首页|Data on Fibroma Reported by Jun-Ru Zhao and Colleagues (CTbased radiomics analy sis of different machine learning models for differentiating gnathic fibrous dys plasia and ossifying fibroma)
Data on Fibroma Reported by Jun-Ru Zhao and Colleagues (CTbased radiomics analy sis of different machine learning models for differentiating gnathic fibrous dys plasia and ossifying fibroma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Fibroma is the subject of a report. According to news reporting from Beijing, People’s Republic of Chi na, by NewsRx journalists, research stated, “In this study, our aim was to devel op and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery . We enrolled 220 patients with confirmed FD or OF.” The news correspondents obtained a quote from the research, “We extracted radiom ic features from nonenhanced CT images. Following dimensionality reduction and f eature selection, we constructed radiomic models using logistic regression, supp ort vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver ope rating characteristic (ROC) curve analysis. After combining radiomics features w ith clinical features, we developed a comprehensive model. ROC curve and decisio n curve analysis (DCA) demonstrated the models’ robustness and clinical value. W e extracted 1834 radiomic features from CT images, reduced them to eight valuabl e features, and achieved high predictive efficiency, with area under curves (AUC ) exceeding 0.95 for all the models. Ultimately, our combined model, which integ rates radiomic and clinical data, displayed superior discriminatory ability (AUC : training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinica l efficacy.”
BeijingPeople’s Republic of ChinaAsi aCyborgsDermatologyDysplasiaEmerging TechnologiesFibromaFibrous Dysp lasiaHealth and MedicineMachine LearningOssifying Fibroma
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