Supplementary file 1_A fusion model based on tumor and peritumoral CT radiomics for differ

A retrospective analysis of 362 patients from two medical centers with pathologically confirmed bronchiolar adenoma or lung adenocarcinoma. The dataset includes clinical characteristics, CT morphological features, and tumor-peritumoral radiomics features extracted from CT scans, used to build a fusion model for non-invasive differentiation. The dataset was authored by Ziqian Zhao and last updated in April 2026.

Use Cases

• Training a classifier to differentiate bronchiolar adenoma from lung adenocarcinoma based on clinical and CT morphological features.
• Developing a radiomics model using tumor and peritumoral CT features for lung nodule analysis.
• Building a clinical-radiomics fusion model to improve preoperative diagnostic accuracy.
• Comparing the performance of different machine learning algorithms (e.g., MLP) on medical imaging data.

Strengths

• Includes data from 362 patients across two medical centers, providing an external validation set.
• The described fusion model achieved high performance, with AUCs up to 0.939 in validation.
• Dataset is openly shared under a CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a 1.4 MB DOCX, which may contain processed results rather than raw feature data.

Provenance

Source

figshare, authored by Ziqian Zhao.

Collection Method

Retrospective analysis of patient data from two medical centers.

Time Range

null

Freshness

Last updated 2026-04-15 04:37:03; freshness should be verified.

Geography

null