首页|First Affiliated Hospital of Jinan University Reports Findings in Liver Cancer (An interpretable machine learning model based on contrastenhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization ...)
First Affiliated Hospital of Jinan University Reports Findings in Liver Cancer (An interpretable machine learning model based on contrastenhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization ...)
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New research on Oncology - Liver Cancer is the subject of a report. According to news reporting originating from Guangdong, People’s Republic of China, by NewsRx correspondents, research stated, “To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers.” Our news editors obtained a quote from the research from the First Affiliated Hospital of Jinan University, “We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P＜0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001).”
GuangdongPeople’s Republic of ChinaAsiaCancerCarcinomasChemoembolizationCyborgsEmerging TechnologiesHealth and MedicineLiver CancerMachine LearningOncology
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