Abstract
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."
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