A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer

Abstract EN

Objectives.

To develop and validate a radiomics-based nomogram with texture features from mammography for the prognostic prediction in patients with early-stage triple-negative breast cancer (TNBC).

Methods.

The study included 200 consecutive patients with TNBC (training cohort: n = 133, validation cohort: n = 67).

A total of 136 mammography-derived textural features were extracted, and LASSO (least absolute shrinkage and selection operator) was applied to select features for building the radiomics score (Rad-score).

After univariate and multivariate logistic regression, a radiomics-based nomogram was constructed with independent prognostic factors.

The discrimination and calibration power were assessed, and further the clinical applicability of the nomograms was evaluated.

Results.

Among the 136 mammography-derived textural features, fourteen were used to build the Rad-score after LASSO regression.

A radiomics nomogram that incorporates Rad-score and pN stage was constructed.

This nomogram achieved a C-index of 0.873 (95% CI: 0.758–0.989) for predicting iDFS (invasive disease-free survival), which outperformed the clinical model.

Moreover, it is feasible to stratify patients into high-risk and low-risk groups based on the optimal cut-off point of Rad-score.

The validations of the nomogram confirmed favorable discrimination and considerable predictive efficiency.

Conclusions.

The radiomics nomogram that incorporates Rad-score and pN stage exhibited favorable performance in the prediction of iDFS in patients with early-stage TNBCs.