Publications by authors named "Fatian Wu"
Article Synopsis
- The study emphasizes that intra-arterial therapies (IATs) are important for treating unresectable hepatocellular carcinoma (HCC) and highlights the need for prognostic risk stratification before applying these treatments.
- A machine learning-based decision support model (MLDSM) was developed using data from 2,959 HCC patients to recommend IAT options, employing five different ML algorithms to analyze various clinical variables.
- The results revealed critical insights, with algorithms like CatBoost and LGBM showing strong predictive ability, and identifying key clinical factors such as BCLC grade and local therapy that could assist in making informed treatment decisions.
Background: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP).
View Article and Find Full Text PDFObjective: To investigate the efficacy of contrast-enhanced computed tomography (CECT)-based radiomics signatures for preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning.
Methods: In this single-center retrospective study, data collected from 297 consecutive subjects with HCC were allocated to training dataset (n = 237) and test dataset (n = 60). Manual segmentation of lesion sites was performed with ITK-SNAP, the radiomics features were extracted by the Pyradiomics, and radiomics signatures were synthesized using recursive feature elimination (RFE) method.