Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning
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ABSTRACT:
SUBMITTER: Nakamura H
PROVIDER: S-EPMC10908399 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Project description:PurposeThis study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.MethodsRetrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. Feature selection was conducted via variance filtering and LASSO regression, followed by the construction of a random forest (RF) model. The hyperparameters were optimized in the training cohort, and the final model was evaluated in the external validation cohorts. The model's performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), the F1 score, accuracy, and area under curve(AUC). The clinical application value of the model was clarified through a comparison between humans and machines.ResultsAn RF model was constructed using six features: Ca 19-9 level, abdominal pain, jaundice, focal/diffuse-type AIP, blood flow signals, and morphology. In external validation cohort 1, the model's sensitivity, specificity, PPV, NPV, F1 score, accuracy, and AUC were 86.0%, 80.0%, 81.0%, 86.0%, 84.0%, 83.0%, and 89.0%, respectively; in external validation cohort 2, these values were 72.0%, 94.0%, 93.0%, 77.0%, 81.0%, 83.0%, and 91.0%, respectively. The predictive performance of experienced radiologists using clinical information and ultrasound images demonstrated a sensitivity of 81%, specificity of 79%, PPV of 78%, NPV of 76%, F1 score of 80%, and accuracy of 80%. For radiologists with intermediate experience, the sensitivity was 75%, specificity was 74%, PPV was 73%, NPV was 76%, F1 score was 74%, and accuracy was 75%. less experienced radiologist had a sensitivity of 55%, specificity of 56%, PPV of 62%, NPV of 49%, F1 score of 58%, and accuracy of 55%.ConclusionThe RF model constructed using clinical ultrasound features achieved diagnostic levels comparable to those of experienced radiologists and can assist in differentiating AIP from PDCA, potentially guiding clinical practice.
| S-EPMC11879800 | biostudies-literature
Project description:BackgroundMisdiagnosis of autoimmune pancreatitis (AIP) as pancreatic cancer (PDAC) or vice versa can cause dismal patents' outcomes. Changes in IgG glycosylation are associated with cancers and autoimmune diseases. This study investigated the IgG glycosylation profiles as diagnostic and prognostic biomarkers in PDAC and AIP.MethodsSerum IgG-glycosylation profiles from 86 AIP patients, 115 PDAC patients, and 57 controls were analyzed using liquid chromatography-electrospray ionization mass spectrometry. Classification and regression tree (CART) analysis was applied to build a decision tree for discriminating PDAC from AIP. The result was validated in an independent cohort.ResultsCompared with AIP patients and controls, PDAC patients had significantly higher agalactosylation, lower fucosylation, and sialylation of IgG1, a higher agalactosylation ratio of IgG1 and a higher agalactosylation ratio of IgG2. AIP patients had significantly higher fucosylation of IgG1 and a higher sialylation ratio of IgG subclasses 1, 2 and 4. Using the CART analysis of agalactosylation and sialylation ratios in the IgG to discriminate AIP from PDAC, the diagnostic accuracy of the glycan markers was 93.8% with 94.6% sensitivity and 92.9% specificity. There were no statistically significant difference of IgG-glycosylation profiles between diffuse type and focal type AIP.ConclusionsAIP and PDAC patients have distinct IgG-glycosylation profilings. IgG-glycosylation could different PDAC from AIP with high accuracy.
| S-EPMC6317216 | biostudies-literature
Project description:BackgroundPatients with mass-forming pancreatitis (MFP) or pancreatic ductal adenocarcinoma (PDAC) presented similar clinical symptoms, but required different treatment approaches and had different survival outcomes. This meta-analysis aimed to compare the diagnostic performance of contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT) in differentiating MFP from PDAC.MethodsA literature search was performed in the PubMed, EMBASE (Ovid), Cochrane Library (CENTRAL), China National Knowledge Infrastructure (CNKI), Weipu (VIP), and WanFang databases to identify original studies published from inception to August 20, 2021. Studies reporting the diagnostic performances of CEUS and CECT for differentiating MFP from PDAC were included. The meta-analysis was performed with Stata 15.0 software. The outcomes included the pooled sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves of CEUS and CECT. Meta-regression was conducted to investigate heterogeneity. Bayesian network meta-analysis was conducted to indirectly compare the overall diagnostic performance.ResultsTwenty-six studies with 2115 pancreatic masses were included. The pooled sensitivity and specificity of CEUS for MFP were 82% (95% confidence interval [CI], 73%-88%; I2 = 0.00%) and 95% (95% CI, 90%-97%; I2 = 63.44%), respectively; the overall +LR, -LR, and DOR values were 15.12 (95% CI, 7.61-30.01), 0.19 (95% CI, 0.13-0.29), and 78.91 (95% CI, 30.94-201.27), respectively; and the area under the SROC curve (AUC) was 0.90 (95% CI, 0.87-92). However, the overall sensitivity and specificity of CECT were 81% (95% CI, 75-85%; I2 = 66.37%) and 94% (95% CI, 90-96%; I2 = 74.87%); the overall +LR, -LR, and DOR values were 12.91 (95% CI, 7.86-21.20), 0.21 (95% CI, 0.16-0.27), and 62.53 (95% CI, 34.45-113.51), respectively; and, the SROC AUC was 0.92 (95% CI, 0.90-0.94). The overall diagnostic accuracy of CEUS was comparable to that of CECT for the differential diagnosis of MFP and PDAC (relative DOR 1.26, 95% CI [0.42-3.83], P > 0.05).ConclusionsCEUS and CECT have comparable diagnostic performance for differentiating MFP from PDAC, and should be considered as mutually complementary diagnostic tools for suspected focal pancreatic lesions.
