Publications by authors named "Tingliang Cao"
Article Synopsis
- The study aimed to identify imaging biomarkers that can predict intratumoral haemorrhage in patients undergoing elective stereotactic biopsy for intracranial lesions.
- 143 patients with 175 lesions were analyzed, using a machine learning approach to extract and evaluate radiomics features from MRI images to distinguish between "hemorrhage-prone tumors" and others.
- The final model demonstrated high predictive accuracy, potentially aiding in biopsy planning and lesion selection, with a notable importance placed on the feature "T2_gradient_firstorder_10Percentile."
Background: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage.
Methods: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014.