Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value-an analysis of 2473 invasive breast cancer patients

Relationships Between Breast Edema and Axillary Lymph Node Metastasis in Breast Cancer

Background/Objectives: To investigate the association between MRI features of primary breast cancers with axillary status, aiming to identify possible imaging biomarkers. Methods: Patients diagnosed with breast cancer between 2021 and 2023 in our clinic were retrospectively evaluated, and those that presented as mass lesions on preoperative MRI examinations (n: 123) were included in the study. Patients with and without metastatic axillary lymph nodes (mALN) were compared in terms of breast density, background parenchymal enhancement, tumor size, location in the breast, distance from the skin, patient age, presence of edema, multiple foci, histopathological type and molecular subtype of tumors. In multifocal/multicentric cases, the largest lesion was taken into consideration. Prepectoral and subcutaneous edema were considered diffuse edema, while perilesional edema was considered focal edema. MannWhitney U/Student-t test, Chi- square/Fischer Exact tests and logistic regression analysis were used for statistical analyses as appropriate. Results: Axilla was positive in 88 patients. There was a statistically significant difference in terms of edema, age, molecular subtype, Ki-67 index, number of lesions, tumor size, and laterality between the two groups (p < 0.05). Univariate logistic regression analysis showed that all included variables were statistically significant (p < 0.05). Multivariate logistic regression analysis revealed that presence of edema (OR: 2.46 CI; 1.11-5.48, p = 0.027) and multiple lesions (OR: 1.86 CI; 1.01-3.43, p = 0.046) were significantly associated with mALN. There was no significant difference between peritumoral edema and diffuse edema. Conclusions: Our study showed a statistically significant relationship between the axillary status and the presence of edema and multiple tumoral lesions on MRI. These findings have a potential to serve as prognostic imaging biomarkers for predicting the presence of mALN. Further studies with larger case series are needed to support our findings.

Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis

BACKGROUND

To perform a systematic review and meta-analysis that assesses the diagnostic performance of deep learning algorithms applied to breast MRI for predicting axillary lymph nodes metastases in patients of breast cancer.

METHODS

A systematic literature search in PubMed, MEDLINE, and Embase databases for articles published from January 2004 to February 2025. Inclusion criteria were: patients with breast cancer; deep learning using MRI images was applied to predict axillary lymph nodes metastases; sufficient data were present; original research articles. Quality Assessment of Diagnostic Accuracy Studies-AI and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. A summary receiver operating characteristic curve (SROC) was performed. R statistical software (version 4.4.0) was used for statistical analyses.

RESULTS

A total of 10 studies were included. The pooled sensitivity and specificity were 0.76 (95% CI, 0.67-0.83) and 0.81 (95% CI, 0.74-0.87), respectively, with both measures having moderate between-study heterogeneity (I2 = 61% and 60%, respectively; p < 0.01). The SROC analysis yielded a weighted AUC of 0.788.

CONCLUSION

This meta-analysis demonstrates that deep learning algorithms applied to breast MRI offer promising diagnostic performance for predicting axillary lymph node metastases in breast cancer patients. Incorporating deep learning into clinical practice may enhance decision-making by providing a non-invasive method to more accurately predict lymph node involvement, potentially reducing unnecessary surgeries.

Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI

Background

Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) is essential for customizing treatment plans and enhancing patient results.

Objective

This research sought to create and verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors to improve the accuracy of predicting ALNM in young breast cancer patients.

Methods

We performed a retrospective analysis at one facility, involving the creation and validation of a nomogram in two stages.At first, a medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, and MRI-determined ALN status.A more detailed clinical-radiomics model was subsequently developed by integrating radiomic characteristics derived from DCE-MRI images.These models were created using logistic regression analyses on a training dataset, and their effectiveness was assessed by measuring the area under the receiver operating characteristic curve (AUC) in a separate validation dataset.

Results

The clinical-radiomics nomogram surpassed the clinical-only model, recording an AUC of 0.892 in the training dataset and 0.877 in the validation dataset.Significant predictors included MRI-reported ALN status and select radiomic features, which markedly enhanced the model's predictive capacity.

Conclusion

Integrating radiomic features with clinical predictors in a nomogram significantly improves ALNM prediction in young onset breast cancer, providing a valuable tool for personalized treatment planning. This study underscores the potential of merging advanced imaging data with clinical insights to refine oncological predictive models. Future research should expand to multicentric studies and include genomic data to boost the nomogram's generalizability and precision.