| S-EPMC10476799 | biostudies-literature
Project description:BackgroundDiscriminating between autoimmune pancreatitis (AIP), chronic pancreatitis (CP), and pancreatic ductal adenocarcinoma (PDAC) can be challenging. In this retrospective study, levels of serum and tissue cytokines were analyzed as part of the clinical strategy for the preoperative differentiation between AIP and PDAC. The identification of differential cytokine profiles may help to prevent unnecessary surgical resection and allow optimal treatment of these pathologies.MethodsTo compare the cytokine profiles of AIP, CP, and PDAC patients, serum and pancreatic tissue homogenates were subjected to multiplex analysis of 17 inflammatory mediators. In total, serum from 73 patients, composed of 29 AIP (14 AIP-1 and 15 AIP-2), 17 CP, and 27 PDAC, and pancreatic tissue from 36 patients, including 12 AIP (six AIP-1 and six AIP-2), 12 CP, and 12 PDAC, were analyzed.ResultsComparing AIP and PDAC patients' serum, significantly higher concentrations were found in AIP for interleukins IL-1β, IL-7, IL-13, and granulocyte colony-stimulating factor (G-CSF). G-CSF also allowed discrimination of AIP from CP. Furthermore, once AIP was divided into subtypes, significantly higher serum levels for IL-7 and G-CSF were measured in both subtypes of AIP and in AIP-2 for IL-1β when compared to PDAC. G-CSF and TNF-α were also significantly differentially expressed in tissue homogenates between AIP-2 and PDAC.ConclusionsThe cytokines IL-1β, IL-7, and G-CSF can be routinely measured in patients' serum, providing an elegant and non-invasive approach for differential diagnosis. G-CSF is a good candidate to supplement the currently known serum markers in predictive tests for AIP and represents a basis for a combined blood test to differentiate AIP and particularly AIP-2 from PDAC, enhancing the possibility of appropriate treatment.
| S-EPMC5457650 | biostudies-literature
Project description:BackgroundAccurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP.MethodsPatients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed.ResultsA total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts.ConclusionsThe findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.
| S-EPMC8889703 | biostudies-literature
Project description:Background18F-fluorodeoxyglucose positron emission tomography-computerized tomography (18F-FDG PET-CT) has demonstrated high sensitivity in the diagnosis of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC), while also exhibiting the ability to distinguish AIP from PDAC lesions. The objective of this investigation was to assess the efficacy of multiparametric 18F-FDG PET with serological examination for distinguishing focal AIP (f-AIP) from PDAC.MethodsA total of 127 patients (43 with f-AIP and 84 with PDAC) who received 18F-FDG PET-CT before treatment were retrospectively included in the cohort study conducted at two centers, Beijing Friendship Hospital and Chinese PLA General Hospital, from January 2015 to December 2021. The baseline characteristics and clinical data were collected. The metabolism parameters of 18F-FDG PET, including maximum standardized uptake value (SUVmax), tumor-to-normal liver SUV ratio (SUVR), mean SUV (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were evaluated. The area under the receiver operating characteristic (ROC) curve was used to evaluate the differential diagnostic efficacy. The diagnostic efficacy improvement was assessed through the integrated discriminatory improvement (IDI), net reclassification improvement (NRI), and DeLong test.ResultsSerum immunoglobulin G4 (IgG4) >280 mg/dL, carbohydrate antigen 19-9 (CA19-9) <85 U/mL, and metabolic parameters differed significantly between patients with f-AID and PDAC. The ROC curve analysis of MTV showed the highest differentiating diagnostic value [sensitivity =0.814, 95% confidence interval (CI): 0.661-0.911; specificity =0.893, 95% CI: 0.802-0.947; area under the curve (AUC) =0.890, 95% CI: 0.820-0.957]. The combined diagnostics model of serum IgG4 >280 mg/dL, CA19-9 <85 U/mL, and MTV resulted in the highest AUC of 0.991 (95% CI: 0.978-1.000; sensitivity =0.953, 95% CI: 0.829-0.992; specificity =0.964, 95% CI: 0.892-0.991).ConclusionsThe multiparameter diagnostic model based on 18F-FDG PET and serological examination has excellent clinical value in the differential diagnosis of f-AID and PDAC.
