首页|First Affiliated Hospital of Nanjing Medical University Reports Findings in Glio mas (T2-FLAIR mismatch sign and machine learningbased multiparametric MRI radio mics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas)
First Affiliated Hospital of Nanjing Medical University Reports Findings in Glio mas (T2-FLAIR mismatch sign and machine learningbased multiparametric MRI radio mics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Gliomas is the subject of a report. According to news reporting from Nanjing, People's Repu blic of China, by NewsRx journalists, research stated, "To investigate the appli cation of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) misma tch sign and machine learning-based multiparametric magnetic resonance imaging ( MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs ). One hundred and forty-six patients, who had pathologically confirmed isocitra te dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3." The news correspondents obtained a quote from the research from the First Affili ated Hospital of Nanjing Medical University, "The T2-FLAIR mismatch sign and con ventional MRI features were evaluated. Radiomics features extracted from T1-weig hted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coeff icient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displ ayed the best performance of each sequence were selected, and their predicted va lues as well as the T2-FLAIR mismatch sign data were collected to establish a fi nal stacking model. Receiver operating characteristic curve (ROC) analyses and a rea under the curve (AUC) values were applied to evaluate and compare the perfor mance of the models. The T2-FLAIR mismatch sign was more common in the IDH mutan t 1p/19q non-co-deleted group (p <0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and a ccuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort."
NanjingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesGliomasHealth and MedicineMachine Learnin gOncology
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