P D Stelzer1, O Steding2, M W Raudner3, G Euller4, P Clauser5, P A T Baltzer6. 1. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: philipp.d.stelzer@meduniwien.ac.at. 2. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: oliver.steding@meduniwien.ac.at. 3. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: marcus.raudner@meduniwien.ac.at. 4. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: gordon.euller@meduniwien.ac.at. 5. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: paola.clauser@meduniwien.ac.at. 6. Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, 1090 Vienna, Austria. Electronic address: pascal.baltzer@meduniwien.ac.at.
Abstract
OBJECTIVES: To investigate whether combined texture analysis and machine learning can distinguish malignant from benign suspicious mammographic calcifications, to find an exploratory rule-out criterion to potentially avoid unnecessary benign biopsies. METHODS: Magnification views of 235 patients which underwent vacuum-assisted biopsy of suspicious calcifications (BI-RADS 4) during a two-year period were retrospectively analyzed using the texture analysis tool MaZda (Version 4.6). Microcalcifications were manually segmented and analyzed by two readers, resulting in 249 image features from gray-value histogram, gray-level co-occurrence and run-length matrices. After feature reduction with principal component analysis (PCA), a multilayer perceptron (MLP) artificial neural network was trained using histological results as the reference standard. For training and testing of this model, the dataset was split into 70 % and 30 %. ROC analysis was used to calculate diagnostic performance indices. RESULTS: 226 patients (150 benign, 76 malignant) were included in the final analysis due to missing data in 9 cases. Feature selection yielded nine image features for MLP training. Area under the ROC-curve in the testing dataset (n = 54) was 0.82 (95 %-CI: 0.70-0.94) and 0.832 (95 %-CI 0.72-0.94) for both readers, respectively. A high sensitivity threshold criterion was identified in the training dataset and successfully applied to the testing dataset, demonstrating the potential to avoid 37.1-45.7 % of unnecessary biopsies at the cost of one false-negative for each reader. CONCLUSION: Combined texture analysis and machine learning could be used for risk stratification in suspicious mammographic calcifications. At low costs in terms of false-negatives, unnecessary biopsies could be avoided.
OBJECTIVES: To investigate whether combined texture analysis and machine learning can distinguish malignant from benign suspicious mammographic calcifications, to find an exploratory rule-out criterion to potentially avoid unnecessary benign biopsies. METHODS: Magnification views of 235 patients which underwent vacuum-assisted biopsy of suspicious calcifications (BI-RADS 4) during a two-year period were retrospectively analyzed using the texture analysis tool MaZda (Version 4.6). Microcalcifications were manually segmented and analyzed by two readers, resulting in 249 image features from gray-value histogram, gray-level co-occurrence and run-length matrices. After feature reduction with principal component analysis (PCA), a multilayer perceptron (MLP) artificial neural network was trained using histological results as the reference standard. For training and testing of this model, the dataset was split into 70 % and 30 %. ROC analysis was used to calculate diagnostic performance indices. RESULTS: 226 patients (150 benign, 76 malignant) were included in the final analysis due to missing data in 9 cases. Feature selection yielded nine image features for MLP training. Area under the ROC-curve in the testing dataset (n = 54) was 0.82 (95 %-CI: 0.70-0.94) and 0.832 (95 %-CI 0.72-0.94) for both readers, respectively. A high sensitivity threshold criterion was identified in the training dataset and successfully applied to the testing dataset, demonstrating the potential to avoid 37.1-45.7 % of unnecessary biopsies at the cost of one false-negative for each reader. CONCLUSION: Combined texture analysis and machine learning could be used for risk stratification in suspicious mammographic calcifications. At low costs in terms of false-negatives, unnecessary biopsies could be avoided.