The Paige Prostate Suite: Assistive Artificial Intelligence for Prostate Cancer Diagnosis
Bibliographic record
Abstract
What Is the Paige Prostate Suite? The Paige Prostate Suite is a set of artificial intelligence (AI) applications that works alongside pathologists reviewing prostate biopsy samples. The suite is not available in Canada as of this writing (June 2024), but international counterparts have authorized it for clinical use. The system requires pathology slides to be digitized for the suite to be able to highlight areas of suspicion for pathologist review. Pathologists can use Paige Prostate Detect as a “second set of eyes” on biopsy slides scanned into the digital system. What Issue Does the Paige Prostate Suite Intend to Address? In Canada, prostate cancer accounted for 20% of new cancers in 2022. It is the most common cancer found in people who have a prostate. Unlike many other cancers, the disease progresses slowly and early diagnosis results in a 5-year survival rate close to 100%. However, health systems are faced with more cancer cases without an increased capacity in pathology. What Is the Potential Impact? Paige Prostate aims to improve pathologists’ ability to detect prostate cancer in less time by allowing pathologists to focus on positive cases. However, the time-saving benefits of the suite require further validation that reflects real world settings and applications. The Paige Prostate Suite has the potential to optimize pathology workflow and prevent delays in diagnosis. Pathologists can use the suite to supplement their review instead of sending biopsy slides to the lab for additional staining or to experts for a second consultation. It does, however, require the added the step of digitizing slides to the workflow. The Paige Prostate Suite aims to improve consistency by helping pathologists grade tumours. What Else Do We Need to Know? Canada is in the early stages of implementing technology to produce digital images of biopsies. To adapt to the uptake of AI, pathology departments will need to adopt new digital workflows and processes. They could leverage this infrastructure for telepathology to improve access in rural and remote areas. Clinicians expect the Paige Prostate Suite to cost more than current practice. Costs will vary depending on the case load, system readiness for AI, and use case for the suite. Cost-effectiveness of the suite requires in-depth investigation that considers the cost-benefit from productivity changes. Upcoming prospective studies will assess the benefits of the Paige Prostate Suite from a clinical utility and cost impact perspective. It is also important to further understand the diagnostic performance and impact of Paige Prostate in the context of race, ethnicity, and other equity considerations, as those in equity-deserving groups may be underrepresented in algorithm development and trials, or have different levels of prostate cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".