基于列线图的临床病理生物标志物联合多参数 MRI 对乳腺癌 TIL（肿瘤浸润淋巴细胞）表达的预测价值

A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results.

Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05).

Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.