mass location, size, margin, cystic changes, calcification, pancreatic or bile duct dilatation, pancreatic atrophy, local vessels involvement, mass contrast enhancement and mass-to-pancreas enhancement ratio. Multivariate logistic regression analyses were used to identify relevant CT imaging findings in differentiation between hypo-PNETs and CMFP.

RESULTS

When compared to CMFP, hypo-PNETs more frequently had a well-defined margin and cystic changes and less frequently had a history of pancreatitis and calcification. CMFP had higher mass contrast enhancement and mass-to-pancreas enhancement ratio in the portal and delayed phases than hypo-PNETs. After multivariate logistic regression analyses, areas under the curve (AUCs) were 0.795 (95 % CI: 0.652-0.899), 0.752 (95 % CI: 0.604-0.866), 0.859 (95 % CI: 0.726-0.943), and 0.929 (95 % CI: 0.814-0.983) for Model 1(clinical factors), Model 2 (qualitative parameters), Model 3 (quantitative parameters), and their combinations, respectively.

CONCLUSION

Combined assessment of clinical factors, qualitative, and quantitative imaging characteristics can improve the differentiation between hypo-PNETs and CMFP at CECT.

Potential Metabolite Biomarkers for Early Detection of Stage-I Pancreatic Ductal Adenocarcinoma

BACKGROUND & OBJECTIVES

Pancreatic ductal adenocarcinoma remains an extremely malignant tumor having a poor prognosis. The 5-year survival rate of PDAC is related to its stage (about 80% for stage I vs 20% for other stages). However, detection of PDAC in an early stage is difficult due to the lack of effective screening methods. In this study, we aimed to construct a novel metabolic model for stage-I PDAC detection, using both serum and tissue samples.

METHODS

We employed an untargeted technique, UHPLC-Q-TOF-MS, to identify the potential metabolite, and then used a targeted technique, GC-TOF-MS, to quantitatively validate. Multivariate and univariate statistics were performed to analyze the metabolomic profiles between stage-I PDAC and healthy controls, including 90 serum and 53 tissue samples. 28 patients with stage-I PDAC and 62 healthy controls were included in this study.

RESULTS

A total of 10 potential metabolites presented the same expression levels both in serum and in tissue. Among them, a 2-metabolites-model (isoleucine and adrenic acid) for stage-I PDAC was constructed. The area under the curve (AUC) value was 0.93 in the discovery set and 0.90 in the independent validation set. Especially, the serum metabolite model had a better diagnostic performance than CA19-9 (AUC = 0.79). Pathway analysis revealed 11 altered pathways in both serum and tissue of stage-I PDAC.

CONCLUSIONS

This study developed a novel serum metabolites model that could early separate stage-I PDAC from healthy controls.

Biomimetic Gold Nanorods Modified with Erythrocyte Membranes for Imaging-Guided Photothermal/Gene Synergistic Therapy

Pancreatic cancer (PC) is one of the most malignant cancers that develops rapidly and carries a poor prognosis. Synergistic cancer therapy strategy could enhance the clinical efficacy compared to either treatment alone. In this study, gold nanorods (AuNRs) were used as siRNA delivery vehicles to interfere with the oncogenes of KRAS. In addition, AuNRs were one of anisotropic nanomaterials that can absorb near-infrared (NIR) laser and achieve rapid photothermal therapy for malignant cancer cells. Modification of the erythrocyte membrane and antibody Plectin-1 occurred on the surface of the AuNRs, making them a promising target nanocarrier for enhancing antitumor effects. As a result, biomimetic nanoprobes presented advantages in biocompatibility, targeting capability, and drug-loading efficiency. Moreover, excellent antitumor effects have been achieved by synergistic photothermal/gene treatment. Therefore, our study would provide a general strategy to construct a multifunctional biomimetic theranostic multifunctional nanoplatform for preclinical studies of PC.

Qian Yang Yu Yin Granule Improves Renal Injury of Hypertension by Regulating Metabolic Reprogramming Mediated by HIF-1α/PKM2 Positive Feedback Loop

Protection against hypoxia injury is an important therapeutic strategy for treating hypertensive nephropathy. In this study, the effects of Qian Yang Yu Yin granule (QYYY) on spontaneously hypertensive rats fed with high salt diet and HEK293T cells exposed to hypoxia were investigated. After eight weeks' treatment of QYYY, blood pressure, serum creatinine, serum cystatin C, blood urea nitrogen, urinary β2-microglobulin, urinary N-acetyl-β-glucosaminidase, and urinary microalbumin were assessed. The changes of hypoxia-inducible factor-1α (HIF-1α), pyruvate kinase M2 (PKM2), glucose transport 1 (GLUT1), lactate dehydrogenase A (LDH-A), connective tissue growth factor (CTGF), transforming growth factor-β1 (TGF-β1), ATP, lactate, pyruvate, and pathology were also assessed in vivo. HEK293T cells pre-treated with QYYY and/or HIF-1α over expressing cells were cultured in a three gas hypoxic incubator chamber (5% CO2, 1% O2, 94% N2) for 12 h and then the expressions of HIF-1α, PKM2, GLUT1, LDH-A, CTGF, TGF-β1, ATP, lactate, and pyruvate were detected. Our results showed that QYYY promoted the indicators of renal inflammation and fibrosis mediated by HIF-1α/PKM2 positive feedback loop in vivo and vitro. Our findings indicated that QYYY treated hypertensive nephropathy by regulating metabolic reprogramming mediated by HIF-1α/PKM2 positive feedback loop.

