Blood-Based Diagnosis and Risk Stratification of Patients with Pancreatic Intraductal Papillary Mucinous Neoplasm (IPMN)

Abstract

Purpose: Intraductal papillary mucinous neoplasm (IPMN) is a precursor of pancreatic ductal adenocarcinoma. Low-grade dysplasia has a relatively good prognosis, whereas high-grade dysplasia and IPMN invasive carcinoma require surgical intervention. However, diagnostic distinction is difficult. We aimed to identify biomarkers in peripheral blood for accurate discrimination. Experimental Design: Sera were obtained from 302 patients with IPMNs and 88 healthy donors. For protein biomarkers, serum samples were analyzed on microarrays made of 2,977 antibodies. A support vector machine (SVM) algorithm was applied to define classifiers, which were validated on a separate sample set. For microRNA biomarkers, a PCR-based screen was performed for discovery. Biomarker candidates confirmed by quantitative PCR were used to train SVM classifiers, followed by validation in a different sample set. Finally, a combined SVM classifier was established entirely independent of the earlier analyses, again using different samples for training and validation. Results: Panels of 26 proteins or seven microRNAs could distinguish high- and low-risk IPMN with an area under the curve (AUC) value of 95% and 94%, respectively. Upon combination, a panel of five proteins and three miRNAs yielded an AUC of 97%. These values were much better than those obtained in the same patient cohort by using the guideline criteria for discrimination. In addition, accurate discrimination was achieved between other patient subgroups. Conclusions: Protein and microRNA biomarkers in blood allow precise diagnosis and risk stratification of IPMN cases, which should improve patient management and thus the prognosis of IPMN patients.

DNA-methylation signature accurately differentiates pancreatic cancer from chronic pancreatitis in tissue and plasma

“…For improving accuracy and robustness further so that it could potentially be sufficient for clinical application, we used the methylation values of the six PRKCB CpG sites to train a support vector machine (SVM) classifier. 41 The data were randomly split into training and validation sets (each made of 4× PDAC and 4× CP). During the training process, a combination of three parameters (cost, gamma, epsilon) was optimised to avoid overfitting and underfitting.…”

Comprehensive Radiomics Analysis for Predicting Benign and Malignant Intraductal Papillary Mucinous Neoplasms (IPMNs) of the Pancreas: Integrating Clinical and Imaging Data

“…Serum CA19-9 levels are associated with malignant IPMN but demonstrate limited diagnostic capability 14 , with reported AUC values ranging from 0.62 to 0.78. Combining serum CEA and CA19-9 concentrations with anti-p53 antibody detection can distinguish high-grade dysplasia and malignant IPMN from low-grade IPMN, albeit with a sensitivity of only 38.4% and specificity of 81.6% 15 .Recent studies suggest that combining DNA methylation biomarkers can further enhance diagnostic accuracy, while protein and microRNA biomarkers in blood can facilitate precise diagnosis and risk stratification of IPMN cases 16,17 .…”

Non-Contrasted CT Radiomics for SAH Prognosis Prediction

“…The utility of SVM becomes particularly evident in scenarios where the underlying patterns are intricate and necessitate sophisticated computational methodologies for accurate analysis and prediction. SVM finds extensive applications in disease diagnosis, prognosis assessment, and biomarker research [ 31 , 32 , 33 , 34 ]. Model_SVM effectively distinguished SAH patients with different prognoses in both the training and testing cohorts.…”