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28th September 2021
Prostate cancer is the second most common cancer in men, with 1.3 million new cases recorded in 2018. Confirmation of a prostate cancer diagnosis can only be achieved via biopsy and subsequent examination of digitalised slides of the biopsy. Now, the first artificial intelligence (AI) software for in vitro diagnostic detection cancer in prostate biopsies has been approved by the FDA in the US. The software is designed to identify an area of interest on the prostate biopsy image with the highest likelihood of harbouring cancer. This alerts the pathologist if the area of concern has not been noticed on their initial review and thus can assist them in their overall assessment of the biopsy slides.
The AI system approved is Paige Prostate and it is anticipated to increase the number of identified prostate biopsy samples with cancerous tissue and ultimately save lives. The FDA approval was based on a study of Paige Prostate undertaken with three pathologists. In the study, which was conducted in two phases, each pathologist was required to assess 232 anonymised whole slide images and asked to dichotomise these as either cancerous or benign, with only 93 slides (40%) that were in fact cancerous. In the first phase, the pathologists assessed the scans alone, whereas in the second phase, 4-weeks later, the same scans were reviewed but this time using the AI software, Paige Prostate.
In the study, the Paige Prostate software alone, had a sensitivity for detecting cancer of 96% and a specificity of 98%. Without the use of Paige Prostate, the pathologists averaged a sensitivity of 74% but with the addition of the AI software, their average sensitivity increased significantly to 90% (p < 0.001). Addition of Paige Prostate mainly improved pathologists’ detection of grade 1 to 3 cancers. However, despite a greater sensitivity from the use of Paige Prostate, there was no significant difference in specificity (p = 0.327) since this was already high at an average of 97% without Paige Prostate.
Source. FDA Press release September 2021
15th June 2021
Advancements in machine-learning (ML) algorithms in medicine have demonstrated that such systems can be as accurate as humans. However, few systems have been used in routine clinical practice and often ML systems tested in parallel with physicians and actions suggested by the system not acted upon in practice. To fully utilise ML systems in routine clinical care requires a shift from its current adjunctive support role, to being considered as the primary option. In trying to assess the real-world value of an ML algorithm, a team from the Princess Margaret Cancer Centre, Ontario, Canada, decided to explore the value of ML-generated curative-intent radiation therapy (RT) treatment planning for patients with prostate cancer. The team’s overall aim was to evaluate the integration of the ML system as a standard of care and undertook a two-stage study comprising an initial feasibility to clinical deployment. For the initial validation phase, the team included data from 50 patients to assess the ML performance retrospectively. The researchers delivered ML-generated RT plans and asked reviewers to assess these plans (in a blinded fashion) with the actual plans used for the patient. In the subsequent deployment phase, again with 50 patients, both physician generated and ML generated were prospectively compared, again with the treating physician blinded to the source of the plan.
The ML system proved to be much faster at generating plans than the equivalent human-driven process (median 47 vs 118 hours, p < 0.01). Overall, ML-generated plans were deemed to be clinically acceptable for treatment in 89% of cases across both the validation and deployment phase (92% duration the validation phase and 86% during the deployment phase). In only 10 cases, the ML-generated method was deemed not applicable because the plans required consultation with the treating physician, thus unblinding the review process. In addition, 72% of ML-generated RT plans were selected over human-generated RT plans in a head-to-head comparison. However, when compared to the simulation and the deployment phase, the proportion of ML-generated plans used by the treating physician actually reduced from 83% to 61% (p = 0.02).
The authors were unable to fully account for these differences and suggested that either retrospective or simulated studies cannot fully recapitulate the factors influencing clinical-decision-making when patient care is at stake and concluded that further prospective deployment studies are required to validate the impact of ML in real-world clinical settings to fully quantify the value of such methods.
McIntosh C et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat Med 2021