首页|Southern Medical University Reports Findings in Gliomas (Prediction of Glioma en hancement pattern using a MRI radiomics-based model)
Southern Medical University Reports Findings in Gliomas (Prediction of Glioma en hancement pattern using a MRI radiomics-based model)
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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 originating in Guangzhou, P eople’s Republic of China, by NewsRx journalists, research stated, “Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate predic tion of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients.” The news reporters obtained a quote from the research from Southern Medical Univ ersity, “This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologicallyproven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid att enuated inversion recovery images, which were divided into enhancing and non-enh ancing groups. Predictive radiomics models based on machine learning with 6 diff erent classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation coho rt (n = 99). Receiver-operator characteristic curve was used to assess the predi ctive performance of these radiomics models. This study demonstrated that the ra diomics model comprising of 15 features using the Gaussian process as a classifi er had the highest predictive performance in both the training cohort and the in ternal validation cohort, with the area under the curve being 0.88 and 0.80, res pectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.8 2, 0.76 and 0.96, respectively, in the external validation cohort.”
GuangzhouPeople’s Republic of ChinaA siaCyborgsEmerging TechnologiesGliomasHealth and MedicineMachine Learn ingOncology
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