Impact of Teratoma on the Cumulative Incidence of Disease-Related Death in Patients With Advanced Germ Cell Tumors
Bibliographic record
Abstract
PURPOSE In men with metastatic germ cell tumors (GCTs), risk-directed treatment is determined, in part, by a distinction between seminoma and nonseminomatous GCT (NSGCT). The importance of NSGCT cell type is uncertain. We evaluated the long-term impact of teratoma on survival in patients with NSGCT. METHODS Prechemotherapy, primary tumors from patients who received platinum-based chemotherapy were studied, and the histology was confirmed by a genitourinary pathologist. The cumulative incidence of disease-related death (CIDD) was the primary end point, and a competing-risk analysis was performed. RESULTS Tumors were available from 232 patients, including 193 with NSGCT. An element of teratoma was present in 82 NSGCT primary tumors (42%). With a median follow-up of 17 years (range, 0.3 to 35 years), 58 patients with NSGCT died, 47 as a result of GCT and 11 as a result of other causes. Most GCT deaths occurred within the first 5 years and were associated with pretreatment risk status ( P < .001). Death as a result of other causes rose steadily after 15 years and was not associated with risk status ( P = .66). A higher CIDD was observed in patients who had NSGCT with teratoma than those with NSGCT without teratoma and seminoma (5-year CIDD rate, 27.4%, 17.4%, and 10.3%, respectively; P = .03). A higher CIDD was observed in patients who had NSGCT with mature teratoma compared with those with either NSGCT with immature teratoma or NSGCT without teratoma (5-year CIDD rate, 38.1%, 19.9%, and 17.4%, respectively; P = .01). CONCLUSION The presence of teratoma, particularly mature teratoma, in an NSGCT primary tumor is associated with a higher CIDD, consistent with the hypothesis that differentiation is associated with adverse outcomes. Death as a result of non-GCT causes is not associated with risk status and must be separated from GCT death when evaluating long-term survival.
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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 | 0.001 | 0.004 |
| 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".