Abstract
Background: In breast MRI (bMRI), prediction of lymph node metastases (N+) on the basis of dynamic and morphologic descriptors of breast cancers remains a complex task.
Purpose: To predict N+ using an artificial neural network (ANN) on the basis of 17 previously published descriptors of breast lesions in bMRI.
Material and Methods: Standardized protocol and study design were applied in this study (T1w-FLASH; 0.1 mmol/kg body weight Gd-DTPA; T2w-TSE; histological verification after bMRI). All lesions were evaluated by two experienced radiologists in consensus. In every lesion 17 previously published descriptors were assessed. Matched subgroups with (N+; n=97) and without N+ were created (N−; n=97), forming the dataset of this study (n=194). An ANN was constructed (“Multilayer Perceptron”; training: “Batch”; activation function of hidden/output layer: “Hyperbolic Tangent”/”Softmax”) to predict N+ using all descriptors in combination on a randomly chosen training sample (n=123/194) and optimized on the corresponding test sample (n=71/194) using dedicated software. The discrimination power of this ANN was quantified by area under the curve (AUC) comparison (vs AUC=0.5). Training and testing cycles were repeated 20 times to quantify the robustness of the ANN (median-AUC; confidence intervals, CIs).
Results: The ANN demonstrated highly significant discrimination power to classify N+ vs N− (P<0.001). Diagnostic accuracy reached “moderate” AUC (median-AUC=0.74; CI 0.70–0.76).
Conclusion: Application of ANNs for the prediction of lymph node metastases in breast MRI is feasible. Future studies should evaluate the clinical impact of the presented model.
Acknowledgments
The long-term database from which the dataset of the present study was extracted could only be made possible due to the ongoing work of our whole breast MRI research team over many years. In this context we would like to thank all our former colleagues and MRI technicians who contributed to the data collection.
Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.