Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.

Abstract

Simple SummaryThe implementation of artificial intelligence in the computer-aided decision (CAD) support systems holds great promise for future cancer diagnosis. It is crucial to build these algorithms in a structured manner to ensure reproducibility and reliability. In this context, we used a dataset of breast ultrasound (US) images with 252 breast cancer and 253 benign cases to refine the CAD image analysis workflow. Various dataset preparations (i.e., pre-processing, and spatial augmentation) and machine learning algorithms were tested to establish the framework with the best performance in the detection and classification of breast lesions in US images. The efficacy of the proposed workflows was evaluated regarding accuracy, precision, specificity, and sensitivity.Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.