machine learning

Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension

Abstract Purpose Portal venous pressure (PVP) measurement is of clinical significance, especially in patients with portal hypertension. However, the invasive nature and associated complications limits its application. The aim of the study is to propose a noninvasive predictive model of PVP values based on CT-extracted radiomic features. Methods Radiomics PVP (rPVP) models based on liver, spleen and combined features were established on an experimental cohort of 169 subjects. Radiomics features were extracted from each ROI and reduced via the LASSO regression to achieve an optimal predictive formula.