Serum miRNA to predict post-chemotherapy viable disease in testicular non-seminomatous germ cell tumor patients.
Bibliographic record
Abstract
546 Background: Retroperitoneal lymph node dissection (RPLND) is recommended for residual masses > 1cm post-chemotherapy (pc) for nonseminomatous germ cell tumors (NSGCT). There is no reliable predictor for pcRPLND histology and up to 50% will harbour necrosis/fibrosis only, thus rendering a potentially morbid surgery to be of limited therapeutic value. Objective: To evaluate the ability of defined serum microRNA (miRNA) using the ampTSmiR test to predict residual viable NSGCT after chemotherapy. Methods: Serum miRNA levels (miR-371a-3p, miR-373-3p and miR-367-3p) were measured in 82 patients (cohort A = 39, cohort B = 43) treated with orchiectomy, chemotherapy and pcRPLND to predict viable GCT post-chemotherapy. Outcomes, measurements and statistical analysis: miRNA levels were compared to clinical characteristics, serum tumor markers and correlated with presence of viable GCT (vs. teratoma; vs. necrosis/fibrosis). miRNA-discriminative capacity was determined by receiver operating characteristic (ROC) analysis. Results: Serum miRNA were associated with stage at the time of chemotherapy and declined significantly post-chemotherapy. Patients with fibrosis/necrosis and teratoma had a significant decline in all three miRNA levels after chemotherapy, while those with viable disease had very little change. Patients with necrosis/fibrosis demonstrated similar miRNA levels as patients with residual teratoma. miR-371a-3p demonstrated the highest discriminative capacity [area under the curve (AUC) 0.874, CI 95% 0.774 - 0.974 p < 0.0001] for viable disease post chemotherapy. If considering a more relaxed cut-point of 3cm before consideration of pcRPLND, miR-371a-3p correctly stratified all patients with residual retroperitoneal lesions ≤ 3 cm ( p= 0.02; 100% sensitivity). Conclusions: Our study is the first to explore a miRNA-based serum test to determine histology in post-chemotherapy residual masses and we demonstrated the value of miR-371a-3p to predict presence of viable GCT. Prospective studies are required to confirm its clinical utility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".