Abstract
BACKGROUND: Detection of metastases in axillary lymph nodes (ALNs) is of vital significance for determining appropriate therapeutic strategies and prognosis for breast cancer patients. Studies combining multiparametric magnetic resonance imaging (MRI) and pathological biomarkers for predicting ALN metastasis in breast cancer are rarely reported. This study aimed to evaluate the predictive value of conventional MRI features, intravoxel incoherent motion (IVIM), quantitative dynamic contrast-enhanced MRI (DCE-MRI), and pathological biomarkers for ALN metastasis in breast cancer patients. METHODS: In total, 149 subjects with breast cancer confirmed via pathology were recruited for study. Among the participants, patients were randomly allocated to the training cohort (42 and 62 presented with ALN and non-ALN metastasis) or validation cohort (18 and 27 presented with ALN and non-ALN metastasis), respectively. All participants underwent both IVIM and DCE-MRI. The analysis focused on the clinicopathological characteristics along with conventional MRI features, in addition to assessment of a range of quantitative parameters, including DCE-MRI derived parameters (K(trans), Kep and Ve), and the IVIM-derived parameter [apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), perfusion fraction (f)]. To evaluate diagnostic efficacy in predicting ALN metastasis, multivariate logistic regression and receiver operating characteristic (ROC) curve assessments were conducted. A nomogram for the combined model was created on the basis of the findings derived from the multivariate logistic regression model. RESULTS: In the training and validation cohorts, patients with ALN metastasis had significantly higher Ki-67 (P=0.01, P=0.03) and hypoxia-inducible factor-1 alpha (HIF-1α) expression (P<0.001, P=0.04). Lymphovascular invasion (LVI) and programmed death ligand-1 (PD-L1) expression were significantly more common in the metastatic group (P=0.002, P=0.003, respectively) in the training cohort. In the training and test cohorts, compared to the non-metastatic group, patients with ALN metastasis exhibited significantly lower D values (all P<0.001) and significantly higher values of D* (P=0.02, P=0.04), K(trans) (all P<0.001), and Kep (all P<0.001). Multivariate analysis identified PD-L1 [odds ratio (OR) =82.55, P=0.045], lesion margin (OR =21.08, P=0.048), D (OR <1,000, P=0.01), and K(trans) (OR >1,000, P=0.01) as independent predictors. Calibration curves confirmed excellent agreement between predicted and observed outcomes (P=0.99). Furthermore, in both training and test validations, the combined model achieved significantly enhanced the areas under the ROC curve (AUCs) compared with the pathologic, conventional MRI, IVIM, and DCE-MRI models (Z=2.083-4.402, P<0.05). CONCLUSIONS: Combining MRI parameters (lesion margin, D, K(trans)) with pathological biomarker PD-L1 significantly improves prediction accuracy for ALN metastasis in breast cancer. This integrated model has considerable clinical potential, enabling precise preoperative assessment and potentially reducing unnecessary lymph node biopsies.