Multi-parameter MRI deep learning model for lymphovascular invasion assessment in invasive breast ductal carcinoma: A
1Department of Radiology, The First Affiliated Hospital of Wannan Medical College (Yiji shan Hospital), Wuhu, 241000, China.
Aims:
To investigate the value of multi-parametric magnetic resonance imaging (MRI)-based deep learning (DL) in predicting the Lymphovascular Invasion (LVI) status of invasive breast ductal cancer (IBDC).
Materials And Methods:
A retrospective analysis of 448 IBDC patients from two centers was conducted, with Center 1 split into training and validation sets (8:2 ratio) and Center 2 used as the test set. A MobileNetV2-3D DL model was used to compare T1WI-CE2 and T1WI-CE3 performance, selecting the latter for fusion tasks. The combined model integrates clinical-radiological features (CRF) and multi-parameter MRI DL features to improve predictive accuracy for LVI. The predictive performance of the model was evaluated by calculating the area under the ROC curve (AUC). Calibration curves were used to evaluate the agreement between predicted outcomes and observations. Furthermore, decision curve analysis (DCA) quantified the net benefit across decision thresholds.
Results:
The T1WI-CE3 DL model achieved AUCs of 0.842 and 0.774 on the validation and test sets, respectively, outperforming the T1WI-CE2 DL model, which had AUCs of 0.748 and 0.619. The combined model achieved a validation set AUC of 0.939 with 0.892 accuracy and a test set AUC of 0.941 with 0.872 accuracy. Calibration curves showed good prediction consistency, while DCA confirmed the combined model's clinical utility, supporting its accuracy, reliability, and potential for application.
Conclusions:
This study offers a novel tool for preoperative LVI assessment in IBDC patients by integrating multi-parametric MRI with DL, potentially aiding clinicians in devising more precise treatment strategies.
