首页|Fourth Hospital of Hebei Medical University Reports Findings in Support Vector M achines (Radiomics to predict PNI in ESCC)
Fourth Hospital of Hebei Medical University Reports Findings in Support Vector M achines (Radiomics to predict PNI in ESCC)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Support Vector Machine s is the subject of a report. According to news originating from Shijiazhuang, P eople's Republic of China, by NewsRx correspondents, research stated, "This stud y aimed to investigate whether contrast-enhanced computed tomography (CECT) base d radiomics analysis could noninvasively predict the perineural invasion (PNI) i n esophageal squamous cell carcinoma (ESCC). 398 patients with ESCC who underwen t resection between February 2016 and March 2020 were retrospectively enrolled i n this study." Our news journalists obtained a quote from the research from the Fourth Hospital of Hebei Medical University, "Patients were randomly divided into training and testing cohorts in a 7:3 ratio. Radiomics analysis was performed on the arterial phase images of CECT scans. From these images, 1595 radiomics features were ini tially extracted. The intraclass correlation coefficient (ICC), wilcoxon rank-su m test, spearman correlation analysis, and boruta algorithm were used for featur e selection. Logistic regression (LR), random forest (RF), and support vector ma chine (SVM) models were established to preidict the PNI status. The performance of these radiomics models was assessed by the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was conducted to evalu ate their clinical utility. Six radiomics features were retained to build the ra diomics models. Among these models, the random forest (RF) model demonstrated su perior performance. In the training cohort, the AUC value of the RF model was 0. 773, compared to 0.627 for the logistic regression (LR) model and 0.712 for the support vector machine (SVM) model. Similarly, in the testing cohort, the RF mod el achieved an AUC value of 0.767, outperforming the LR model at 0.638 and the S VM model at 0.683. Decision curve analysis (DCA) suggested that the RF radiomics model exhibited the highest clinical utility. CECT-based radiomics analysis, pa rticularly utilizing the RF, can noninvasively predict the PNI in ESCC preoperat ively."
ShijiazhuangPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
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