Dietzel, Matthias;
Baltzer, Pascal A. T.;
Dietzel, Andreas;
Vag, Tibor;
Gröschel, Tobias;
Gajda, Mieczyslaw;
Camara, Oumar;
Kaiser, Werner A.
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
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Medientyp:
E-Artikel
Titel:
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
Beteiligte:
Dietzel, Matthias;
Baltzer, Pascal A. T.;
Dietzel, Andreas;
Vag, Tibor;
Gröschel, Tobias;
Gajda, Mieczyslaw;
Camara, Oumar;
Kaiser, Werner A.
Beschreibung:
<jats:p> 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. </jats:p><jats:p> Purpose: To predict N+ using an artificial neural network (ANN) on the basis of 17 previously published descriptors of breast lesions in bMRI. </jats:p><jats:p> 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). </jats:p><jats:p> 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). </jats:p><jats:p> 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. </jats:p>