Esophageal Cancer Postoperative Survival Data with Nutritional and Inflammatory Indicators

660 patient records from a retrospective study (2012–2018) were used to develop a prognostic nomogram. The model incorporates the inflammation–immunity–nutrition score (IINS) and red cell distribution width-to-lymphocyte ratio (RLR) alongside clinical covariates. Huike Wang published this dataset on figshare under a CC-BY-4.0 license.

Use Cases

• Training survival prediction models based on combined nutritional and inflammatory indicators like IINS and RLR.
• Validating risk stratification tools against traditional AJCC staging for esophageal cancer.
• Investigating the relationship between host immunity surrogates and postoperative outcomes in oncology.
• Benchmarking model performance using metrics like C-index, AUC, and decision curve analysis mentioned in the study.

Strengths

• Data from 660 esophageal cancer patients who underwent radical surgery.
• Model validation performed using separate training (n=459) and validation (n=201) cohorts.
• Prognostic model incorporates 10 independent variables, including novel scores IINS and RLR.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 1.2 KB file size suggests a very limited scope, likely containing summary or model coefficients rather than raw patient data.

Provenance

Source

Huike Wang via figshare.

Collection Method

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

Time Range

Patient data likely spans 2012 to 2018.

Freshness

Last updated 2026-04-14 04:19:52; freshness should be verified.