首页|University Medical Center Utrecht Reports Findings in Pancreatic Neoplasms (DNA methylation profiling enables accurate classification of non-ductal primary panc reatic neoplasms)
University Medical Center Utrecht Reports Findings in Pancreatic Neoplasms (DNA methylation profiling enables accurate classification of non-ductal primary panc reatic neoplasms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Digestive System Disea ses and Conditions - Pancreatic Neoplasms is the subject of a report. According to news reporting originating in Utrecht, Netherlands, by NewsRx journalists, re search stated, "Cytological and histopathological diagnosis of non-ductal pancre atic neoplasms can be challenging in daily clinical practice while it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagn ostic tool in other cancer entities." The news reporters obtained a quote from the research from University Medical Ce nter Utrecht, "Here, we investigate if methylation profiling can improve the dia gnostic work-up of pancreatic neoplasms. DNA methylation data were obtained for 301 primary tumors spanning six primary pancreatic neoplasms and 20 normal pancr eas controls. Neural Network, Random Forest, and XGBoost machine learning models were trained to distinguish between tumor types. Methylation data of 29 non-pan creatic neoplasms (n = 3708) were used to develop an algorithm capable of detect ing neoplasms of non-pancreatic origin. After benchmarking three state-of-the-ar t machine learning models, the Random Forest model emerged as the best classifie r with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshol d, improved the Random Forest classifier performance up to 100% wi th 87% of samples with scores surpassing the cutoff. Using a logis tic regression model, detection of non-pancreatic neoplasms achieved an area und er the curve (AUC) of > 0.99. Analysis of biopsy specime ns showed concordant classification with their paired resection sample. Pancreat ic neoplasms can be classified with high accuracy based on DNA methylation signa tures. Additionally, non-pancreatic neoplasms are identified with near perfect p recision."
UtrechtNetherlandsEuropeCyborgsD igestive System Diseases and ConditionsDigestive System NeoplasmsEmerging Te chnologiesEndocrine Gland NeoplasmsEndocrine System Diseases and ConditionsGastroenterologyGeneticsHealth and MedicineMachine LearningNeoplasmsP ancreasPancreatic Diseases and ConditionsPancreatic Neoplasms
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