首页|Cancer Hospital Reports Findings in Liver Cancer (Radiomics and machine learning based on preoperative MRI for predicting extra- hepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization)
Cancer Hospital Reports Findings in Liver Cancer (Radiomics and machine learning based on preoperative MRI for predicting extra- hepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization)
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New research on Oncology - Liver Cancer is the subject of a report. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment. A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio." The news correspondents obtained a quote from the research from Cancer Hospital, "Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis. In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk."
BeijingPeople's Republic of ChinaAsiaCancerCarcino- masChemoembolizationCyborgsEmerging TechnologiesHealth and MedicineLiver CancerMachine LearningOncology
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