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External validation of AI algorithms in breast radiology: the last healthcare security checkpoint?

Quant Imaging Med Surg 2021;11(6):2888-2892 | http://dx.doi.org/10.21037/qims-20-1409 Breast cancer is the most common cancer and the second most frequent cause of cancer death in women in the United States (1). In order to alleviate this major healthcare problem, breast cancer screening programs were launched in the eighties in developed countries. Although not free from controversy, population-based mammography screening has been proven to reduce breast cancer mortality between 20% to 40% in randomized controlled trials (1,2). However, there are still substantial differences between countries and regions in different aspects of these screening programs, such as starting age, mammography interval or number of readers. In addition, the interpretation of mammographies by radiologists is qualitative and therefore, susceptible to reading errors. In general, the rate of false-negative results remains low (between 10–30%), but the rate of false positive results is not negligible, and requires subsequent additional imaging and a significant number of benign biopsies (3). False positive results have been linked to a number of factors related to patient characteristics, technical issues and radiologists’ skills. Higher recall rates have been recorded in patients younger than 49-year-old, with higher density category, risk factors or previous mammographic findings (4). In addition, digital breast tomosynthesis, also known as 3D mammography, has widely demonstrated its superiority to conventional 2D mammography for breast lesion detection and characterization (5). In this direction, a recent meta-analysis suggests that breast tomosynthesis can improve cancer detection rates and decrease recall rates compared to traditional 2D mammography (6). Finally, double versus single reading, geographic location of the readers and particularly, radiologists’ reading volume and experience have also been linked to differences in results and recall rates of screening programs (4,7,8). Clearly, there is room for improvement in several aspects of this screening approach. Radiology is one of the most obvious gateways for the use of artificial intelligence (AI) in healthcare. The large amount of data present in medical imaging studies offers the ideal scenario for developing AI-based solutions. In this direction, deep learning (DL), a specific subtype of AI-based on artificial neural networks with representation learning, is expected to help radiologists to handle the growing number of radiological images and the resulting quantitative information (9). In this sense, breast tomosynthesis provides a greater number of images than 2D mammography, which involves an increase in radiologists’ workload and decision times (10). Also, AI is expected to alleviate the general deficit of radiologist numbers, as in a growing number of countries the number of radiologists per 100,000 population is commonly below the desired standard, making this growing workload overwhelming and unsustainable. In the specific case of breast imaging, the shortage of subspecialized radiologists may result in a significant problem, bearing in mind the increasing demand for mammographies. (11). Editorial Commentary