Data Sheet 2_Brief report: Artificial intelligence meets small cell lung cancer—integratin

A research document describes a dataset of 307 patients diagnosed with small-cell lung cancer, used to build prognostic models. The data includes clinicopathological information and whole-slide images, 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, last updated in April 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Training deep learning models for survival prediction based on whole-slide image data.
• Building random forest models for risk stratification based on clinicopathological variables.
• Developing multimodal AI models that integrate image and clinical data for prognostic prediction.
• Validating prognostic models for small-cell lung cancer on an independent clinical trial cohort.

Strengths

• Includes data from 307 patients with a defined split of 263 for training and 44 for validation.
• Model performance metrics, including AUC values with 95% confidence intervals, are reported for both training and validation sets.
• Data is multimodal, combining structured clinicopathological data with whole-slide images.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data tables is unknown, which may limit suitability assessment.
• The primary file is a 731.4 KB DOCX document, which suggests the dataset itself may be described rather than directly provided.

Provenance

Source

figshare, author Pedro Rocha.

Collection Method

Data was collected from patients diagnosed with SCLC, including participants in the CANTABRICO phase IIIB clinical trial.

Time Range

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Freshness

Last updated 2026-04-20 05:23:01; freshness should be verified.

Geography

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