Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications

The rapid competitive economy of machine learning development: a discussion on the social risks and benefits

“…The same holds true in the natural sciences: engineers are training AI systems to recognize cancer cells on MRI images. This does not make the physician unnecessary, but instead shifts his or her job to becoming a controlling agent, making sure that the computer has found the right solutions (mostly checking for false positives), thereby making the whole process of diagnosing cancer much more efficient and accurate [120]. In other words, the AI provides valuable information whereas the physician checks the results and makes the necessary decisions afterwards.…”

Assessing the clinical performance of artificial intelligence software for prostate cancer detection on MRI

“…Extended systematic and targeted biopsies would be better for algorithm learning [12]. In a similar vein, prostatectomy histology learning also introduces bias because many men do not undergo prostatectomy after MRI-influenced biopsies [2]. Furthermore, radiologists' determinations on the likely nature of the CAD detected lesions as used in the current study are not accurate because of the dependence of predictive value for csPCa on the PI-RADS category.…”

“…Multiple researchers are developing computer-aided detection (CAD) algorithms to enable the detection of clinically significant prostate cancer (csPCa) on MRI [1][2][3]. Commercial vendors are offering early versions of CAD software that claim to improve radiologists' performance and productivity by decreasing the observer variability for detecting suspicious lesions while decreasing time-intensive reporting and data processing tasks.…”

“…These developments are to be welcomed by radiologists, urologists and healthcare administrators, as we face the increased demand for MRI-influenced prostate biopsies. As software developments gather momentum, developers need to provide clinically relevant metrics to enable comparisons between algorithms, commercial products and the current standard of multidisciplinary care [2].…”

“…For men with suspicious MRI scans, accurate localization of all positive targets is necessary to ensure appropriate tissue sampling. A suitable lesion-level metric is the false detection rate in men deemed to be positive by experienced radiologists [2]. Generally, to mitigate over-diagnoses, there need to be low false-detections to decrease the number of lesions sampled or biopsy cores taken [6].…”