ABVS and VTIQ Parameters for Predicting Breast Cancer Lymph Node Metastasis

Tao Liu's dataset contains clinical and imaging parameters for 80 breast cancer patients from the Third Affiliated Hospital of Guangzhou Medical University, collected between June 2021 and August 2023. The data includes parameters from Automated Breast Volume Scanner (ABVS) and Virtual Touch Imaging and Quantification (VTIQ) measurements. It was used to investigate predictors for high axillary lymph node metastasis burden.

Use Cases

• Building a binary classifier to predict high vs. low lymph node metastasis burden based on imaging parameters.
• Analyzing the predictive value of the 'lotus root sign' and 'maximum diameter' features for metastasis.
• Evaluating the combined diagnostic performance (AUC, sensitivity, specificity) of ABVS and VTIQ parameters.
• Conducting feature importance analysis on parameters like 'mean shear wave velocity' for clinical risk assessment.

Strengths

• Includes data for 80 patients with a mean age of 55.61 ± 11.10 years.
• Multivariate logistic regression results (odds ratios, confidence intervals, p-values) are provided for key predictors.
• Reported AUC for the combined parameters is 0.872 with 73.0% sensitivity and 90.7% specificity.

Limitations

• Row count and column-level documentation are unknown; field semantics must be inferred after download.
• Data is from a single hospital (Third Affiliated Hospital of Guangzhou Medical University) and a limited time range, which may affect generalizability.
• The dataset is very small at 98.6 KB, indicating limited scope.

Provenance

Source

Tao Liu, via figshare.

Collection Method

Retrospective study of patient records and imaging data.

Time Range

June 2021 to August 2023.

Freshness

Last updated 2026-04-10 05:57:57; freshness should be verified.

Geography

Patients from the Third Affiliated Hospital of Guangzhou Medical University.