The impact of (simulated) resolution on breast cancer diagnosis based on high-resolution 3D micro-CT microcalcification images.

Abstract

Breast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs - if their characteristics are captured at high resolution and in3D. The main purpose of the study was to explore the impact of image resolution (8µm, 16µm, 32µm, 64µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MCimages. Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8µm (original resolution) and 16µm, 32µm, 64µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom-based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8µm, 16µm, 32µm, and 64µm resolution; (b) the same MCs visible in 8µm, 16µm, and 32µm resolution; (c) the same MCs visible in 8µm and 16µm resolution; (d) all MCs visible in 8µm, 16µm, 32µm, and 64µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for eachexperiment. The individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16µm instead of 8µm, a 4.01% drop when using 32µm instead of 8µm, a 10.69% drop when using 64µm instead of 8µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16µm instead of 8µm, a 4.91% drop when using 32µm instead of 8µm, and a 6.3% drop when using 64µm instead of 8µm. The highest classification results are obtained at the highest resolution (8µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70µm), breast cancer diagnosis will beimproved.