首页|Qilu Hospital of Shandong University Reports Findings in Pancreatic Cancer (Prel iminary study on the ability of the machine learning models based on 18F-FDG PET /CT to differentiate between massforming pancreatic lymphoma and pancreatic ... )
Qilu Hospital of Shandong University Reports Findings in Pancreatic Cancer (Prel iminary study on the ability of the machine learning models based on 18F-FDG PET /CT to differentiate between massforming pancreatic lymphoma and pancreatic ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Pancreatic Cancer is the subject of a report. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "T he objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tom ography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati c lymphoma from pancreatic carcinoma using machine learning. A total of 88 lesio ns from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validatio n set at a 4-to-1 ratio." Our news editors obtained a quote from the research from the Qilu Hospital of Sh andong University, "The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracte d using 3Dslicer and PYTHON. Following the selection of optimal metabolic parame ters and radiomics features, Logistic regression (LR), support vector machine (S VM), and random forest (RF) models were constructed for PET metabolic parameters , CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was asses sed in terms of area under the curve (AUC), accuracy, sensitivity, and specifici ty in both the training and validation sets. Strong discriminative ability obser ved in all models, with AUC values ranging from 0.727 to 0.978. The highest perf ormance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844. Machine le arning models utilizing the metabolic parameters and radiomics of F-FDG PET/CT s how promise in distinguishing between pancreatic carcinoma and mass-forming panc reatic lymphoma."
JinanPeople's Republic of ChinaAsiaCancerCarcinomasCyborgsEmerging TechnologiesGastroenterologyHealth an d MedicineHematologyImmunoproliferative DisordersLymphatic Diseases and Co nditionsLymphomaLymphoproliferative DisordersMachine LearningOncologyP ancreasPancreatic Cancer
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