Application of artificial neural networks for the prediction of lymph node metastases to the ipsilateral axilla - initial experience in 194 patients using magnetic resonance mammography.

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.