[Contrast-enhanced CT and texture analysis of mass-forming pancreatitis and cancer in the pancreatic head]

Objective: To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods: A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results: The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%, +LR 17.43, -LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions: The CT findings model and the texture analysis model have equivalent diagnostic performance in the differentiation of mass-forming pancreatitis and pancreatic cancer. The enhanced CT combined with texture analysis model has the best diagnostic efficiency and can further improve the diagnostic ability.

Differentiation of duodenal gastrointestinal stromal tumors from hypervascular pancreatic neuroendocrine tumors in the pancreatic head using contrast-enhanced computed tomography

PURPOSE

To determine useful contrast-enhanced computed tomography (CE-CT) features in differentiating duodenal gastrointestinal stromal tumors (duodenal GISTs) from hypervascular pancreatic neuroendocrine tumors in the pancreatic head (pancreatic head NETs).

METHODS

Seventeen patients with pathologically confirmed duodenal GISTs and 25 with pancreatic NETs underwent preoperative CE-CT. CT image analysis included tumor size, morphology, and contrast enhancement. Receiver operating characteristic curves were performed, and cutoff values were calculated to determine CT findings with high sensitivity and specificity.

RESULTS

CT imaging showed duodenal GISTs with higher frequencies of tumor central location close to the duodenum and a predominantly solid tumor type when compared with pancreatic head NETs (p < 0.05 for both). Duodenal GISTs were larger than pancreatic head NETs (3.3 ± 0.9 cm vs. 2.5 ± 1.1 cm, p = 0.03). Duodenal GISTs had significantly lower CT attenuation values (112.9 ± 17.9HU vs. 137.4 ± 32.1HU, p < 0.01) at the arterial phase and higher CT attenuation values at the delayed phase (94.3 ± 7.9HU vs. 84.9 ± 10.4HU, p < 0.01) when compared with pancreatic head NETs. A CT attenuation value of ≤ 135 HU at the arterial phase (30 s) was 76% sensitive, 94.1% specific, and 83.3% accurate for the diagnosis of duodenal GISTs, while a CT attenuation value of ≥ 89.5 HU at the delayed phase (120 s) was 93.3% sensitive, 81.8% specific, and 76.2% accurate for the diagnosis of duodenal GISTs.

CONCLUSION

Tumor central location, size, texture, and contrast enhancement are valuable characteristics for the differentiation between duodenal GISTs and hypervascular pancreatic head NETs during preoperative examination.

The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma

BACKGROUND

Diagnostic type II endometrial carcinoma (EC) is considered more aggressive and has a poorer prognosis than type I EC; differentiation between them is helpful for preoperative clinical decision-making. However, the diagnostic value of the apparent diffusion coefficient (ADC) in differentiating them remains unclear.

PURPOSE

To investigate the value of ADC in differentiating type II EC from type I EC.

MATERIAL AND METHODS

Ninety-four patients with EC who underwent diffusion-weighted imaging (DWI) were retrospectively included and divided into type I and type II subgroups, based on the postoperative pathologic results. We analyzed the clinical characteristics, conventional magnetic resonance imaging manifestations, and ADC mean values (ADCmean), ADC minimum values (ADCmin), and ADC max values (ADCmax). Receiver operating characteristic (ROC) curve analysis was further used to assess the predictive performance.

RESULTS

The ADCmean, ADCmin, and tumor size differed significantly between the two subtypes. The area under the ROC curve (AUC) for ADCmean and ADCmin was 0.787 (95% confidence interval [CI] = 0.692-0.88) and 0.835 (95% CI = 0.751-0.919) for predicting type II EC, respectively. The optimal cut-off value of ADCmean for prediction was 0.757 × 10-3 mm2/s with a sensitivity of 91%, a specificity of 58%, and an accuracy of 74%, while for ADCmin was 0.637 × 10-3 mm2/s with a sensitivity of 82%, a specificity of 73%, and an accuracy of 75%.

CONCLUSION

EC with lower ADCmean and ADCmin values derived from DWI, and a larger size, are indicative of type II EC.

Differentiation between renal oncocytomas and chromophobe renal cell carcinomas using dynamic contrast-enhanced computed tomography

PURPOSE

To evaluate the ability of inferior vena cava-lesion-attenuation-difference (ILAD) and lesion-cortex-attenuation-ratio (LCAR) to differentiate renal oncocytomas (RO) from chromophobe renal cell carcinomas (chRCC).

METHODS

Retrospective study with analysis of 84 cases of chRCC and 30 cases of RO confirmed by surgical pathology. ILAD was calculated by measuring the difference in Hounsfield units (HU) between the inferior vena cava and the lesion of interest on the same image slice on preoperative CT scan. Calculating LCAR using the CT attenuation ratio of lesion to renal cortex at the same image slice. Receiver operating characteristic (ROC) curves were plotted to analyze the diagnostic values of ILAD and LCAR for disease activity.

RESULTS

There were no statistically significant differences in demographic and lesion characteristics between patients with chRCC and RO (p > 0.05). ILAD has significant statistical differences in the identification of RO and chRCC in the arterial (p = 0.031), venous (p = 0.047), and delayed phase (p = 0.002). And LCAR showed a statistically significant difference between two lesions during the arterial (p = 0.043), venous (p = 0.026), and delayed phase (p = 0.008). When all significant variables were used in combination to build a predicting model (Mix), the AUC was 0.871 (95% CI 0.759-0.984) with 67.9% sensitivity and 100% specificity.

CONCLUSION

ILAD and LCAR at the arterial phase, venous phase and delayed phase were shown to be useful CT attenuation parameter in discriminating RO from chRCC when histologic evaluation on biopsy is indeterminate.