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Published on: December 15, 2014
Preliminary Study of the Added Value of T1 Mapping in Multiparametric MRI Radiomics for Noninvasive Prediction of
Chun Lian1, Yan He1, Jianle Liang1
1Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, China (C.L., Y.H., J.L., D.A., J.L., G.L., R.H., Y.D. ).
Rationale And Objectives:
This study presents a preliminary effort to quantify the value of T1 mapping by comparing radiomics models from conventional multiparametric magnetic resonance imaging (mpMRI) with and without T1 mapping to improve noninvasive breast cancer biological assessment.
Materials And Methods:
All enrolled patients underwent mpMRI examinations, including T1 mapping, T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced (DCE) sequences. Images were registered to DCE-MRI using Elastix. Radiomics features were extracted via PyRadiomics and normalized. Five models were built and compared: conventional mpMRI, non‑contrast mpMRI with T1 mapping, comprehensive mpMRI, clinical, and integrated clinicoradiologic models. Feature selection was conducted using analysis of variance and least absolute shrinkage and selection operator regression. Model performance was evaluated using the area under the curve (AUC), with 95% confidence intervals estimated via 1000 bootstrap resamples of the test set. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis and nomograms.
Results:
A total of 101 breast cancer patients were included (estrogen receptor [ER]+/-: 85/16; progesterone receptor [PR]+/-: 75/26; human epidermal growth factor receptor 2 [HER2]+/-: 56/45; Ki-67 high/low: 57/34; histological grade high/low: 28/68; axillary lymph node [ALN]+/-: 39/62). The integrated clinicoradiologic model achieved the highest predictive performance, with test AUCs of 0.944 for ER, 0.990 for PR, 0.956 for HER2, 0.862 for Ki-67, 0.931 for histological grade, and 0.912 for ALN status, outperforming all ablated models. SHAP analysis identified key radiomics features for grade and nodal status, while nomograms offered intuitive individualized risk assessment.
Conclusion:
Radiomics models incorporating T1 mapping with conventional mpMRI, particularly when combined with clinical data, provide a noninvasive approach for characterizing breast cancer biology and may aid personalized clinical management. Nevertheless, these findings remain preliminary and require validation in larger, multicenter cohorts before clinical translation.
