Hemodynamic Inflow-Outflow Profiling as an Integrated DCE-MRI Biomarker for Diagnosis and Risk Stratification in Breast Cancer: A Multicenter Retrospective Study.

Abstract

Background: Limitations in conventional MRI biomarkers (ADC, DCE-MRI parameters) for characterizing tumor microenvironment heterogeneity necessitate novel approaches integrating vascular remodeling dynamics. We introduce the Intratumoral-Peritumoral Inflow-Outflow (IPIO) model to address these constraints.

Methods: This multicenter retrospective study developed and validated a hemodynamics-driven IPIO model using DCE-MRI kinetics in 159 patients (training: n=93; independent validation: n=66). Performance was evaluated for malignancy detection and stratification (invasive grading, molecular subtyping of TNBC, Ki-67 expression, lymph node metastasis) through ROC analysis and correlation studies.

Results: IPIO demonstrated robust malignancy discrimination (AUC 0.934, 95% CI: 0.897-0.974; sensitivity 91.7%, specificity 83.3%), significantly outperforming ADC (ΔAUC +0.123, P<0.05) and conventional kinetics. For TNBC identification, IPIO achieved an AUC of 0.901 (accuracy 96.4%); for LNM detection, AUC was 0.821 (sensitivity 95.5%); Strong correlations with invasive grade (r=0.548), Ki-67 (r=0.501), and metastasis (r=0.506; all P<0.001) confirmed biological coherence. Performance gradients (malignancy > TNBC > LNM) reflect increasing microenvironment complexity.

Conclusion: IPIO provides an integrated hemodynamic framework for both breast cancer diagnosis and risk stratification, with particular clinical utility for identifying triple-negative subtypes and metastatic potential. Its spatiotemporal profiling of vascular-peritumoral interactions represents a promising approach for noninvasive tumor characterization. Limitations include retrospective design and small TNBC subgroup; prospective validation is required.