Esophageal Cancer Survival Prognostic Model with Nutritional and Inflammatory Indicators

Huike Wang's retrospective study analyzes clinical data from 660 esophageal cancer patients who underwent radical surgery between 2012 and 2018. The dataset includes derived indicators like the inflammation–immunity–nutrition score (IINS) and red cell distribution width-to-lymphocyte ratio (RLR), used to build and validate a prognostic nomogram for postoperative survival. The data was last updated on 2026-04-14 and is shared under a CC-BY-4.0 license.

Use Cases

• Training survival prediction models based on the inflammation–immunity–nutrition score (IINS) and red cell distribution width-to-lymphocyte ratio (RLR).
• Benchmarking new prognostic indicators against the established AJCC staging system for esophageal cancer.
• Conducting retrospective analyses on the relationship between systemic inflammation, nutritional status, and surgical outcomes.
• Developing risk stratification tools for perioperative optimization and potential immunotherapy patient selection.

Strengths

• Includes data from 660 patient records, split into training (n=459) and validation (n=201) cohorts.
• Model validation involved multiple metrics including C-index, calibration, time-dependent AUC, DCA, IDI, and NRI.
• The prognostic model incorporates 10 independent variables, including the novel IINS and RLR indicators.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for some machine learning tasks.
• Data is from a single retrospective study; external multicenter validation is noted as warranted.

Provenance

Source

Huike Wang via figshare.

Collection Method

Retrospective analysis of clinical data from patients who underwent radical surgery.

Time Range

Patient data from 2012–2018.

Freshness

Last updated 2026-04-14 04:19:51; freshness should be verified.