首页|Third Affiliated Hospital of Southern Medical University Reports Findings in Men ingeal Neoplasms (Combined radiomics nomogram of different machine learning mode ls for preoperative distinguishing intraspinal schwannomas and meningiomas: a .. .)
Third Affiliated Hospital of Southern Medical University Reports Findings in Men ingeal Neoplasms (Combined radiomics nomogram of different machine learning mode ls for preoperative distinguishing intraspinal schwannomas and meningiomas: a .. .)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions-Meningeal Neoplasms is the subject of a report. Accor ding to news originating from Guangzhou, People's Republic of China, by NewsRx c orrespondents, research stated, "The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imagin g (MRI) radiomics and clinical features to distinguish intraspinal schwannomas f rom meningiomas. This research analyzed the preoperative magnetic resonance (MR) images and clinical characteristics of 209 patients with intraspinal tumors who received tumor resection at three institutions. 159 individuals from institutio ns 1 and 2 were randomly assigned into a training group (n=111) and a test group (n=48) in a 7-3 ratio." Our news journalists obtained a quote from the research from the Third Affiliate d Hospital of Southern Medical University, "A nomogram was constructed using the training cohort and was internally and externally verified in the test cohort a nd an independent validation cohort (n=50). Model performance was assessed utili zing the area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration curves. The nomogram exhibited s uperior predictive efficacy in distinguishing between spinal schwannomas and men ingiomas when compared to both the radiomics model and the clinical model. The n omogram yielded AUCs of 0.994, 0.962, and 0.949 in the training, test, and exter nal validation cohorts, respectively, indicating its exceptional differentiating ability. The DCAs demonstrated that the nomogram yielded the best net benefit. The calibration curves indicated that the nomogram got good agreement between th e predicted and the actual observation."
GuangzhouPeople's Republic of ChinaA siaCentral Nervous System Diseases and ConditionsCentral Nervous System Neop lasmsCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMe ningeal NeoplasmsMeningiomaNervous System Diseases and ConditionsNervous S ystem NeoplasmsNeurilemmomaSchwannoma
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