Prostate Cancer Treatment on the Basis of an Individual Risk Profile; Can we Reduce Overtreatment?
Bibliographic record
Abstract
Prostate cancer (PCa) is the most prevalent cancer in male population with an incidence rate of 93 per 100.000 men in Europe and is the sixth leading cause of cancer related deaths in men. In the last two decades the incidence of PCa has increased, which is related to widespread prostate-specific antigen (PSA) based screening and increased life expectancy. Mortality rates of prostate cancer have been reduced due to improvement in treatment and/or the widespread screening activities. Major down sides of screening are the potential risks of overdiagnosis and subsequent overtreatment. Approximately 50% of PCa cases detected through screening are potentially overdiagnosed and hence do not require active treatment. However, in clinical practice men with a potentially non-life-threatening cancer (indolent cancer) are often treated actively resulting in unnecessary suffering from serious side effects coinciding with active treatment. The way out of this dilemma is two-fold. First, the actual diagnosis could be delayed or even avoided and second, radical treatment could be delayed or avoided for patients with low-risk PCa. To better predict the presence of a (potentially indolent) prostate cancer nomograms have been developed. These multivariate prediction tools can be of aid in avoiding unnecessary biopsies reducing overdiagnosis, or identifying potentially indolent prostate cancer after diagnosis and hence adapt the treatment strategy. In this expert opinion we discuss the available tools and their performance in reducing the unwanted side effects of prostate cancer screening. In addition, we provide an overview of strategies concerning optimisation and individualisation of treatment, to reduce overtreatment of prostate cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".