The Past and Future of Biomarkers in Testicular Germ Cell Tumors
Bibliographic record
Abstract
Testicular germ cell tumor (GCT) is the most common malignancy in 18- to 40-year-old men. Unlike most other cancers, GCT is frequently curable even when metastatic. These tumors can be classified histologically into seminoma and non-seminoma, which determines treatment. Therefore, successful treatment requires accurate diagnosis, classification, and monitoring. Serum tumor markers, including lactate dehydrogenase, α-fetoprotein, and β-human chorionic gonadotropin, aid in the classification and staging of GCTs. These markers therefore play a critical role in the decision-making process when managing GCT patients. However, there exist many scenarios in which these markers fail to perform adequately. This is particularly true in the case of seminoma, where only 10% to 15% will have elevated serum tumor markers. Non-specific elevation of these markers is also a common occurrence, complicating the interpretation of borderline positive results, particularly in follow-up. To bridge this gap in performance, next generation biomarkers are being investigated. In this review, we consider the role of conventional serum tumor markers in GCT management and discuss recent advances in the next generation of biomarkers, with a focus on circulating microRNAs. We discuss the value that circulating microRNAs could bring as an addition to currently used markers, as well as potential weaknesses, in GCT management.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".