CDCSI: A Machine Learning Index for Head and Neck Cancer Prognosis and Therapy Response

A 334.0 KB Excel file containing a machine learning-based index for head and neck squamous cell carcinoma (HNSCC). The data was developed by Xiaoqin Luo and published on figshare in May 2026. It integrates multi-omics data and 270 machine learning algorithms to create a prognostic and therapeutic response signature.

Use Cases

• Predicting patient prognosis in head and neck squamous cell carcinoma based on the CDCS index.
• Characterizing the tumor immune landscape based on the identified CDCS subtypes.
• Evaluating potential response to radiotherapy, chemotherapy, and targeted therapy based on the CDCSI score.
• Comparing prognostic performance against other published signatures using the provided validation framework.

Strengths

• Index is validated across multiple datasets and compared against previously published signatures.
• Construction integrated five types of multi-omics data and 270 machine learning algorithms.
• Interpretability is supported by SHAP analysis and validation at single-cell and spatial transcriptomic levels.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's 334.0 KB size suggests a limited scope, likely containing index scores and associated gene lists rather than raw patient data.

Provenance

Source

figshare

Collection Method

Integrated bioinformatics analysis of multi-omics data using machine learning algorithms.

Freshness

Last updated 2026-05-22 05:36:39; freshness should be verified.