| S-EPMC10498237 | biostudies-literature
Project description:Immunological consequences of endoscopic ultrasound (EUS)-local thermal ablation (LTA) for pancreatic ductal adenocarcinoma (PDAC) have not been extensively assessed. We aimed to explore EUS-LTA effects on the systemic immune response in PDAC. Peripheral blood was collected from 10 treatment-naïve patients with borderline resectable and locally advanced PDAC, randomly allocated to Nab-paclitaxel plus Gemcitabine chemotherapy (CT-arm, n = 5) or EUS-LTA with HybridTherm Probe plus CT (HTP + CT-arm, n = 5). Twenty healthy donors were included as controls. Flow-cytometry and multiplex assays were used to profile immune cell subsets and measure serum cytokines/chemokines, respectively. At baseline, PDAC patients showed increased circulating monocytes and lower circulating lymphocytes and CD19+ B cells counts compared to healthy controls. After 4 months, CT induced decrease of B regulatory cells, CD4+ cytotoxic T cells and IL-1β. The addition of EUS-HTP to CT selectively decreased the serum levels of APRIL/TNFSF13 as well as T regulatory cells, total, classic and inflammatory monocytes. Serum levels of APRIL/TNFSF13 and total, classic and inflammatory monocytes counts at baseline were associated with worse overall survival. EUS-HTP has the potential to selectively impact on immune cells and cytokines associated with poor outcomes in PDAC.
| S-EPMC10378428 | biostudies-literature
Project description:ObjectivesThe purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.Methods96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.ResultsA total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.ConclusionsThe CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.
| S-EPMC10014827 | biostudies-literature
Project description:BackgroundThe fecal microbiota in pancreatic ductal adenocarcinoma (PDAC) and in autoimmune pancreatitis (AIP) patients remains largely unknown. We aimed to characterize the fecal microbiota in patients with PDAC and AIP, and explore the possibility of fecal microbial biomarkers for distinguishing PDAC and AIP.Methods32 patients with PDAC, 32 patients with AIP and 32 age- and sex-matched healthy controls (HC) were recruited and the fecal microbiotas were analyzed through high-throughput metagenomic sequencing. Alterations of fecal short-chain fatty acids were measured using gas chromatographic method.ResultsPrincipal coordinate analysis (PCoA) revealed that microbial compositions differed significantly between PDAC and HC samples; whereas, AIP and HC individuals tended to cluster together. Significant reduction of phylum Firmicutes (especially butyrate-producing bacteria, including Eubacterium rectale, Faecalibacterium prausnitzii and Roseburia intestinalis) and significant increase of phylum Proteobacteria (especially Gammaproteobacteria) were observed only among PDAC samples. At species level, when compared with HC samples, we revealed 24 and 12 differently enriched bacteria in PDAC and AIP, respectively. Functional analysis showed a depletion of short-chain fatty acids synthesis associated KO modules (e.g. Wood-Ljungdahl pathway) and an increase of KO modules associated with bacterial virulence (e.g. type II general secretion pathway). Consistent with the downregulation of butyrate-producing bacteria, gas chromatographic analysis showed fecal butyrate content was significantly decreased in PDAC group. Eubacterium rectale, Eubacterium ventrisum and Odoribacter splanchnicus were among the most important biomarkers in distinguishing PDAC from HC and from AIP individuals. Receiver Operating Characteristic analysis showed areas under the curve of 90.74% (95% confidence interval [CI] 86.47-100%), 88.89% (95% CI 73.49-100%), and 76.54% (95% CI 52.5-100%) for PDAC/HC, PDAC/AIP and AIP/HC, respectively.ConclusionsIn conclusion, alterations in fecal microbiota and butyrate of patients with PDAC suggest an underlying role of gut microbiota for the pathogenesis of PDAC. Fecal microbial and butyrate as potential biomarkers may facilitate to distinguish patients with PDAC from patients with AIP and HCs which worth further validation.
| S-EPMC8130326 | biostudies-literature
Project description:BackgroundIt is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.MethodsThis retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists' evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.ResultsThe areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists' evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.ConclusionsThe radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.
| S-EPMC8025779 | biostudies-literature