Data Sheet 1_Brief report: Artificial intelligence meets small cell lung cancer—integratin

A research dataset of 307 patients diagnosed with small-cell lung cancer, used to build prognostic models. The data includes clinicopathological information and whole-slide image inputs, with a training set of 263 patients and a validation set of 44 patients from the CANTABRICO phase IIIB clinical trial. The dataset was authored by Pedro Rocha and last updated on April 20, 2026.

Use Cases

• Training prognostic models for long-term overall survival based on clinicopathological data.
• Developing deep learning models for survival prediction based on whole-slide image analysis.
• Benchmarking multimodal model performance by combining random forest and deep learning outputs.
• Studying risk stratification for small-cell lung cancer to personalize treatment strategies.

Strengths

• Dataset includes data from 307 patients, providing a specific cohort size.
• Model performance metrics, including AUC values with 95% confidence intervals, are reported for both training and validation sets.
• Data is structured with a defined training set (263 patients) and validation set (44 patients).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file format is DOCX, which may not be the most accessible format for direct data analysis.

Provenance

Source

figshare

Collection Method

Data was collected from patients participating in the CANTABRICO phase IIIB clinical trial.

Time Range

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Freshness

Last updated 2026-04-20 05:22:59; freshness should be verified.

Geography

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