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Updated: Dec 9, 2025
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with
Xian Jiang1,2, Xiuhe Zou3, Jing Sun4
1Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
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.
