# Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer

## TL;DR

This study shows that a machine learning model using CT scan data can help distinguish between two types of breast cancer, potentially aiding in diagnosis.

## Contribution

A novel machine learning model using dual-layer spectral CT radiomic features for differentiating Luminal and non-Luminal breast cancer is proposed.

## Key findings

- A Gaussian Naive Bayes model achieved an AUC of 0.778 in predicting Luminal vs non-Luminal breast cancer.
- 13 optimal radiomic features combined with clinical parameters improved model performance.
- The model outperformed six other machine learning models in accuracy and stability.

## Abstract

This study aims to explore the value of a machine learning (ML) model based on dual-layer detector spectral CT (DLCT) radiomic features in predicting Luminal versus non-Luminal breast cancer (BC).

A retrospective analysis was conducted on 128 pathologically confirmed BC patients from the Department of Breast Surgery, Jiangsu Cancer Hospital. DLCT chest enhancement images were analyzed, with regions of interest delineated to extract radiomic features. Optimal features were selected through univariate analysis, correlation analysis, and LASSO algorithm, followed by ML model construction.

A total of 1,037 radiomic features were extracted, from which 13 optimal features were selected. Combined with clinical parameters (age, body mass index (BMI), and menopausal status), seven ML models were constructed. Among them, the Gaussian Naive Bayes (GNB) model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.778 (95% CI: 0.582–0.974), accuracy of 0.821, sensitivity of 0.833, and specificity of 0.778, outperforming the other six models.

The GNB model demonstrated relatively superior and stable predictive performance in internal testing, suggesting that DLCT radiomics may offer a potential auxiliary tool for distinguishing between Luminal and non-Luminal BC. However, further validation through large-scale multicenter studies is required.