Publications by authors named "T Wake"
Article Synopsis
- Radiofrequency ablation (RFA) is a minimally invasive treatment for hepatocellular carcinoma (HCC), and predicting patient outcomes after RFA is crucial for managing cancer risk.
- This study focused on creating a transformer-based machine learning model to forecast overall survival rates in HCC patients treated by RFA, comparing its effectiveness with traditional deep learning models.
- Results showed that the transformer model outperformed the deep learning model in predicting survival (Harrel's c-index 0.69 vs 0.60), allowing for better risk stratification and personalized predictions for individual patients.
Aim: Radiofrequency ablation (RFA) is regarded as a first-line treatment for hepatocellular carcinoma (HCC) at an early stage. When treated with RFA, tumor biopsy may not be performed due to the risk of neoplastic seeding. We previously revealed that the risk of neoplastic seeding is significantly reduced by performing biopsies after RFA.
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- The study aimed to assess how steroids impact ischemic complications following radiofrequency ablation.
- It involved 58 patients divided into two groups based on whether they received corticosteroids or not.
- Results showed that patients who took steroids experienced a significantly shorter fever duration, suggesting that steroids might help decrease severe outcomes by mitigating systemic inflammation.
The prognostic impact of direct-acting antivirals (DAAs) on patients with hepatitis C-related hepatocellular carcinoma (C-HCC) is still unclear. This study aimed to evaluate the prognosis of C-HCC in the DAA era. We enrolled 1237 consecutive patients with treatment-naive C-HCC who underwent radical radiofrequency ablation between 1999 and 2019.
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