首页|Department of Gastroenterology Reports Findings in Liver Metastasis (Prognostica tion of colorectal cancer liver metastasis by CEbased radiomics and machine lea rning)
Department of Gastroenterology Reports Findings in Liver Metastasis (Prognostica tion of colorectal cancer liver metastasis by CEbased radiomics and machine lea rning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Oncology - Liver Metastasis is th e subject of a report. According to news reporting originating from Guangzhou, P eople’s Republic of China, by NewsRx correspondents, research stated, “The liver is the most common organ for the formation of colorectal cancer metastasis. Non -invasive prognostication of colorectal cancer liver metastasis (CRLM) may bette r inform clinicians for decision-making.” Our news editors obtained a quote from the research from the Department of Gastr oenterology, “Contrast-enhanced computed tomography images of 180 CRLM cases wer e included in the final analyses. Radiomics features, including shape, first-ord er, wavelet, and texture, were extracted with Pyradiomics, followed by feature e ngineering by penalized Cox regression. Radiomics signatures were constructed fo r disease-free survival (DFS) by both elastic net (EN) and random survival fores t (RSF) algorithms. The prognostic potential of the radiomics signatures was dem onstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics f eatures were selected for prognostic modelling for the EN algorithm, with 835 fe atures for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm succe ssfully separates DFS of high-risk and lowrisk cases in the training dataset (l og-rank test: p<0.01, hazard ratio: 1.45 (1.07-1.96), p<0.01) and test dataset (hazard ratio: 1.89 (1.17-3.04), p<0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p<0.001, hazard rati o: 2.54 (1.80-3.61), p <0.0001) and test dataset (log-rank test: p<0.05, hazard ratio: 1.84 (1.15-2.96), p<0.05).”
GuangzhouPeople’s Republic of ChinaA siaAlgorithmsCancerColon CancerColorectal ResearchCyborgsEmerging Te chnologiesGastroenterologyHealth and MedicineHepatologyLiver MetastasisMachine LearningOncology
NETL
NSTL
