Review of novel tissue-based biomarkers for prostate cancer: towards personalised and targeted medicine
Bibliographic record
Abstract
Abstract Background: Prostate cancer is the most commonly diagnosed cancer in men and responsible for about 10% of all cancer mortality in both Canadian and American men. Currently, serum PSA level is the most commonly used test for the detection of prostate cancer, though the levels can also be elevated in benign conditions, has limited specificity and has a high rate of overdiagnosis and treatment of indolent disease. Consequently, in recent years, several investigations have been conducted to identify novel cancer biomarkers capable of both effective screening and diagnosis, as well as assisting to shift the diagnostic and treatment paradigm of prostate cancer towards more patient-specific and targeted medicine. The goal of this narrative review paper is to describe eleven novel and promising tissue-based biomarkers for prostate cancer capable to account for individual patient variabilities and have the potential for risk assessment, early detection and diagnosis, identification of patients who will benefit from a particular treatment and monitoring patient response to treatment. Materials and methods: We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on tissue-based biomarkers for screening and early diagnosis, treatment and monitoring of prostate cancer. Conclusions: Emerging prostate cancer biomarkers have the potential to guide clinical decision-making since they have the potential to detect the disease early, measure the risk of developing the disease and the risk of progression, provide accurate information of patient response to a specific treatment and are capable of informing clinicians about the likely outcome of a cancer diagnosis independent of the treatment received. Therefore, the future holds promise for personalised and targeted medicine from prevention to diagnosis and treatment that considers the individual patient’s variabilities in the management 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".