Data Sheet 1_Non-invasive prediction of lymph node involvement in prostate cancer via mach

A retrospective cohort of 339 prostate cancer patients who underwent preoperative whole-prostate MRI followed by radical prostatectomy and extended pelvic lymph node dissection. The dataset, created by Yun Luo and last updated in 2026, was used to develop a composite machine learning model integrating MRI-derived radiomics, deep learning features, and clinical parameters.

Use Cases

• Training machine learning models for lymph node status prediction based on integrated clinical and imaging features.
• Benchmarking radiomics and deep learning feature extraction methods on T2-weighted MRI scans.
• Validating non-invasive prediction models against surgical pathology results from extended pelvic lymph node dissection.
• Conducting decision curve analysis to assess the clinical utility of predictive models in prostate cancer management.

Strengths

• Model performance is quantified with high ROC-AUC scores of 0.963 and 0.920 in training and validation sets.
• Dataset size is specified as 339 patients with linked preoperative MRI and surgical pathology data.
• The model integrates multiple data types: clinical parameters, radiomics, and deep learning features from MRI.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying feature data is unknown, which may limit suitability assessment.
• Data may reflect temporal and institutional bias inherent to the single retrospective cohort study.

Provenance

Source

figshare

Collection Method

Retrospective cohort study of patients who underwent preoperative MRI and subsequent surgery.

Freshness

Last updated 2026-05-11 04:26:54; freshness should be verified.