Novel approaches to improve prostate cancer diagnosis and management in early‐stage disease
Bibliographic record
Abstract
The reported incidence of prostate cancer has risen since the implementation of screening. It is felt that the introduction of widespread prostate-specific antigen testing is responsible for most patients with prostate cancer now being diagnosed with asymptomatic, clinically localised disease. Diagnosis at this stage is associated with significantly improved treatment outcomes and longer life expectancy. Although there is evidence that screening has reduced prostate cancer mortality, there is a risk of over-diagnosis and over-treatment of early state prostate cancers, including clinically insignificant and indolent cancers. Active surveillance and focal therapy have been advocated as potential management options for some patients. However, these approaches face several challenges. Biopsy sampling errors together with less than optimal imaging of tumours can lead to difficulties in selecting suitable low-risk patients for these options. To overcome these challenges, novel approaches to the staging and monitoring of patients with early prostate cancer are being developed. These include new imaging techniques, such as multi-parametric magnetic resonance imaging, and the development of new biomarkers and biopsy-based methods. These techniques aim to assess the potential of a specific tumour to be aggressive, and to improve patient outcomes. The aim of the present paper is to summarise presentations and debates at the third annual Interactive Genitourinary Cancer Conference concerning the use of population-based screening methods and the roles of active surveillance and focal therapy as prostate cancer treatments. The application of novel imaging biopsy-based methods and biomarkers in early-stage prostate cancer will also be explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".