The value of multiparametric MRI-based combined intratumoral and peritumoral radiomics in differentiating luminal and non-luminal molecular subtypes of breast cancer: a multicenter study.

BACKGROUND

Breast cancer remains the predominant contributor to global cancer-related morbidity and mortality in women. Luminal subtypes, accounting for approximately 70% of cases, demonstrate favorable prognoses through endocrine-targeted therapeutic regimens owing to hormone receptor positivity. Conversely, non-luminal breast cancer variants, including human epidermal growth factor receptor 2 (HER2)-enriched and triple-negative subtypes, exhibit aggressive biological characteristics, intrinsic endocrine therapy resistance, and require molecularly guided therapeutic strategies such as HER2-directed biologicals, platinum-based cytotoxic regimens, or radiation therapy. This study aims to evaluate whether preoperative multiparametric magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics can effectively discriminate between luminal and non-luminal breast cancer subtypes.

METHODS

This retrospective study analyzed 305 female breast cancer patients. Center 1 (Affiliated Hospital of Qinghai University) was randomly split into a training set (n=140) and an internal test set (n=59) in a 7:3 ratio, while Center 2 (Second Hospital of Lanzhou University) (n=67) and Center 3 (The Cancer Imaging Archive I-SPY1 trial) (n=39) served as external test sets 1 and 2, respectively. Tumor subtypes were classified as luminal or non-luminal based on estrogen receptor (ER) and progesterone receptor (PR) status. Two radiologists performed manual tumor segmentation using 3D Slicer on multiparametric MRI sequences: dynamic contrast enhancement (DCE; phases 3 or 4), fat-suppressed T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Peritumoral regions were defined by a 3 mm expansion from the tumor volume of interest (VOI). For each sequence (intratumoral and peritumoral), 2,252 radiomics features were extracted using PyRadiomics. After Z-score normalization, features were selected through univariate analysis, correlation analysis, and simulated annealing. Eight radiomics models were constructed using random forest (RF), including intratumoral-only, combined intratumoral-peritumoral (3 mm), and multisequence fusion models. Performance was assessed using area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

After feature selection, eight optimal radiomics features were used for model development. The combined DWI_Peri3 + T2WI_Peri3 + DCE_Peri3 RF model demonstrated superior performance, with AUCs of 0.819 [95% confidence interval (CI): 0.748-0.889], 0.795 (95% CI: 0.676-0.915), and 0.771 (95% CI: 0.640-0.902) in training, internal validation, and external validation set 1, respectively. Among single-parameter models, T2WI_Peri3 RF showed the best classification performance (AUC =0.774, 95% CI: 0.698-0.849) for luminal non-luminal differentiation.

CONCLUSIONS

The model constructed based on multiparametric MRI intratumor combined with peritumor radiomics features can better predict luminal and non-luminal types of breast cancer. This study can provide a reference basis for individualized treatment plans for breast cancer.