Urine biomarkers in cancer detection: A systematic review of preanalytical parameters and applied methods
Bibliographic record
Abstract
The aim of this review was to explore the status of urine sampling as a liquid biopsy for noninvasive cancer research by reviewing used preanalytical parameters and protocols. We searched two main health sciences databases, PubMed and Web of Science. From all eligible publications (2010-2022), information was extracted regarding: (a) study population characteristics, (b) cancer type, (c) urine preanalytics, (d) analyte class, (e) isolation method, (f) detection method, (g) comparator used, (h) biomarker type, (i) conclusion and (j) sensitivity and specificity. The search query identified 7835 records, of which 924 unique publications remained after screening the title, abstract and full text. Our analysis demonstrated that many publications did not report information about the preanalytical parameters of their urine samples, even though several other studies have shown the importance of standardization of sample handling. Interestingly, it was noted that urine is used for many cancer types and not just cancers originating from the urogenital tract. Many different types of relevant analytes have been shown to be found in urine. Additionally, future considerations and recommendations are discussed: (a) the heterogeneous nature of urine, (b) the need for standardized practice protocols and (c) the road toward the clinic. Urine is an emerging liquid biopsy with broad applicability in different analytes and several cancer types. However, standard practice protocols for sample handling and processing would help to elaborate the clinical utility of urine in cancer research, detection and disease monitoring.
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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 | 0.023 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".