Predicting Overall Survival of NSCLC Patients with Clinical and Imaging Features

A dataset supporting research on predicting overall survival for patients with Non-Small Cell Lung Cancer (NSCLC). It was authored by Kanakarajan, Hemalatha and last updated on May 25, 2026. The data package contains clinical, radiomics, and deep learning features as described in the associated research paper.

Use Cases

• Train survival prediction models based on clinical patient data.
• Develop radiomics-based prognostic tools for NSCLC.
• Benchmark deep learning feature extraction methods for medical imaging.
• Conduct multi-modal analysis combining clinical and imaging data for cancer research.

Strengths

• Data is directly linked to a specific, described research paper titled 'Predicting overall survival of NSCLC patients with clinical, radiomics and deep learning features'.
• Last update timestamp is precisely recorded as 2026-05-25 06:10:15.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

DataverseNL Harvested Dataverse

Freshness

Last updated 2026-05-25 06:10:15.