Radiomics and Habitat Analysis Model for Predicting HCC Recurrence After Liver Transplant

Ya Yang developed a combined radiomics and habitat analysis model for predicting early recurrence of hepatocellular carcinoma (HCC) after liver transplantation. The model was trained and tested on a retrospective cohort of 140 HCC patients using arterial-phase CECT images and clinical data. The dataset was last updated on 2026-05-26.

Use Cases

• Training machine learning models for cancer recurrence prediction based on radiomic features.
• Comparing the performance of combined radiomics-clinical models against single-modality models.
• Validating habitat analysis techniques for tumor sub-region characterization in medical imaging.

Strengths

• Model performance metrics are provided, including an AUC of 0.929 in the training cohort.
• The dataset is openly available under a CC-BY-4.0 license.
• The study includes a defined cohort of 140 HCC patients.

Limitations

• The dataset is very small at 17.0 KB, suggesting limited raw data or a model description document.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for direct data analysis.

Provenance

Source

figshare, author Ya Yang.

Collection Method

Retrospective analysis of clinical data and CECT images from 140 HCC patients.

Freshness

Last updated 2026-05-26 04:39:17; freshness should be verified.