KEYWORDS: Breast, Tissues, Image segmentation, Data modeling, Magnetic resonance imaging, Image registration, Medical imaging, Cancer, Breast cancer, Artificial intelligence
Breast Magnetic Resonance Imaging (MRI) is recognized as the most sensitive imaging method for the early detection of breast cancer in women who carry a lifetime risk for breast cancer higher than or equal to 20%. Given the aggressive biology of cancers in this population, early detection is crucial for a favorable prognosis. This study aimed to use artificial intelligence for the detection of lesions at the earliest stage in high-risk women. A Generative Adversarial Network (GAN) detected lesions in breast MR data by quantifying anomaly as divergence from healthy breast tissue appearance. First, follow-up images of patients were aligned and the breast was segmented automatically. Then, the GAN created a model of healthy variability of appearance change during follow-up in 64x64-sized image patches sampled only at healthy tissue locations in follow-up image sequences. During the assessment of new data, each image position was compared with the model yielding an anomaly score. On a image patch level, we evaluated if this anomaly score identifies confirmed lesions, as well as lesionfree regions, where lesions appear during later follow-up studies. In the first experiment of lesion detection, a mean sensitivity of 99.5% and a mean specificity of 84% was achieved. When applying the model to studies denoted as lesion-free, subsequently occurring lesions were predicted with a mean sensitivity of 92.7% and a mean specificity of 78.8%.
In this study we used a large previously built database of 2,892 mammograms and 31,650 single mammogram radiologists’ assessments to simulate the impact of replacing one radiologist by an AI system in a double reading setting. The double human reading scenario and the double hybrid reading scenario (second reader replaced by an AI system) were simulated via bootstrapping using different combinations of mammograms and radiologists from the database. The main outcomes of each scenario were sensitivity, specificity and workload (number of necessary readings). The results showed that when using AI as a second reader, workload can be reduced by 44%, sensitivity remains similar (difference -0.1%; 95% CI = - 4.1%, 3.9%), and specificity increases by 5.3% (P<0.001). Our results suggest that using AI as a second reader in a double reading setting as in screening programs could be a strategy to reduce workload and false positive recalls without affecting sensitivity.
KEYWORDS: Magnetic resonance imaging, Breast cancer, Feature extraction, Decision support systems, Lawrencium, Diffusion weighted imaging, Data conversion, Data modeling, Cancer, Machine learning
Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced breast cancer to reduce tumor burden, and potentially enable breast conservation. Response to treatment is assessed by histopathology from surgical specimen, a pathological complete response (pCR), or a minimal residual disease are associated with an improved disease-free, and overall survival. Early identification of non-responders is crucial as these patients might require different, or more aggressive treatment. Multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this paper, we determine the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death.
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