Supplementary file 1_Integrating pretreatment CT radiomics and circulating tumor cells usi

Supplementary file 1_Integrating pretreatment CT radiomics and circulating tumor cells using machine learning to predict survival in hepatocellular carcinoma.docx is a research document describing a multimodal prognostic model for advanced hepatocellular carcinoma. The model integrates clinical variables, CT radiomic features, and circulating tumor cell counts, developed by Yongzhong Li and last updated on 2026-05-19. It includes internal and external validation results, reporting a concordance index of 0.789 and AUCs for 1-, 2-, and 3-year overall survival.

Use Cases

• Predicting overall survival in advanced hepatocellular carcinoma based on integrated clinical, radiomic, and biomarker data.
• Comparing prognostic model performance, such as Clinical–Radiomic–CTC versus Clinical–Radiomic nomograms, using metrics like concordance index and AUC.
• Risk stratification for patients with advanced hepatocellular carcinoma to support clinical decision-making.
• Evaluating the impact of immunotherapy on prognosis in hepatocellular carcinoma patients.

Strengths

• The model integrates three distinct data modalities: clinical variables, CT radiomics, and circulating tumor cell counts.
• Model performance is validated internally and externally, with reported concordance index of 0.789 and AUCs for 1-, 2-, and 3-year OS.
• The document is licensed under CC-BY-4.0, facilitating open access and reuse.

Limitations

• The dataset is a 1.3 MB DOCX file; the underlying tabular or image data is not directly accessible.
• Row count and column-level documentation are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Yongzhong Li.

Collection Method

Pretreatment CT images and baseline clinical data were collected from patients with advanced HCC; radiomic features were extracted and machine learning pipelines were screened.

Freshness

Last updated 2026-05-19 07:51:52; freshness should be verified.